of engagement, and we won't see private cloud in its fullest free software expression. But the mission we should not forget about, the other half of this mission is an economic one. Every cloud competes with the public cloud and it should, it's right that it should. If you're building an open stack, you are at some level competing with Amazon, SoftLayer, with Google and with Microsoft, and it's healthy to face that competition head on and we should all be mindful of the long term operating challenges, operating costs of your private cloud infrastructure. And that has been our relentless focus throughout. We just saw the latest open stack user survey, who's seen it, who's super interesting. I really recommend it as a read, full of information. From the perspective of people building clouds, there are two numbers that really jumped out for me. The first is this shift. Juju, the number of clouds built with Juju, grew by 50% in six months and is now ahead of clouds built with Chef, with Salt, with Packstack, the Rel installer. And most importantly, I think that shift represents a growing awareness of the long term operating costs of a large scale out infrastructure because uniquely, Juju allows us to crowdsource and collaborate on operations, not just configuration management. So that's a key theme for us. The other big piece of information there is that Ubuntu continues to be the platform of choice for open stack. I think what validates open stack is that every platform is here. You can build open stacks with Hyper-V. You can build them with VMware, ESX. You can build them with REL, with CentOS, with SUSE, with Debian, with Ubuntu. And more people build on Ubuntu than all of the other platforms combined. And I think that's really testament to the extended community and to the confidence that people have in the ability for themselves to shape their own open stack in an Ubuntu environment. Now people think they're here to talk about open stack, but I don't think it's cloud that will be the defining term of our era. I think it's something new, something different, something that people haven't really given a name to. And I want to do that today. And that name is big software. You've all heard of big data. And I want to remind people, just remember to the dawn of big data, when data professionals laughed. Right? In the late 90s, early 2000s, being a data professional was a serious career. People have been doing it for 15, 20 years. We were in a world where we had very sophisticated ways to model data. And as the scale of data grew, we increased the sophistication of our tools to the point where some people started to realize that things were breaking down. And so we needed to move through a phase change to an unstructured. We needed to embrace, in addition, an unstructured view of data. And again, the professionals laughed. The professionals said, look, these kids just don't know how to do it right. These kids just don't have access to big enough machines. These kids haven't been trained properly, right? But really what was going on was a phase change. Phase changes are really powerful things. If I say to you that your mission was to get 100 tons of H2O to Houston this afternoon, you'd really want to know, were you dealing with ice? Are you dealing with water? Are you dealing with steam? It is a two degree Celsius difference to go from any one of those phases to the next one up. But it is an entirely different user experience, an entirely different set of safety regulations, an entirely different set of tools to deal with a new phase of H2O. In the same way we needed an entirely different set of tools and thinking to deal with big data. And my proposition to you today is that we need an entirely different way of thinking about big software. So what's happened? Applications used to be simple, two, three pieces spread across two, three machines. Today, of course, any interesting software is made up of many, many applications, many components, spread across many machines. This is the scale out era, but it's really the big software era. And architecture today is really a discussion about how best to spread many pieces of software across many pieces of physical infrastructure. The architectural conversation in OpenStack is really about how to spread the 1520 to 30 pieces of software that make up a modern OpenStack deployment across tens, hundreds, or thousands of diverse physical servers. Now that's a phase change. And just like the proverbial frog in the pot, the temperature rises and people try to attack this problem with their established tools. And it's only later that everyone steps back and realizes that was a boiling point, that was a change. And that really is the thing that motivates me, that's what motivates our team. Now we've talked for a long time about JuJu as a way of modeling applications specifically to get a handle on big software and this phase change. And we just launched Ubuntu 16.04, which includes JuJu 2.0 and a new tool, a very lightweight wrapper on top of JuJu. Which I just want to give you a quick taste of. This is something called Conjure Up. And this makes it extremely easy for any member of the community to provide a walk-through installer of any big software. Any big software at all, better. So I can conjure up OpenStack, but I can conjure up any big software. And you all know that JuJu is essentially charms modeling individual applications and then a model of those applications spread across different servers. Here, this is a bundle specifically focused on OpenStack. There are a couple of different ways I can attack OpenStack in this conjure up deployment.   And I'm going to use container machines. I'm going to simulate many machines, but I'm actually using LexD containers for each of those machines. And the reason that's interesting is because it allows me as a developer to experience a real OpenStack. For example, a highly available OpenStack. Now who's used DevStack? Right, DevStack is a script that installs all the components of OpenStack in one process space, right? What we're doing here is essentially creating 20 or 30 process spaces, 20 or 30 container machines, and then installing all of the components of OpenStack as if they were installed in a cluster across many racks. And that allows us on a single laptop to have a real experience. And I can go through here and change some of the configuration settings, or I can just go say, build that out, and it will essentially spin up that OpenStack on a single machine. So I think that's really going to change the experience that developers have with OpenStack. We see a lot of bugs creeping into the OpenStack code base because of DevStack, because people essentially experience a naive or simplistic OpenStack in the development cycle.  So I think that's going to be a great thing for OpenStack development. Let's talk a little bit about production and scale. Now, the real test of success for an OpenStack cloud, for a serious business that's going to count on that OpenStack cloud, is not what it costs to stand it up. It's what it costs to operate it over time. All the backups, all the fixes, all the updates, upgrades. That's really where the long-term economics will be tested. There are other factors, but let's just start with operations. A key focus for us has been to automate and to crowdsource operational expertise, and that's really reflected in another layer on top of JuJu called the autopilot. And I want to show you where that's at today. So here we have three racks of servers. Inside each of these is 10 little Intel nooks. And so we can use those as a simulation of a rack, effectively. This is showing a picture of number 36, which I think is the third one over on the right, or my right, your left. And you can see that it's got an OpenStack, it's got something deployed, there's a bunch of nodes active. But it also has, the autopilot has some spare capacity that I'm going to use to go build a cloud. Now this is, the intent here is to essentially make all of the decisions in terms of the allocation of resources, but also the operational decisions to automate those. So we're using the crowdsourced charms, which capture a bunch of information. We're also using the fact that the underlying infrastructure is modeled. So MAS, this will tell me what my network ranges are. I'm going to choose this network range, I've got more IP addresses, I know, spare on that one. And it'll offer me a series of choices, a new choice in this latest iteration is integration with Nagios. This is our top customer request, very interesting. Super widely used open source monitoring software Nagios, really easy now to integrate an OpenStack deployed, an autopilot deployed OpenStack with your Nagios. Now the fundamental decision I need to make is at the level of resource allocation. How many servers do I want to give this cloud? There are, I'm just going to choose all of the spare servers in that cluster. And this is a new option that we've presented, which is the ability to allocate specific roles to those machines. You'll see that because of the underlying modeling from the bottom up, the autopilot will preclude me from saying put network gateways, for example, on something that doesn't happen to have enough network interfaces, or it'll preclude me from putting storage on machines which don't have enough extra disks to provide useful storage services. So this is kind of a manual approach, allowing people to create their own architectures, experiment with their own architectures. But we retain, I'm just going to go back, we retain what I think is the most important thing, which is fully automated deployment. And so here you can see that if I press go, I'm going to build a cloud across three availability zones that's all abstracted from the bottom up. I haven't had to enter a single IP address. That wasn't just browser caching that's modeled from the bottom up. We treat IP addresses as an actual asset and a resource. And I can crack on or install that. You will see lights coming on as that cloud gets built.  And the important thing I think about our reference architecture is that we've considered it from an economic perspective first. And the key thing here is the maximum utilization of the resources that you have fed to that cloud. The way we achieve that is with a hyper-converged architecture which is a lot of syllables. What it really means is that we use every disk, we use every core, and we dynamically spread load including administrative overhead load. And we use containers as a very efficient way to take a slice of a computer and guarantee a certain amount of performance to that slice. So for example, we can have a rabbit messaging subsystem or a part of a slice of a MySQL database that's feeding the entire cloud on any given unit. And the use of containers gives us live migration. It gives us a lot of profound administrative operational benefits. And of course it leaves the maximum amount of capacity available to our users. So I'm just gonna kill that and that. So containers, it's difficult to have a conversation these days about technology without talking about containers. There is an enormous amount of really important work being done with immutable containers like Docker containers as a new kind of application primitive. But our focus in that, alongside the work of Docker, and Ubuntu is the platform of choice for Docker, 70% of the Docker images are Ubuntu based. But the really interesting thing for us is to enable people to treat containers as a hypervisor. And so the pure container hypervisor, LexD, is now fully integrated with NOVA in OpenStack. And so here I have, I think it's, again it's on that last rack over there. I have OpenStack deployed and you can see it looks like any other OpenStack. I may have to log in again. But the key thing here is that the hypervisor in this OpenStack is LexD. So it's pure containers and not a single VM here. If I create an instance, what I'm gonna be doing is creating a container. I just wanna show you how fast that is. The critical thing about containers as a hypervisor is density, latency, and performance. With containers, we have no physical infrastructure virtualization. So we really have the bare metal performance. And so I should spin up five of those. And I need to allocate, let's just use a XenialImage, mediums. In the latest version of LexD, we can in fact do very detailed quality of service. So we can guarantee performance to containers. We could also stop those containers from using excess capacity on the machine. So we really are starting to get to the point where containers are like a hypervisor. If you've done this before on a couple of Intel Nooks with KVM, you'll know that it can take a little while to spin up five machines. But in about 15 seconds, you should have five instances running on this OpenStack cloud. This is really important for very high density applications. It's also important in cases where latency is crucial. For example, in telco NFV or real-time transcoding and streaming, any dropped packets, dropped frames, dropped microseconds, milliseconds are gonna be noticed by the end user. So using a container, we can actually very precisely put a workload on the underlying host CPU, pin it to a particular core, pin it to a particular thread, use numeric primitives to get very, very accurate and fine granularity on the allocation of capacity to workloads. So we see this being interesting for high-density use cases, dev and test, idle workloads, but also very precise supercomputing or other workloads. So one last thing on the question of infrastructure. Today we see a very definitive move towards software-defined infrastructure. Software-defined storage is big software, right? It's scale-out software, multiple components across many machines. We have done that consistently with Swift and Cep and Nexcentre and various other storage components modeled in Juju Charms. Today I'm delighted to announce the newest member of that program, that's EMC, with Scale.io, now fully charmed and integrated into Ubuntu OpenStack. And the driver for this work really has been telco adoption of Ubuntu OpenStack and the desire to operate with very high-performance software-defined storage at scale. Infrastructure's exciting. This is essentially an infrastructure conference, but at the end of the day, nobody comes here to make disk spin and lights blink, right? This is all in the name of applications, business intelligence, answers. And those answers come from big software. The future of big data is machine learning, it's big software. The future of PAS, it's container orchestration, it's big software. So in Juju, we're modeling big software on top of the cloud to operate in a hybrid cloud environment. With Juju, we can place applications across public and private clouds, traditional vert infrastructure, and as you've seen containers and bare metal as well. This is a snapshot of some of the vendors who are producing charms to model their big software applications across all of those cloud and bare metal environments. And in particular, I want to call out the rapid acceleration in telco usage of Juju for this purpose. These are a selection of the telcos who are using Juju, both for what they call the VIM, we call it a cloud, the virtualized infrastructure management layer, but also for the VNF, the rest of us call them apps, right? The virtual network function layer. And I want to invite up onto the stage Robert Schregler, who is lead architect and director of operations for Deutsche Telekom to tell us a little bit about Deutsche Telekom's experience with OpenStack. Robert, welcome. Thank you. Thank you very much. Now working, yeah. At Deutsche Telekom, we approach now a completely new game as a software-defined operator, as we call it, where Deutsche Telekom wants to deploy the cloud infrastructure underneath its new applications for all over Europe. So therefore, the nice picture here in the background. And it consists of three main things for us. And these puzzle pieces are coming now together. You might know that we are dealing with it for a longer time. And now the IP network strategy with Terastream, where we have an IP network, IPv6-based network approach which explains the connectivity underneath. BNG is a German subversion of it. And this then combined with the SDN approach and real-time network service management, which we are currently building up with NetConfiang to provision, modify, and also handle the change management of all the network elements automatically. And last but not least, and this is where I'm mostly working in the infrastructure cloud. So this is an existence in two flavors. We experimented in the last years with a so-called front-end data center, which is data plane-oriented. So high throughput directly connected to the R2 router and completely automatically installed already with Juju on IPv6. But very simplified model, very simple installation in fact. Now this year we started with the bigger installation and the tight integration in the IP network on the so-called back-end data center networks with complex underlay network structure with SDN solution in place. We use contrail for that one. And OpenStack completely automatically installed with Juju. So this is a hell lot of planning to do it right, to do the scaling, the hardware scaling in the right manner, the underlying infrastructure configuration correctly, but then it is amazing. So my journey is when I started three years ago with OpenStack, it took us three months to install it with good planning. And now when we have a good planning done, we install in three days. So this is really, really amazing. And yes, we have strange errors, maybe one nice anecdote. We had a bonded network connectors plugged just 90 degrees wrong. So small packages ran, big packages not, so be aware if you have these kinds of problems, check cables. And it takes long. And this is really amazing for us because at the end we are now onboarding applications on top of it and we are planning to put TV load on it. We are planning to put simple applications like email on it. This is all nice and good. And the biggest problem, I think as you said, Mark is managing the software because the complexity now is in the software. And I disagree with the B-model approach. I think the slicing of the software, if you manage your software in a way so that you have your application lifecycle management approach or software delivery lifecycle approach in a way so that you can release and deploy smallest chunks in a tested environment, then you're good to go. Thanks. Robert, thank you very much. Finally, mission accomplished, the Buntu-powered TVs. Just not quite the way we thought it was gonna happen. I'm super appreciative of one key thing, which is that Deutsche Telekom has insisted that all of this work happen as open source. And so the operational learnings, the operational insights, all of those little debugging tools, gotchas, glitches, all of that ironing out is happening as open source in the charms. And that's really important for other people who might relate to this complexity. For everybody to do this from scratch is some definition of insane. And so it is a delight for us to be able to participate in that project and share that work with like-minded carriers around the world. What really matters here is that portfolio of software on top of the cloud, that portfolio of VNFs. And here is a selection of VNF vendors delivering charms, not just for Deutsche Telekom, but for many of these telcos who are using charms to model those applications at scale. One of the more exciting projects for us is interestingly exploring the 5G network as a series of software-defined radio experiences. And I actually have that running on this rack. So here we have OpenStack. And that OpenStack is running a cloud EPC from Expeto. That's a VNF essentially, a big software VNF. And those are powering this GSM base station. So the Texas Rangers are banging on the door because we've got an unlicensed carrier in the room. If you turn on your phone and look for carriers, you'll find one called Test PLMN. And this phone is registered, I have the magic SIM card, you don't, but you can see the carrier. And to prove that it works, I want to try and make a Google Hangouts call to one of my colleagues. Very relieved is the answer to that. So I just want to clarify for you what you're seeing. You're seeing an OpenStack deployed with the autopilot on this box. You're seeing a full EPC, Evolved Packet Core, which is a piece of software that's not just used by telcos, but you might have seen Google's announcement that they're using an EPC to deliver Wi-Fi on trains in some cities. So that's a software-defined EPC experience driving this base station. For those of you who want to dig deeper, this is the next generation. It's a software-defined radio, again driven by a set of charms, providing 5G. This one is running over Amir LTE. All of that running live on stage on OpenStack. So another really interesting project in the telco space is trailblazing work by a global tier one telco, which is focused very much on performance, price performance at scale for VNFs. And they've chosen to explore that work on power, on an architecture that is legendary for its scale and performance. And I'm delighted to welcome up on stage Mr. Doug Balog, the general manager of IBM Power Group, and he's going to tell us a little bit about power in the context of NFV and other amazing things that are being done on power today. Doug, welcome. Thank you, thank you. So you're probably wondering what's a hardware guy doing up here to talk about big software to use Mark's terms. So Mark talked about phase changes. And I'd say in the hardware space, just like probably many of your companies, we're going through our own transformation. Why? Because if you think about functions like NFV or IoT aggregation for analytics, those have a thirst for performance, a thirst for analytics, a thirst for ingestive information, and then trying to get insight out of that information. About a little over two years ago, we started working closely with not only Ubuntu, but companies like Melanox and Vidya and Google, recognizing the world that we all knew, certainly called Moore's Law, doubling of transistor density every two years, therefore the doubling of performance. Bad news for you is coming to an end. And so while on one side, we have this incredible set of applications, big software to use Mark's term, that desire, performance, compute, ingest, we have a fundamental law of physics problem in front of us. And we needed to crack that. We needed to solve that. So we created a foundation, open source foundation called the Open Power Foundation. And its purpose was to really look at the system design, given these big software applications we see, and fundamentally find ways to continue to deliver double the performance every two years, but more than just a processor alone. It was gonna take things like accelerators. As I mentioned, and Vidya was part of the original formation of the Open Power Foundation, but since then we've seen not only GPUs, but FPGA members all joined as well too. So with the power architecture, we've open licensed it. Anybody can license power now to create derivatives, to create manufacturing, and create new systems designs. So whereby I like to say about two years ago, we had five companies, five guys in a bar on napkin. We now have over 200 companies around the world innovating around the power architecture, focused on this set of problems we see around the big software and analytics. Now it doesn't just stop at NFV, stops, it continues on to areas that we think are pretty exciting, that at least in IBM we call the cognitive era. Hopefully some of you have heard of Watson? Watson everybody? Yep. Now it's evolved from the Jeopardy playing machine of four years ago, to really the way in which we're bringing data, yet previously unseen by computers on structured information into the world of healthcare, into IoT, into commerce, into finance, into retailing around the world, so that there is an aid to solving some of the big problems of the world. This aspect of cognitive, whether it be, you think about it, advanced analytics, so artificial intelligence, machine learning, and deep learning, there's a whole category of rich analytics that occur, that again have a thirst, just a fundamental thirst for compute, for IO bandwidth, for memory bandwidth, for big memory to go with big software. That's what the Open Power Foundation with these members are working on every day around the world, and we're pretty excited about the capability this provides us, and Ubuntu has been a leader with us in this space since the day it was born over two years ago. In fact, Canonical's own John Zenos is now the, well I like to call him the grand pooba of the Open Power Foundation, or I guess it's president or chairman, or some big title, so we're thrilled that we have that kind of partnership. But that's what's been going on in the hardware world. So again, transforming as it needs to transform, whole open licensing models around power that might surprise you, but really fundamentally focused on solving some of the rich analytics problems that we see in the world today. Because it's a world of big software, as you said, Mark. It is so exciting for us to be working together. I couldn't help but notice that the company that open sourced machine learning first, Google, is blazing that trail on Open Power. And so it is very exciting to be able to bring full choice of architectures and this amazing portfolio of capabilities to some of these new business problems. In fact, Google with Rackspace and IBM and others just announced a couple of weeks ago their plans to release Open Power meets Open Compute meets Open Stack based on Power 9 to the industry here as an architecture for the data center. So we're pretty excited about that joint announcement took place at the Open Power Summit in San Jose. Doug, thank you very much. Thank you, Mark, pleasure to be here. So riffing on that theme of machine learning, deep learning, neural networks, and the beginnings of AI, team at IBM and a team at Canonical over the last month have done a little deep dive into open source machine learning. And I'd like to show you the fruits of that labor. This is work and I'd like to thank a company called SkyMind, another company called DataFellas who worked with us on this. This is deep learning 4J running live using a model that was built on Ubuntu on Power. The way deep learning works is that you push large amounts of data through a learning algorithm to produce a model. And this model actually is very interesting. It is looking at Open Stack logs and doing predictive failure analysis. So I want you to think about a large scale Open Stack deployment generating megabits per second of logging information at every level of the stack. It is far too much information to introspect manually. But what if we could bring deep learning to that challenge? So here you have a running Open Stack and essentially a real time signaling service telling us based on machine learning a predictive, giving us a predictive failure indication. So it's essentially giving us an early warning of storms brewing in the logs of a large scale Open Stack cluster. That's a model built on Power, on Ubuntu with Open Stack with the help of SkyMind and DataFellas. This is another proof point of the same story. Again, open source machine learning. This is looking at network intrusion data. So again, real time analysis of streaming logs with the aim of predicting, giving early warning of attacks in the network. Really profoundly interesting stuff. This I didn't talk about earlier, but when I talked about scale out software defined storage this is an interesting example. This is a Cep dashboard developed by Canonical on behalf of one of its customers giving real time performance analytics on a large scale Cep dashboard. Again, that's a straightforward juju deploy of the dashboard and wiring it in with a relation command to your juju deployed Cep cluster. So, and this is of course the juju topology of the deep learning system that did that data analytics essentially that built that neural network model of both the Open Stack log analysis and the network intrusion analysis. All right. So I just wanna recap. You've seen an interface that allows anybody to build Open Stack trivially given four nodes, given 40 nodes, given 4,000 nodes. You've seen an Open Stack running that uses pure container hypervisor for the world's fastest Open Stack lowest latency and bare metal performance. You've seen VNFs deployed with juju running live on Open Stack providing LTE services. And you've seen a machine learning deployed with juju on Open Stack on power. The point is this, this conference is full of complexity. But the reality is we need to step back and think deeply about the core primitives that are needed to attack the problems of the new world. We will not survive this next decade as a private cloud forum and community unless we think really profoundly about the core primitives. I believe with LexD, with Maz, with juju, we have three really important primitives to wrap around your Open Stack experience. Everything we talked about here is on the booth. It's on the IBM booth. It's on the Ubuntu booth downstairs. Marketplace opens at six o'clock. I believe there's a tradition of a beer crawl. In Austin, that should be spectacular. Welcome to Austin. Thank you very much for coming. Stick around. And we may have time for questions. Can we get a signal on that from the back? Do we have time for questions? If you have a question, please come to the mics. Right, well. If that was so completely definitively final, thank you very much for coming. There are a series of great sessions in here. Lots to see on the agenda. Have a great week.


Good morning, good afternoon, good evening ladies and gentlemen. Welcome to our panel session on App for Enterprise Business. I'm Catherine Lefebvre, I'm the ONAP TST chair, working as an EVP at AT&T, and today our speaker will be Amar Kappadia, co-founder of ARNA Networks, Javier Singh Seti, software engineer at ARNA Networks, and Bin Young-Jean, our ONAP architecture subcommittee vice chair, but also principal engineer at Ericsson. Let me tell you first why ONAP becomes a true enabler for innovation in support of future industry use case. Over the last four years, the ONAP committee has been collaborating with the industry through cross-open source committee engagement and cross-influences with standard design organizations. We have developed impactful features and functions, like 5G footprint, network slicing, and ORAN integration. We continuously add more cognitive and modular capabilities, secure and robust integration. We are also implementing ONAP best practice and global requirements in our source code to offer scalable, reliable, and secure production readiness deployment. All of this makes ONAP a true enabler for innovation. While ONAP has primarily been used by network service providers to support their network automation transformation and run virtualization journey, the ONAP community also recognized the value of ONAP in enterprise vertical markets. A new task force ONAP for enterprise business has been created accordingly early this year. So, Amar, can you please give us more insight about our ONAP for enterprise roadmap? Yes. Thank you, Catherine. As Catherine mentioned, this working group's goal is to show some concrete set of activities that can demonstrate value for enterprises. One of the first use cases we are working on is around private 5G. The market estimates for private 5G are extremely promising. You've probably heard the numbers, Nokia has estimated it'll be an $800 billion market by 2030, ABI research, STL have even predicted larger numbers. Along with edge computing, private 5G is expected to revolutionize industries such as industry 4.0, precision agriculture, hospitals, smart cities, V2X, construction, and more. So, as you see, we have a rich roadmap for the enterprise working group. Broadly speaking, it's in two aspects. One aspect is collaboration with the 5G super blueprint, which we will talk about in the next slide. And where this working group is the ONAP subject matter expert and all the ONAP related work architecture is being driven by this enterprise working group. The second is to integrate with a very novel network slicing approach from the University of Southern California called Sabres, which is highly secure as well. So now with that background, some high points on the roadmap. So what have we accomplished so far? We kicked off the task force in the first half. We did the architecture work to define how magma can be integrated with ONAP and you will see a lot more details about that in subsequent slides. And we have completed the integration of ONAP with Anukit. Anukit, as you will see in a subsequent slide, is the NFVI layer, its standardization of the NFVI layer, and we have completed the integration with the Kubernetes version of Anukit. And we have also integrated magma with Anukit. What are we working on this half in the second half? We are starting to implement the architecture work we did in the first half, meaning implementing the integration of the magma controller and access gateway with ONAP. And you will see a lot more details as we go along. We are now working on architecting some of the data collection KPIs and metrics, which we will implement subsequently. For this half, we are going to exercise the magma 5G core, which we will talk about more as well, with a UE and GNode B emulator. And finally, we are starting the design work to integrate with sabers. Next half, we are going to implement many of the things we are architecting in this half. So data collection and implementing the KPIs will be done next half, doing some control loop work, implementing initial sabers integration, and with core slicing with ONAP. And we will replace the GNode B and UE emulator with an actual RAM. So it will be a commercial RAM that we will use. Subsequently, later next year is going to be all around making what we have done more sophisticated. So the orchestration will be more sophisticated in terms of going to a fully cloud-native environment. We will be handling multiple access gateway CNFs, optimization for network slicing, more complicated LCM operations. And then later in 2023, we will be all around machine learning where we will look at AIML patterns. We will integrate with a function called NWDAF, Network Data Analytics function, which is all around machine learning. Can go to the next one. So this one talks about our collaboration with the 5G Super Blueprint. The 5G Super Blueprint is a place where we can integrate numerous Linux foundation projects that are all extremely relevant to 5G and edge computing. So before this initiative, there was no single place to integrate all these projects. And the 5G Super Blueprint is such a place. And as I mentioned, the ONAP Enterprise Working Group is a subject matter expert. And all of the ONAP design work and a lot of the implementation is coming, is driven by this Enterprise Working Group. So as you see, the scope of the blueprint is quite ambitious. It goes from the user edge to the service provider edge to the cloud slash core. It has edge computing components. It has 5G components. For today, we are not going to talk about the edge computing portions. We are only going to talk about the 5G and the core cloud components. So the notable parts are ONAP. So ONAP will be providing orchestration and lifecycle management. Historically, this role has been called the MANO, Management and Orchestration. More recently, there is a new term called SMO, Service Management and Orchestration. We are approaching this from a cloud native point of view. So we are using Kubernetes and using that to orchestrate both containers and virtual machines. 5G network slicing, we talked about, support for ORAN SC as the SMO. Initially, it will be a commercial ORAN. But over time, we will support ORAN SC, which stands for software community, ORAN software community. Patrol loop automation analytics and more. So that's the role of ONAP. We will be integrating with MAGMA. You will see more about MAGMA in a subsequent slide. It's a 5G core. It's an open source 5G core, LTE and 5G core, I should say, in the Linux foundation. ORAN SC, which is the implementation of the ORAN software components. And last but not the least, Anukit, which as I mentioned already is the NFVI layer. And for purposes of this blueprint, we are using the Kubernetes version of Anukit. There are two OpenStack and Kubernetes. So that's the whole scope. And with that, I'm going to hand it off to the next speaker, Prabhjod. Yeah. Thank you, Amar. So coming to ONAP compliance with Anukit, with respect to platform development process, ONAP is already leveraging conformance, functional validation, and performance frameworks from Anukit. As part of this initiative, we are essentially looking forward to use the infrastructure management aspects, leveraging Kubernetes reference infrastructure based on reference architecture and implementation projects in Anukit. Essentially, covering Kubernetes conformance aspects, most for most of the use cases, enabling faster, robust onboarding into production environments. And in a process, of course, saving on engineering cycles and reducing cost. So ONAP is already a fully containerized platform deployed using Kubernetes. And as part of this, we are rolling out ONAP itself and some of the other managed services like Magma Controller on a kubref environment, where kubref is a project under Anukit that delivers the Kubernetes-based reference implementation according to the reference architecture RA2 specification, allowing us to achieve conformance as per RA2 project of Anukit. So ONAP as well as Magma Controller deployment on kubref is already validated, making ONAP compliant with Anukit standardized Kubernetes infrastructure. Additionally, in future, we may also look forward to leveraging some of the conformance and performance framework from Anukit, which will allow us to enrich information with respect to interoperability aspects as well as the capacity or capability of the various solutions that will be delivered as part of this task force. Next slide, please. So another important piece of the solution with respect to 5G is Magma. So for those who are not familiar with Magma, it is an open source packet core originally open source by Facebook in 2019 and has recently become a part of Linux Foundation. As represented in the diagram on the right side, Magma essentially has three major components, access gateway, providing packet core functionality equivalent of AMF, SMF, UPF in 5G terminology. However, Magma itself is a converged core and that's why access gateway also extends support for LTE and carrier Wi-Fi. The second component is federated gateway, which is responsible for interfacing with the mobile network operator core. Other important component is orchestrator and NMS. Even though it is referred to as orchestrator, it mostly provides more of a controller or EMS functionality. So we are going to be referring to it as a Magma controller, which is mainly responsible for managing and configuring access gateways and federated gateways along with collecting various telemetry data from these connected devices. What's unique about Magma as opposed to other open source packet core projects is that it's already in production and additionally the project is backed by many contributors along with Facebook making Magma as the packet core of choice. And from architectural perspective, Magma core is hyper scalable, distributed where you can take access gateways, keep replicating it and scaling it out. It is highly available with many of its component being already containerized and cloud native except for access gateway containerization for which is currently in progress. It is vendor and transport agnostic and performs local breakout for internet traffic. It is also capable of interfacing with the MNO core with the federated gateway and along with providing capability to perform configuration and life cycle management remotely. All in all, it gives a converged packet core suitable for our use case. So with this, I'll hand over to the next speaker. Hi, thank you. Now we talk about the ONEP Magma integration scope. We defined two major integration scope here, initial scope and future scope. For the initial scope, ONEP is supporting orchestration of Magma controller and gateway, including access gateway. This function scope covers onboarding Magma CNF and VNF packages, defining services, and orchestrating the services through ONEP orchestration flow by leveraging ONEP runtime components and cloud infrastructure. Secondly, ONEP support Magma controller and access gateway configuration through the ONEP configuration flow. For instance, access gateway needs to be registered to the Magma controller to be ready to work with the controller and ONEP. Another one is ONEP supports Magma controller and access gateway life cycle management through the ONEP CNF resource life cycle management flows, for example, updating access gateway component. As the last initial scope, Magma network slicing will be supported. ONEP is a 5G network slicing orchestration platform conforming to 3GPP. Once Magma support network slicing later this year, ONEP and Magma can be integrated for the network slicing management. For the future scope, ONEP plans to support Magma controller access gateway control loops. For instance, ONEP collects network data through Magma and it can automate additional control loop, life cycle management orchestration actions. Also, with the collected Magma network data, ONEP can support analytics after that Magma plans to go further for identifying AI machine learning patterns on Magma controller data for smarter management. Next slide please. This is about ONEP Magma integration realization. The diagram defeats how ONEP and Magma realize the integration. ONEP has several sub components for design, orchestration, policy, assignment, configuration, FM, PM, and Covenant interactions. Magma consists of three major components as addressed before, controller access gateway and Federation gateway. Among them, controller and access gateway will be integrated into ONEP and tested first. The controller provides external interface for external systems. The access gateway implements CNF, BNF, PNF as IP edge handling. Federation gateways interacting with the MNO core network through 3GPP interfaces. ONEP manages Magma controller and access gateway onboarding and service design through the ONEP SDC component. Once Magma packages are onboarded to SDC, SDC designs Magma services based on the onboarded Magma models and distribute the Magma service package across the ONEP component. As shown in the diagram, there are two main interfaces between ONEP and Magma, controller, NOSPAN interface CM, LCM, and controller, NOSPAN interface FM, PM. ONEP service orchestrator and associated components work together to orchestrate the Magma network service into Covenant through the first Magma controller NOSPAN interface CM, LCM. Using the REST APIs, ONEP will register access gateway with the Magma controller and ONEP handles life cycle management on access gateway. Another interface point between ONEP and Magma for FM, PM is Magma controller NOSPAN interface FM, PM through the VES agent. ONEP DCAE component will collect FM, PM, and other telemetry data through VES agents. This is the summary of the ONEP Magma integration. Now back to you Catherine. So thank you so much Amar Payot and Bjong for sharing the first accomplishment made by the ONEP for Enterprise Task Force and also to give more insight about what we tried to accomplish in the coming months. So I also want to see the opportunity to welcome participation from new contributors that want to expand the applicability of ONEP. So if you're interested to join the ONEP for Enterprise Task Force, we are meeting on a big weekly basis every Wednesday. If you want additional information, you can go to the ONEP weekly and also type the Task Force ONEP for Enterprise business. And finally, we have also a mailing list if you want to reach any of us and additional ONEP community members. So now let's open the floor to any live questions from the audience.


Welcome to Digital Asset News to get top stories in crypto.  So first up, we're gonna talk about some Cardano highlights, which Charles Hoskinson came out and just did a brief update where he talks about the new summit that's gonna happen. He's gonna talk about a new listing that's gonna happen. And also, of course, the Alonzo smart contracts. So we'll take a look at that. Also, we'll take a look at three mayors in the United States who are super pro-Bitcoin and they share their thoughts. And I gotta tell you, I thought all politicians were evil, but apparently not. And then lastly, we'll talk about the Golden Ouroboros from Cardano Combat, the NFT giveaway, which we'll be doing right at the very end of this video. So stick around for that. But first, let's take a look at what's going on into the market. So right now, a little bit of a down day. I mean, look, we've been going up for the last, what, 10 days for two weeks almost?  However, I was wrong last week because it kept going up. But I can just tell you what history tells us. And that is that what goes up must come down. And I just can't keep going up forever. So I can't really tell you specifically when and hint, hint, nobody can. Cause I don't have a crystal ball and nobody's notice you're down. It's especially not me, but you just have to take the bad days with the good and just think of it this way. Weren't we just talking about how Bitcoin was at 29, 31,000 somewhere around there and that the whole market was down, but we were feeling pretty hopeful. Now, all of a sudden it goes up, it shoots up tremendously. We passed the two trillion market cap. We were just at 1.28 trillion, not too long ago. And now people are like in the dumps.  But today, I still think it's a pretty good day. But yeah, we are down a little bit, negative 2%. Bitcoin's at 48,214. Ethereum's at 3,200, it's still pretty good. Cardano's just blowing things up to 80 of 40% for the week and 5% down for the five day average. But when we talk about the big thing, which is what's gonna happen tomorrow, it'll be interesting to see what happens. And that's the whole point of the thumbnail. So let me just do this. Let's just jump into today's top story and with the Cardano highlights. Oh, actually first, I wanna talk about this, which was the traditional markets. Cause I'm always interested to see what's going on. I mean, of course with our market, crypto and digital assets, but how does it correlate with what's going on in the traditional war? Because as we see, we know that there is a lot of overlap between the traditional space and our space. Well, this is from Charlie Baleo. He's always got great stuff. Give him a follow on Twitter, founder and CEO of Compound Capital Advisors. He's always got the great stats. And he just said, hey, look, today is the first day of all time that NASDAQ hit 15,000 or the NASDAQ composite 15,000. August 24th, that's a pretty good indicator. And I was thinking myself, well, I wonder how the rest of the traditional market is. So I always take a look at the S&P 500 and I just kind of scaled it back by five years. And you can see we're like at an all-time high for traditional markets as well. So, I mean, if you look at what's going on in the crypto market and what's going on in the traditional space, you can say, yeah, there's a correlation. What I want to see, and what I think a lot of people are hoping for is can we uncorrelate from these traditional markets? I don't know if we ever can because there's so many different big institutions and players coming into this space that it's kind of hard for them not to because look, what's open 24-7, 365? Cryptocurrency. All right, so that's what's going on there. Let's jump into it, shall we? So this was a quick or brief update that Charles Hoskinson, the CEO or head guy over there at IOHK, broke Cardano and he just gave a couple of different things. And the first thing was, it's all right here. I'm gonna link this video in the description, but I'm just gonna give you a quick summary of what's going on. Let me blow this up so you can see what I see. So he talks about dates and announcements. The first thing he talks about was this, are different listings. And this is what I was thinking about might be pretty big for Cardano, which is Cardano is gonna be listed on Bitcoin on the 25th of August. I'll get to that a little bit more detail later. But he also talked about the Cardano summit. And if you don't know, the Cardano summit is going to happen on September 25 and 26. And it's gonna be a mesh of virtual happenings and actual live in different cities. So you can sign up for there, it's 100% free. I'm gonna do that myself. Maybe they'll go to kind of Puerto Rico where I'm at. Who knows, but that'll be interesting. And then he also talked about the big thing, which was the smart contracts at five minutes and 15 seconds. And basically what he said here between the dates and the about the smart contracts Alonzo, there was two big things that he said. One, he goes, everything's on time. He goes, so don't worry about that. We did, he talked about how the audit with Quantstamp went about a year ago and how they're trying to play, they're not trying to play catch up. They've already done the actual audit. Quantstamp said, hey, there was a couple of things you need to change. Actually it was very rigorous. They did all those changes. They went back to Quantstamp and said, hey, reevaluate what we just did. And they go, yeah, sure, pay us six figures. And Charles is like, no. So they're looking out for somebody else to actually do it. But he said that all the different things that Quantstamp said for them to change that actually did. Another thing that he talked about as far as like the smart contracts is that September 12th is going to be the day and they're going to release the smart contracts. Now everything's gonna be on a test net first. They're gonna run it through its paces but that's pretty much how they do everything they're at Cardano just to make things that are safe. And the third thing he said, which I thought was one of the most interesting things was he said that they're partnering up with a lot of different players in the cryptocurrency space, competitors, he said. And they're trying to work together to bring about some change and some really great things. And I thought to myself, that's fantastic. I've always thought in my small mind was that cryptocurrency can't survive with 10,000 projects, it's just not gonna happen. I think what's really gonna have to happen is either they're gonna be a lot more collaboration or there's gonna be a lot more absorption where you're gonna see some big type of entities like a Cardano, like an Ethereum, like a chain like start to just merge and acquire these other smaller projects and start to become even stronger and stronger. It worked pretty well for the e-commerce space, Amazon worked pretty well for the social media space. I mean, depending on who you ask, if you're talking about Facebook and what's happened, Instagram and things like that, I think that would be the next wave of what's about to happen. And I for one am excited. So that was pretty much in a nutshell. He goes over a couple more things again in the video, I'll link it, but those are the big things and the big takeaways from me. Let me know what you think about that in the comment section. Let's go on to the big story and what the whole thumbnail was built around, which was this. Cardano is to be launched on the 25th of August and get listed in Japan on this exchange called Bitpoint. Now, I'm not from Japan, shocker. And I don't know anything about Bitpoint. I try to look up some things. There's different basic information about it, but Bitpoint and especially just how rigorous, I guess the exchanges are in Japan, they're a lot more rigorous than some of our exchanges. We'll just say that. And for Cardano to be listed, it's a big deal, but I didn't know how big a deal it was. And it states here that lists such as this aren't constantly occurring. You've got Cardano is now one of the currencies on Japanese exchanges like Bitcoin, Ethereum and Litecoin. And when I took a look at Bitpoint, I didn't see a lot of listings. I mean, I saw those Bitcoin, Ethereum and Litecoin. And then I saw another different article where it says that to get listed on Bitpoint, and this is just hearsay, so I'm not from Japan. So correct me please in the comment section, if you are from Japan or know a lot of things in Japan, is Bitpoint that big of a deal because they were comparing it to like in that region to almost like a Coinbase or a Coinbase effective. If that is true, then Cardano could see quite a big pump tomorrow, the 25th, when it'll be listed on Bitpoint. So again, let me just think about that. That could be big, but you have to remember this. Last thing I'll say is these little ups and downs aren't the big play. The big play is what's gonna happen the next year, three years and five years. So always have that in the back of your mind. This is not investment advice, just investment opinion. I do not care what's happening in the short term. I do not care of all the different foot articles and things like that. I just see what's gonna happen in the future. And I've been doing this since 2017, just a lot of cost averaging. I think that if I just stick around for quite a long time, quite a bit of time, I think things will work out. And that's it for that piece. Let's finish up with two more, or actually one more snippet, US mayors of Bitcoin and we'll do the giveaway. So this was, it was an interesting article because mayors are politicians too. And politicians aren't really much to be trusted with, but hey, if you're speaking my language, I'll listen to you. And this was Mayor Scott Conger, I hope it's all right, of the US city of Jackson, Tennessee. And he states, as the feds continue to print more USD, this chart, and he was talking about the Bitcoin chart, will continue to rise, Bitcoin could be the present, could be the present, but is definitely the future monetary system and F 21 million, there isn't anymore. This is Scott Conger, so I will link him in the description. I've already followed him. I like that guy's style. I'll stick around and see what he's got to say. And he just references this. But what's more interesting, there was a couple more things. In April, he talked about doing a payroll conversion for city employees, as well as Bitcoin mining to add Bitcoin to the city's balance sheet. And then in July, he tweeted, how the city can accept property tax payments in Bitcoin and allow our employees to DCA in Bitcoin. Totally speaking my language, this guy's talking about, hey, look, we want you to be successful.  Here's what we can set you up with, dollar cost average, maybe they do like a little bit of education snippet, or maybe they just tell them to go to Dante Descripto. I'm just kidding. But yeah, I mean, that is a politician looking out for the betterment of his constituents. And I think that could play out very well. And lastly, to finish up Mayor Jason Stewart of Cool Valley, Missouri, said he's raising funds to give $1,000 in Bitcoin to every resident in the city. Another mayor who sounds like he gets it. And of course, Mayor Francis Suarez from Florida. But just so you know, Cool Valley, Missouri has like a whopping population of like 1,300 people. And then Conger or Tennessee, where is he in Jackson, Tennessee? That's like 63,000. So not like it's a huge amount. And hopefully everybody's safe over there in Tennessee. There's major floodings, but that's awful. But as far as this goes, it's a very, it's a positive step in the right direction, especially, I mean, if you take a look at, and if you work in the mayor's office in any kind of city that you are a resident of, just be aware that there's this thing called a pension crisis. And if you're not from America, we are like in deep in this type of thing.  Nearly every state in the United States is facing a pension shortfall. States have a combined 4.2 trillion in pension liabilities. These are all types of government employees, different type of employees that just get a pension and live off that for the rest of their lives. But less than three trillion in assets that decide to pay for those pensions. Let me do some quick math. 4.2 minus three, 1.2 trillion dollars. That's a lot of money. So if you're looking for a way potentially to roll this all together, maybe cryptocurrency might be your answer. Again, if you're a mayor, that's just an investment opinion. You can do what you want, but it seems like a pretty good deal. All right, so that's it for that. Let's get into the giveaway. I'm gonna make this really quick. So this was for the Golden or a Boris. The group over at Cardano comments, send me one to do what you want with it. I said, sure, we'll do a giveaway. NFTs, right, whatever. So I just said all you had to do was just pretty much comment and follow me and follow the guys over there at Cardano Combat and what I'm gonna do. So I never liked that whole thing. We put it through a randomizer and we randomly found somebody and their name is Joe Smoh or whatever else. I'm like, did you really? So let's just do this. Who is this? Klein-Vock, great. First of all, did you do? Did you follow me? Whoops, let me go back. Follows you, great.  Let me do this, do this.   Damn me. Okay, and that's it. So congratulations to Klein-Vock. He apparently is the winner and as you can probably see, just randomized it and that's what we got to today. So look, that is it for today. If you liked today's video and you found a little value, go ahead and give it a thumbs up. I know a lot of people didn't win, so I probably like thumbs down, but you know, hey, whatever. Also, if you like this type of comment where you listen to the things that are happening right now that could help you get into the crypto market and maybe make some moves, consider subscribing. A lot of things we talk about are time sensitive and that's it for today. So thanks so much, I appreciate it. See you on the next one.


Yeah, thank you very much for inviting me. I mean just to make it, you know I don't have a disclaimer and I'm trying something new not having slides But my background actually said, you know, it's it's a little bit Different. I'm I'm new at sink tank Territory my my background is electric power I spent 20 years in the Canadian power system in Ontario And I used to run the International Emission Training Association that David is is a CEO now A chair now, so it's a very very heavy corporate. I have to say that However, it's good now to be able to write about things without having to worry about reaching consensus in the business community and having to you know before you get up and say something everybody know what you're gonna say so So I'm free as a bird as they say but Yeah, we you know, we actually had a session yesterday at that saps Looking at the 2030 paper and one we had a guest from from Ireland Charing that meeting Frank Connery chaired that that meeting for for us at saps So we had a quite quite an interesting discussion and and Frank actually managed to extract a lot of wisdom Well, this was very very useful, but I'm coming back. We also or I also went through the Trying to understand as to what had to work and what hadn't work in in the EU climate and energy policy and The ETS in in in my mind, and I don't disagree with David Has worked and is still working and when you say worked I look at it from the point of view of Good market work functioning and would market functioning that way we clear definition or somewhat clear definition in terms of tight spreads and liquidity and Transparency and the ability to get in and out of the market Etc etc etc this has happened. There's no doubt about even in the worst moments of the ETS This has happened, but the worst moments have really not been very long They've been at the end of period one and then we but we knew we're getting into period two However, I don't know that this is the only be the case for much longer I think that what we you know kind of moving from one period of the other and not having banking has been masked by By this this this jumping between phases because what we see now We see variability in in prices of 10% a day I think both four or five times for the last three weeks 10% a day if you have 1300 on the Dow in one day somebody's gonna jump out the window and yet We are taking this in stride and we think it's normal, but I don't call this good market functioning And the kind of thing that you want to see in the market that drives your economy The second thing that I would also say that we've seen reduction quite clearly we've seen reduction But we don't really understand why we assumed that most of them have happened because of the economic crisis and At this point we start getting some sympathy for the Eastern Europeans, you know, remember some of you who follow the International negotiation they've been blighted by this term of hot air, which is very pejorative in my mind because we paid with a lot of hot Social misery in Eastern Europe and they you know, they don't forget that they said we paid for it But now we have Western European hot air in the ETS so we don't really know what the amount is due to reduction what your amount is due to To the impact of ETS that probably not much do the result of ETS The renewable has been successful beyond what we thought it would be Energy efficiency has been but much less so And we have seen the international impact of the EU climate change policy a reflection to some degree of the domestic policy As being somewhat successful and somewhat less so we've seen a kind of the impact of on the CDM and driving a price off of Carbon internationally and that becoming known as a cultural change an enormous success way beyond people what people understand But at the same time the the success of the EU in driving international climate change policy Has also been to some degree mixed in my own opinion You know Copenhagen hasn't been this disroaring success And I think we've made some some progress sense and the EU has managed to move the agenda in the international negotiation But what is also important the energy investment really has not been very successful because we've seen energy Investment going in the wrong places the only one that I think we're seeing that successful is that that was subsidized The only you know we see coal fire generation and the only the only generation that is renewable is because the subsidy and feed in Terrace, but otherwise I don't think that it would be there So now what do you know what? Having reflected on on kind of this what are some of the lessons learned of Of what we have seen because you know you have to learn from the past in order to be able to move successfully in the future first of all We need to understand that we are in the middle of a of a transformational period This is not a normal period and climate change is not not a normal and not an easy topic There are two international Dimension things that are not working very well We have not made success one of them is the UN FCCC negotiation and the other one is Doha round of Trade and the reason for that is that both they are both trade agreements essentially So let's let's not forget that and not not forget that we are trying to negotiate or trying to do in Europe About four or five different things at the same time. So a whole bunch of people are being agitated about back loading Same people talking about structure reform in the ETS Roughly the same bunch of people talking about post 2020 or 2030 climate energy policy and And somewhat different people talking about the 2015 international agreement Timelines are different. The issues are not necessarily different. I didn't relate it But all that in the major in the middle of a major international realignment Was was all the bricks becoming or the basics now becoming a very different economy that we used to have So the fact that we're not reaching conclusion doesn't necessarily that we're stupid or we're not trying but the reality These are this is a very complex and very kind of watershed moment Now what are the interaction because I've been asked to talk about interaction as best as I can and I think that the Interaction there's a number of interaction identified one of them is the interaction of the energy between energy and climate change policy the interaction of the international dimension and International the interaction was competitiveness and leakage and these are the three ones that that I found What was discovered during the in relation to these interactions up to now in the 2020 20 package one of them is the the fact of the lack of flexibility And we you know in the business community and I've been you know part of it until about a year and a half ago had been asking about flexibility and predictability and and that is you know kind of We do you know this is something a new phenomenon because for the longest time with people have been asking to be to have certainty There's no certainty in business. There's there's some you know kind of predictability or have you act But the predictability that's been asked now has become a rigidity and the rigidity is transformed There is especially in the UTS, which is the centerpiece of this whole thing Basically has become so rigid that it cannot respond to market forces the changes are given in the market But the time frames and the mechanism for for for adopting those changes are in the legislative arena and the legislative arena in the US I'm in the EU. I'm being Canadian. It's a little bit much a little bit simpler in Canada Even though we have provinces and so on but this is by factor off By a factor of incomprehensible to me a long time to even begin understand how this thing work so We Essentially we we we needed to understand what the Coordination was between the various pieces what we have we have three targets There has been some sought-being given at the beginning as when when those targets were set as to what the impact would be But nobody really thought to put in place a mechanism How to adapt if there were changes, you know, we have an abysmal way of predicting the future especially the economic future And as such we predicted in 2008 What the economic future is going to be it up to 2020 and established targets based on that and no mechanisms to adapt to that That is a little bit rich. Frankly. I mean not surprising that it doesn't work So if you want to have either you have one target Which is the greenhouse gases because this is you know, you want to have an environmental target or you have three targets Because you have other objectives But the other objective I really have to be explained and specified very very clearly if you have three targets You have to have coordination between these three have to have it Especially if you want this to be technology neutral now if you don't want it to be technology neutral You have you want to do certain things then I mean let's kill something and that's not for ourselves It costs money to run an emission trading system It counts money to have people running and trade, you know We built the last ten years we built trade desk and expertise and by the way every day I see friends of mine that have been driven out of this greenhouse gas market because there's nothing to do Are these guys gonna come back is the same talent gonna come back the answer is no so unless we pre provide a Flexibility in this market. I think we need to this is not gonna work. The second thing is the flexibility to responding to The Variation in what we predict and in renewable friends that we expect the renewable have certain impact But the impact on on the carbon reduction has been much much larger How do we adapt to this so we have a piece of adapting to what the economic condition is and it needs to be some Flexibility mechanism is in there, but at the same time we will have a predict a Forecast of what the impact of renewables is gonna be on carbon David has just had all these very nice graphs explaining What happens? But if you don't have something that kind of looks okay We predicted so much but actually there's so so much more and as such we got to take stuff of the market because otherwise You shadow the price of carbon and carbon becomes irrelevant It's not only becomes irrelevant, but it's a loose loose proposition because it's irrelevant by you pay for it Because it is in the cost of electricity, but you it has no impact. So it's wide. Why do we do this? It's you know, I remember I frankly I remember talking to Stefan Dion who was the minister President of Copping Canada and basically Stefan said to me miss your mark cool Which is you know incomparable French accent it look this ambition trading system in our industry doesn't support it You don't support it. Why should I do it? I'm a politician So if it has no pride if it has no impact on reduction has no impact on reduction But it cost the Greeks and the and and the Spanish at 27% unemployment Why do it unless you can actually make it the instrument that is effective and drive something and yes We do understand that it drives certain elements of the economy doesn't drive the basic R&D It does not be drive the basic R&D drives Implementation, but so do feeding tariffs feeding tariffs do not drive R&D for feeding tariffs drive implementation so it's a competing instrument and Finally the there is an interaction the international dimension and that interaction also has to be taken into account Because we have done better than we thought and we have catalyzed all the Bolivians and the Peruvians and the South Africans to produce stuff and Now we find the situation so you guys are too good at this you're producing too much too many credits Including your own young people here in Ireland at the electricity supply board But we don't want it It's really creating a serious problem and unless we manage to have a mechanism to deal with this gonna be a problem The other thing that I think is learn is this this job thing this green job things that you know my own steps organization I have a whole bunch of people having grant and doing research and it is great But the fact that the matter is what yes, we catalyze green jobs. Yes, we catalyze all this wind energy But it's not it's not here. It's all in China We did the research we we produced everything and then the Chinese are Producing it at prices that we cannot compete with and then we're installing it here So if we are to have this as a target for green job, this has got to be thought around as to what else needs to be done In order for it to have this job being retained in Europe or else We gonna we're gonna subsidize somebody else's research and and and so on because what's the basic research is done and many of these things It's not necessarily very complicated. I don't know much. I've never made a turbine in my life But I understand it's not that complex to make a turbine. Is it bang bang, okay? In terms of infrastructure the same the next thing is infrastructure really we have implemented all these Renewables, which is great But we cannot get them out of Spain and we cannot them get it off the north of the Germany because the infrastructure is not there And unless we do this there's no point in doing I'm an electrician and I don't understand too many things understand two things one of them is football and at Barcelona loss Which hurt me the other one is in I understand the electricity because we had the same problem in Ontario and In Quebec we had a lot of generation up north hydroelectric and we had no ways to get them down there same situation here Not very different and finally last but not least and Right now. I do not believe with all due respect to my friends in industry that leakage is a problem At three years the tone it cannot be a problem I can tell you that at $15 a ton in Canada the price the impact on the price of barrels 25 cents So if that is the case, I'm not sure what your calculations are, but that's what I'm being told So at three years a ton is not but if you really are to drive this and you have 25 30 35 Years a ton then it becomes a problem becomes a problem twice that is not being addressed quite well One of them is an internal problem because the way it's treated right now is only certain bunch of certain member states Then can do the subsidy because they have the money But most most people don't the Italians in the Greeks and the Spanish can't do it But the Germans can then you have the real elephant in the room the real elephant in the international dimension Because we're gonna get to two feet 2015 agreement and we linking with Australia and This is going to be a competitive issue because we don't really understand how to do this. I'm happy to collaborate. Thank you very much


So my name is Abhijit Pasoda, I am working as a senior software engineer in Red Hat. I work mostly on Ansible core team. So there I work on VMware related modules. So as you know, we actually provide you the modularity kind of architecture. And we have different modules for different cloud technology, networking technology. So in that thing I work on VMware technology. So about me, I am a free and open source software into a evangelist. I am a part of a core Ansible core team. You can follow me on Twitter on this hashtag. You can find my GitHub ID as this. Currently I am managing VMware community under the Ansible name spaces. So whatever is related to VMware, guest, hypervisors and all that kind of stuff, I am working on that. So today I am talking about how you can use Ansible and take it to the next level of the things of Ansible. Not limiting yourself to the just playbooks and rules. So I will be telling you about pluggable architecture given by the Ansible which are called as plugins. I will explain what are plugins and how, what are the different types. I will show you two existing plugins. One is like printer and second is action plugin. Then I will walk you through the demo of couple of plugins and I will tell you how you can write your own plugin. So just stop me if you have any question. I can answer that question and if it is a big question we can take it offline. So just to give you a brief idea, what is Ansible? Ansible is an open source IT automation engine which can be used as a configuration development tool or application deployment tool or a task automation tool. When you have certain kinds of tasks which are repetitive in nature, like deploying an application, configuring the application, then uninstalling it, cleaning up the part and all that kind of stuff, which are very repetitive. People try to use shell script, they try to use Python or any other scripting language in order to automate this stuff. So Ansible take this task to the next level. You can use Ansible for different types of tasks that I have mentioned. So what is the advantage of Ansible over other configuration tools like Chef, Puppet? So the first advantage of Ansible is agent-based. Agent-based in the sense you have a control node and you have a management node. Control node is the one where Ansible is installed and the managed node is where you are managing those nodes. So the software like Puppet, Chef needs to have agents installed on the managed nodes.  So it is agent-based. Ansible directly talks to the managed nodes using SSH and get the real-time data or the facts related to that managed node and performs action on top of it. So that gives you an additional advantage that you don't need to store the data into any data list. So it avoids the bottleneck to the data list. It's like you are talking to the machine in a real-time gathering the information and returning back and performing some action depending upon that facts. So it gives you the additional advantage of not storing that data anyway. So it also removes the problem of steel data. Steel data means if the agent is providing some old data of a particular machine, the performance or the operations can go wrong. Means like if I am having an agent on top of any managed node and it is saying that I have this dead time in this thing and that is not matching with the operator or controller node. You have a drift in time and so you can go wrong in that operations. But when you have a real-time data like you are asking a managed node that what is the time? It is written in the time and you are performing operation on that time itself or that fact itself you cannot go wrong in that thing. So it helps a lot when you have a bunch of operations deploying in this thing. The second advantage that I am going to talk about is idempotent. idempotent is a phenomena. So I will give you one example like let's assume that you have one file located in some 10 directory. So the operation is like if you want to create this file, just create it and come back. And if that file is not created or it is previously created, don't do anything. So the atomicity of the operation is called as idempotent. Means like either the file is created, don't do anything and if it is not created then create it. So it maintains the stability of the system. It's like maintaining the atomicity of the system. That is why idempotent means like to give you one another example is like if you want to install any package on a particular machine you first go and check like rpm-qw that package name. If that package is available, you don't do anything. But if that is not available, you just do here install and install everything. So these operations help a lot. Like to give you one example like if you are deploying on 1000 nodes some package management thing and network goes down and you don't know how many systems are installed with the package and how many systems are without the package. But here Ansible will give you the additional advantage that it will detect from like I said it will gather the real-time facts from the machine and it will start from the place where it took off. Means like it will start from the place where it filled and it will start on the use. So you don't have to maintain the state of the machines which are in that operation. That is called as idempotency which helps in stability, consistency, reliability of your id environment. So another additional advantage of Ansible is that it is very simple to setup. It is just like pip install Ansible and it will install Ansible with its dependencies like Paramiko and other cryptographic libraries and all that. And even if it is a simple tool, it is very powerful. If you are using Ansible, you will know that it is very powerful because it comes with a bunch of models and a lot of libraries and all that kind of stuff. So any question till this point? So this is just an introduction how Ansible is working and how it is helpful. So we will come to the topics, talks topic that is what is plugins. Plugins are nothing but so Ansible is very flexible in extensibility. It provides you a facility to create plugins and attach into the existing system or work on top of the existing facilities. So it is very extensible and a lot of, so all the architectures of things are pluggable. So you can write your own plugin even if it is not available in the upstream report.  Before that I will show you, I will show you the repo of Ansible and how it is helpful and where are the... So this is my Ansible repo. I have just checked out of this thing. If I do ls minus other. So here is the cloned repo of Ansible. Over here if you see inside lib directory there is a directory called as plugins. So there are different types of plugins available. Plugins are nothing but additional libraries you can say which can be used by Ansible to perform certain actions. To give you one example like if you are trying to filter something. So I will give you one example so you will get to know how it is useful. So this is a plain Ansible repo. Here I am doing what it is running on a local host. I am not gathering any facts. I am using variables and I am performing two tasks. So the first task is just debug. Means like it is a print statement of Ansible. Just printing whatever the variables are available. The second task is I am using a full filter. So plugin filter is a special type of plugin which can be used to modify the certain behavior of the things. So if I run this playbook. So if you see I am using Ansible 2.8 and I am using the developed modules. And here if I run this module Ansible playbook. So you will notice that it printed that variable over here. And if you notice that there is some change in the second print. That this change is due to this shuffle.  So if you go in the source itself. So if I go in live Ansible plugins filters. You will notice that there are core functionalities available. The problem is coming from icon library random directly. So it is just shuffling the things which has provided to that thing. So I will just give you one more example. So these are all kind of filters available. So to give you idea like plugins are nothing but a specific type of modules. Which can be worked in order to handle the situation. So here you will see there is another filter called as 2yaml. So if I use this thing. If I want to use this filter. I will do what? I will just use debug. Debug is nothing but print statement of Ansible. And I will say just yaml. And if I run it again. You will notice the difference. So it took the variables from the Ansible. And it converted that into a yaml things. And if you check the source of that thing over here. It is doing nothing. Just importing yaml and doing yaml down. And using the things and returning the text from it. So it is doing some additional stuff on top of your stuff. So that is the advantage of using plugins. So there are different types of plugins available. Like there are action plugins. Cache plugins. Callback plugins. Connection plugins. Shell plugins. Strategy plugins. Lookup plugins. Wars plugins. Invented plugins. And filter plugins. So I will give you one more example of these things. So you will get to know how they are used. So action plugin is nothing but plugins which are used on a control node. Or a master node. In order to change the behavior on a master side. So let's assume that you want to add a host to a inventory. So that can be an action plugin. Because the things which are happening are happening on the master node. That is the sensible control node. So if I click on this. Action plugin. So it says that you can use these plugins with the conjunction with the modules. And do the task on top of master. Or an sensible controller. Okay. Then the second type of plugin is like cache plugin. So many a times you collect facts from the various environments and all that. So you don't want to consume time in gathering those facts. And want to cache that thing. So here the cache plugin will help you. So cache plugin will do what? Whatever the cache facts are gathered from the machine. It will store using the cache plugin. So there are different types of cache plugins. Like JSON file, memory cache, memory cache. So by default as it uses memory cache. Which is a RAM backed thingy. Then MongoDB as a fact cache database. Means like whatever the facts are gathered from the managed node. It will store them in a MongoDB database. Or JSON file. Or in a Redis database. Or it will store these facts in a YAML database. Something like that. Okay. So any question in this point? Like why cache plugin or action plugins are used? No? Next is like callback plugins. Callback plugins are nothing but plugins which can be used in order to change the behavior of a play.  And then you want to modify the behavior of the play. Means like you don't want the output to be seen. Or if you have some secrets in your playbook. Which you don't want to show to the user. Something like that. So you can use callback plugins. Or to give you a good example. Like if you want to run multiple playbooks. And want to send the report directly to the mail. Or any system that means mail box. You can use this callback plugin. So you just have to whitelist the callback plugin. And use it. So these are different types of plugins available. So like I said. You have mail plugin. Which sends the failure or everything to a mail. Specified email. Then there are different types of other plugins. Like OSX say. OSX say is a plugin which if you enable that thing. And if you are running ansible from a macbook. So macbook will start speaking using say command. So it will say like playbook one running. Playbook one finished or something like that. So that is like a callback plugin. And you have different plugins available. In a scenario like when you have things. If you want to store your logs to a certain database or logstash. You can use logstash callback plugin. Which will gather all the logs. And put it into the logstash server directly. So these are different types of callback plugins available. Then there is a connection plugin as well available. Which is used for host connection management. So what is host connection management? So you have different type of connection. From the controller node to the management. Like SSH. Then in case of networking devices. They have their own proprietary protocol. Which ansible talks to the proprietary devices. So you can use these kind of things. Like to give you one example. Is like you have a docker container. Docker D1 running on a machine. And you don't have IP address. You cannot communicate to the docker container using IP. Then there is a way with which you can directly talk to docker D1. And with docker container D1 will talk to the container itself. So that is the flow. So ansible uses that and using this connection plugin. Docker connection plugin you can directly talk to the container itself. Without having the network connectivity. So that is the one use case we can see. Then there are different type of other plugins as well. So there is a winRM connection plugin available. With which you can directly talk to the windows machines. So you have ansible installed on Linux. And you want to control the windows machine. Then you can use this winRM connection plugin. And you can directly talk to the windows machine. Then for Solaris zones you can use zone plugin. So with which you can directly talk to the Solaris zones. Any question tell this point. So that there is a shell plugin. So shell plugin we have different type of shell. Power shell then SS shell then fish or CSH. Then you can configure this in order to use this shell. If you are not using bash or something like that. So this is very interesting plugin. So let's assume that you have written a playbook. And you are running it for 6 months and so on. It is working fine. One fine day it is not fairly. So how do I get to know why it is fairly. So there is a very interesting plugin called as debug plugin. So if you enable debug plugin. So it will put you into a 3db kind of thing. Python 3db kind of thing. And you can debug all the what is happening. And what kind of data structures are there available in the memory or something like that. And you can go to the pinpointer issue directly. Okay. Actually by default it uses linear strategy plugin. Okay. Then there are two available as well. Free strategy and host page. You can go through the documentation. And if you want to check out how they work and how they can be used. Then you can go through the source code. Okay. Then there are lookup plugins as well. So if you want example. They are like. So let's assume that you store your passwords in a centralized repo. So something like red slash or some free IPA kind of environment. And you just retrieve those passwords from that environment. So if you want to retrieve those passwords. You will need something on the fly over there itself. So you can use lookup plugins over there. So lookup plugins are nothing but modules which will talk to your credential management system. Take out the credentials from their thing. And put it into the playbook itself. So that is one of the use case of lookup plugin. You can use lookup plugin for different reasons as well. Let's assume that you want to read file into the memory. So you can use file lookup plugin. And you just specify the name of the file. And symbol will do what? It will open up the file and put it into the memory. And it will make it available to the playbook. And you can start working on that file contents. So that is how you use plugin. Lookup plugin. You can also query those things. Like if you have a dictionary. And you want to take out some variables out of it. You can use dict lookup plugin for that. So these are the few of the examples with which you can use lookup plugin. So like I said one password. Then query slash. Release query. Then release. These are different types of lookup plugins with which you can actually different actions. Then there is a worse connection plugin. So many a times you wanted to inject your own variables into the Ansible workflow. Then worse plugins can be used. So worse plugins inject additional variables into the environment. So there is only one good example. That is host group wars. If you want to put host group wars into your animal. You can use this variable and get the variables into the Ansible workflow. Inventory plugins. So this is very interesting type of plugin. So previous 2.5 release we had scripts which uses to gather the dynamic inventory. Inventory is nothing but a list of managed nodes. And to gather the list of managed nodes on the flag. Means dynamically creating the list of the managed nodes we use dynamic inventory. So previous 2.5 version we had scripts which can be used to gather the information from the inventory. So let's assume that you have an open stack environment. You spin up some VMs over there. And you don't know, so the IPs are very dynamic. You don't know anything about that. So you write a dynamic inventory script and you gather the information from there. And put that information in Ansible Playbook. And Ansible Playbook will work on those machines. So now inventory plugin will do what? So previous 2.5 release we need to run the Ansible dynamic inventory script on its own. With inventory plugin, dynamic inventory gathering part is part of Ansible core engine itself. So Ansible core knows about the inventory on the flag directly. So you don't need to rely on 3rd party scripts or something like that. You just use inventory plugins and the inventory details are available with the core engine itself. So there are different types of plugins available. To notice, let me tell you about this thing. So there is AWS EC2 inventory. So if you are running EC2 instances, then AWS EC2 will gather information from EC2 and put the information in YAML format and provide it to the core engine and tell you that these are the inventories available. These are the host wars and group wars and all that kind of stuff. And you can work on that dynamic inventory itself. Same case goes with Azure RM. So it will go to the Azure resource manager and pull out the virtual machine's information and put it into the Ansible's inventory directory. And it will work on that thing. So these are the inventory things available with the thing. Okay. After this thing, there is a last thing like I talked about. It's a filter plug-ins. I'm not sure what happened to this thing. Last many things. It's okay. So filter plug-ins are nothing but like I said, if you want to massage the data which is coming from or going to somewhere, if you want to do some manipulation, data manipulation, then you can use the filter. So it will do what? It is nothing but a ginger filter which will take the data, massage the data and output it in a certain way. So like in the previous case, we had a normal YAML data and we wanted to convert it into some other YAML. Or we had some JSON data and we wanted to convert it into YAML. Then we can use the two YAML filter and do the modification. Okay. So these are the different types of plug-ins available. We have seen the filter plug-ins previously. So we'll see one action plug-in over here. So all the plug-ins are available over here. In Ansible Laboratory, they are live over here. Ansible Live Ansible Plug-ins. So whatever I have talked about, those plug-ins are mentioned over here. So if you want to know more about them, how they work and all that kind of stuff, you can go through this directory and you can find their individual working functionalities. Okay. So I'll give you one example of action plug-in. So many of you must be knowing that there is a module called as command which executes command on the given machine. So here I am doing what? I am running a kind of command called as command and providing the parameter called as ls and normal ls command and running it on the local host user. So command is a module. Command is a module. I am providing the arguments with hyphen a, that is ls, and running it on a local host. If I run it, it will print out the list of the files which are available in this directory. And so how action plug-in comes into the picture. So if I go into the source of command plug-in. So here it is doing some additional stuff. So let's assume that... So here you will see that there is a line called as self-execute module. So this is the API which runs the module. So here it is doing what? It is taking additional parameters from the users and executing the module itself and returning the result over here. So I'll just understand this for right now and show you the real working module over here. So you'll get to know how they are working. I'll show you the example itself so you'll get to know how it is working. So I have written a small action plug-in. Okay, this is a bare minimum action plug-in. Action module is the class and it is inherited from the action base. And it has the built-in methods and we have to overwrite that method in order to make it happen whatever we want to happen. So here we are doing what? We are just calling the super of this thing and then we are displaying and here and then I am just changing the result of this thing to true and I am returning the result. So I'll just show you one playbook which uses this action plug-in. So here I am just specifying that sample is that action plug-in over here and I am storing that result of that thing in register in a variable called as setup output and I am just printing it out here. And if I run this playbook you'll notice that the task I would have any modules which is called as sample. It says that module sample not on. That means in that part there is no module called as sample. But we are using action plug-in. We have created one directory called as action plug-in beside your playbook play or playbooks and which contains a file called as sample.py. So it is picking up that sample.py as an action plug-in or a special kind of a module and running it and doing a lot of stuff. So if I run it again, providing hyphen vv you'll notice that there is a message coming. I am here. How that message is coming? If you go here, I have written display.vv. I am here. So vv specifies that I have specified hyphen v two times. So that is why it is coming from that. And why the status of play is changed to true because in this action plug-in I am explicitly changing the state of this thing. So while returning the result I am changing the status changed to true. You can imagine various scenarios like you can take an example like you want to remove some explicit data from your playbooks or modules or something like that. Like you have a Windows registration key or VMware ESXi license key or something like that. And you don't want to show that license key or any explicit protected data. Then you can just delete it from this result directory and it will print out to the result. And that is how you can leverage the plug-in itself. I will give you one more example of action plug-in so that you will understand it more. Let's assume that there are two machines like an Ansible Controller machine and a Managed Machine. And these two machines are not in sync. There are time differences very much. And you want to calculate that time difference. So how do you do that? Either you can write your own module or you can write a shell script that goes and checks the time from the managed node and comes back and calculates the time difference and report it to the user. But here I have done what? I have created one plug-in which does what? Here first it goes on the managed node runs the setup module on that machine takes the output checks whether the output is failed or not means setup whether the setup module failed on the other machine or not. If it is not, it will take out the date time from that machine means whatever the facts are available. I will show you one thing first. So if I run Ansible-M setup module setup module on the local host you will notice that this is a different type of facts available from the machine itself and there is a date and time available. So you will notice that it is reporting me some kind of data about the date and time and I want to use that data. So assume that I am going onto the managed node taking out the date time and calculating the difference in between the controller node and the managed node. So that is how I am doing over here. So I am checking whether the task is failed or not taking out the date and if the date is available from the remote machine then I am calculating the date of this controller node doing some data over here and creating a resultant data structure called as RET and RET contains data in second, data in time and data in microsecond and I am just returning that data into a new dictionary and returning back to the Ansible. So if I run it again using