Thank you for joining us today for this session, Demystifying the Edge with the new LF Edge Taxonomy and Framework. I'm Molly Wojcik and I chair the State of the Edge Landscape Working Group as part of the LF Edge Foundation. And I'm also the Marketing Director at Section, which is an edge compute platform provider. And I'm joined today by Vikram Swatch and Jason Shepard. Gentlemen, would you like to introduce yourselves? Sure. Hi, everybody. I work as a product manager for Mobilegex. I deal with most of the infrastructure in the company. And I chair Technical Architecture Working Group here, which helps define and was one of the outcomes of that group's work was this paper which we released. I'm excited to be here today. Thank you. And Jason Shepard, I sit on the LF Edge board and I've been involved a variety of different aspects of LF Edge, helped work on the white paper with Vikram. And I also lead our ecosystem efforts at a company called Zedita, both in the open source realm, working with, of course, LF Edge and then also in the commercial sense. Excellent. So maybe just to kick us off, we can for those not as familiar with LF Edge, just give a high level overview of the organization. So LF Edge is an umbrella organization that aims to establish an open, interoperable framework for edge computing. And it's really about accelerating adoption and the pace of innovation for edge computing through cooperation. And so that's kind of the basis for, excuse me, this taxonomy work that has been done. And so Jason, I'm going to open it up with the big meaty question of how do you define edge computing? Yeah, that was a good question. So it means a lot of different things, a lot of different people. Obviously, any kind of emerging trend, there's just a lot of definitions. I always say the old standards joke is, how do we solve the standards problem? We're going to come up with a new standard. And kind of the same thing with taxonomy is, how do we solve the taxonomy problem? Come up with a new taxonomy. Simple terms, edge computing, as I would see it, it's moving compute as close as both necessary and feasible to subscribers that need it. So the necessary and feasible are key. So necessarily I need to move compute closer for reasons of latency, bandwidth, et cetera. Feasible is, if you're serving autonomous vehicles, people, et cetera, obviously heavy compute, there's only so far you can go, maybe to the base of a tower. But if you're doing things, say in a factory floor, you're going to increasingly see that compute, it's feasible to deploy more heavy compute on that floor. So those two criteria, I think kind of cover it, but there's a lot of considerations within that. Yeah, certainly. And Vikram, given the confusion out there around definitions, tell me about the recent LF edge taxonomy paper and how it came to be. Yeah, it's actually based on the diverse communications we are seeing. I mean, as Jason was describing, edge computing, the nearness purpose of why we want to distribute this, right? So fundamentally, if you're going to distribute cloud, do the in-service providers are in user's edge, the market focus is on it. So for somebody who is developing an IoT application, he's only concerned about how does he actually make it work on a factory floor, or something like that. So the conversation is siloed in there, right? But if you look at some service providers and they will define their network accordingly and they'll say, near, far, take, then, and different terms will pop up. The ideology behind edge computing is going to be derived on cloud-native technology. There's not going to be something new, right? So how do we take some of these principles and try to define some common language around how to actually can divide this scheme of edge, right? And then what can we do to help solve 14 different standards defining the same thing, right? In terms of what market really needs, right? And when you boil down or crystallize that conversation from market perspective, it becomes much easier because then you can see commonalities across these models, whether people are using containers, whether they're using cloud-native technologies, those principles still apply. And that's how we try to address them in a language we will understand, right? In a more easier way than complicating it with more standards, right? So that was the effort which drove this exercise from our side.  But since it's a continuum, you know, the whole point is to extend, you know, these cloud-native principles as far as you can down into the various different edge locations but also recognize inherent trade-offs. So when we've seen a lot of taxonomies out there that say, you know, near and far, if and then thick as Vikram alluded to, it's confusing because they're ambiguous. You know, what does that mean? They're loaded terms as I would call them. And so the idea behind the white papers we worked as a community was, how do we base a taxonomy on inherent trade-offs, you know, versus loaded terms? And so that's kind of how it came to be. Yeah, that's great. And loaded terms and also relative terms depending on as you were saying, which, you know, area of where you lie on that edge continuum. Yeah, I see people all the time talking about real time, but it's very, very subjective, you know, real time in building automation is 15 minutes, real time for your airbag and your car, a little different. Right, yeah, it comes down to that latency-sensitive versus latency-critical. Right. And so can you describe the overall taxonomy in the paper? Vikram, go ahead and maybe. Oh, yeah, yeah, yeah, okay. Y'all talk about just sort of a high level. You know, so this is a view from the paper. I highly recommend, you know, going to download the paper if you just type in LFEDG taxonomy, it should be the first hit. And, you know, as we've been saying, it's a continuum. And the way we broke it down and the paper goes into great depth is as you go from centralized data centers to constrained devices, there's a couple key paradigm shifts in terms of these trade-offs.  You get on the left side and there's eventually trillions of constrained devices out in the field. So the scale factor is exponential as you go from right to left.  And then of course the division is this last mile network. So that's that point where you switch from a wide area network to a local area network. And so that's a key delineator when we talk about is something latency sensitive or is it latency critical?  It's a safety standpoint.  So that's this type of stuff. We're gonna see more and more processing moving, you know, kind of into the user edge on-prem. Whereas if it's latency sensitive, but I need to provide a better experience, then you're gonna see a lot of aggregation happening, you know, say at the server's provider edge, right? I bring that process closer, but people can still, and people can benefit from that better experience. And, you know, I'm sure Vikram can dive in, you know, deeper there. The user edge, there's one big delinear that we talk about in the paper where the on-prem data center edge, that's very similar tools as you would see in the centralized data centers. We're seeing evolution into, you know, Kubernetes, for example, taking the world by storm, we're seeing evolution of Kubernetes moving left. Things like K3S, you know, just this week was, you know, announced K0S and we're seeing a lot of innovation happening with Kubernetes. But they're generally the same tools. So the next big inflection point is between the on-prem data center edge and what we've called the smart device edge, here's where you're no longer in a physically secure data center. So now you have unique considerations around how do you manage distributed devices? How do you secure them when people can access them? And so that's, you know, first as wide area network to local area network, then it's the security considerations of being in a physically secure data center or not. That smart device edge is starting to get into the wild west where you've got all kinds of different, you know, devices, many, many different types of form factors. That smart device edge, it's both mobile devices, you know, PCs, tablets, you know, mobile phones, et cetera. Very well solved ecosystems, you know, driven by Windows and, you know, Android and iOS, et cetera.  So you'll see a lot of the projects are focused on how do we create more of an open foundation for that type of workload. But those devices are still capable of running apps. So think of smart device edge as devices from at the lower extreme, they have enough memory to run apps, at the upper extreme, you know, it's a small server cluster deployed outside of a data center. So there's a spectrum, all of these, you know, elements have a spectrum. And then you get below the smart device edge and now you have constrained devices and the delineation there is that there's so resource constrained that the stacks tend to be more, they're embedded, they tend to be more developed specifically for a given device, a specific type of silicon, you know, so the fragmentation gets even higher. And so it's, there's different paradigms, they're very similar principles as you move left, but they're necessarily different paradigms because of these trade-offs. Yeah, so maybe Vikram, you can, given your focus at Mobile Edge X, you can tell us a bit more about the service provider edge and key considerations there. Sure. So if we look at today, service providers typically for last so many years I've provided a bit pipe, right? I mean, I don't like to use that term because they've just decreased their value, but at the end of it, they are the ones who are carrying the traffic across to these end devices which we use often, right? And the problem was mostly like serving content, right? So if you see the design principle where let's say Google or Netflix have this huge amount of content, right? Which is running through these pipes, they tried developing caching technologies which was based on the user consumption from their end users. So they could figure out, hey, I think in this region I need a cache, I probably need to download this content in the midnight so that my users can use it, right? And those principles will hold on true in the next generation too because that's the principle of how you manage limited resources, whether it's caching or whether it's network. But then there's other trend and service providers which was not visible for quite some time which is the interactions between the devices and the back ends is becoming more and more immersive. We talked about downstream, but there's an upstream trend which is coming in where people need, we talked about latency sensitive but we fear playing a game and the augmented reality application. These things become more and more data intensive just to fall, right? And also latency sensitive, right? And those are typical problems of privacy and security, you know, definitely encroach and so if you actually are running a robot in your factory floor and these scanning images, that's a highly sensitive data to your company, right? You probably do not want it to be exposed into a public domain of a different country. So things like that of that nature where robots and automation is happening, how do you build a service provider, right? Which is no longer a byte pipe, it's actually can do a meaningful role there, right? If it as when we look at them in the paper, we identify a typical deployment model which works in harmony with the cloud because it's not that people will just shift their working model and cloud and say, yeah, I'm gonna develop for edge. That's not how it probably would work. So in this continuum which Jason described if I look at the last bullet there, what are we carrying over is the learnings from last 10 years of building stuff on applications on cloud. Can we import those principles today? How a container is built, how it's run, it's pretty simple, right? People understand that technology. Can I use the same way of actually offering them edge, right? And if we look at those technologies which are people are getting more used to as the time passes, there needs to be some way for people to first orchestrate these things, in a meaningful way, right? And then to discover them, right? So then the quality of experience constructs to become even more important when we say something is near, does it necessarily mean it's better? Probably not, right? So then the discovery based on a quality of experience happening at the end device becomes more fundamental, right? For service providers own edge locations, right? Plus also offering them as a viable entity to the market. So we discussed a little bit about how the typical deployment should work in the paper. And then we discussed about these design principles which will exist. And then we talk about a little bit how discovery works and what APIs would look like in that fashion, right? Towards the end, we also talk a little bit go deeper in inside the mobility principles because these devices may or may not be static as we know we all move around. So how do these things actually work? Because today, relatively when I connect to a Netflix cache, it's sitting somewhere in my East Coast, right? Where I live, and I download the movie, right? And I watch it. But like say I'm moving around and I have an immersive experience going on which is a little insensitive too. How would I move this container around in these geographies where telco's edges is? Because it'll be much more distributed than what a Netflix cache is today, right? So those things will be becoming more and more material. So these new things will evolve, but nothing radical will change. We'll keep on learning from the design principles which have been very successful and passed and keep on evolving that structure, right? So that's what we basically discuss in the service provider section. And some of the work we're doing at Mobiology is essentially that, right? We're trying to help boot up that space for a meaningful consumption, right? Yeah, so that's a good lead into the next question here which is, you know, LF Edge has a number of member projects within it that span this continuum. So Jason, can you maybe help us understand how the projects fit into this continuum? Yeah, so, you know, the focus of the projects tend to kind of lay out across the landscape and there's currently nine projects within LF Edge. You know, LF Edge, as you described, you know, it's about how do we harmonize a more of a common open foundation for edge computing and how do we get to where we can make these workloads more transportable across, you know, that spectrum. And so if you look at the projects, Acreno was one of the anchor projects. Acreno is a little different because it's about building blueprints on how do you take both upstream and downstream projects? Of course, you know, the ones within LF Edge as well and create, you know, best practices around deployment for, you know, aligned with different use cases, different kind of horizontal and vertical use cases. So Acreno sort of spans the spectrum. And then you'll also notice there's a variety of projects that tend to align to some of the nuances of, you know, the user edge, for example, and they span from infrastructure up to application layer. So there's that kind of common, you know, foundation that we're looking to build and then there's tools around how do we enable developers to build applications more effectively and you know, have more of that common framework and then they can focus on innovation rather than reinventing the base. And so if you look at the infrastructure side, Secure Device Onboard is a new project contributed by Intel. That's like, that's all about how do I simplify bootstrapping devices out in the field and how do I assign owners to devices at the end of a multi-party supply channel?  It's based off of what's now a fifth standard from a security, you know, that security organization. And so the idea is provide code that makes it easy to go, you know, embed into your products and enable secure onboarding within the channel. You know, because if a company ships a product it's gonna go through multiple different hands before it gets to an end user and making sure that you bind that identity is important. You know, Eve as a project is around how do I build a secure OS for edge computing when I'm not in the side of a data center? So that's kind of going along that inflection point between stuff that's in a secure data center versus stuff in the wild. It's focused on IoT edge computing. You know, think of Eve is due for the IoT edge compute nodes what Android did for mobile. And so create more of a universal open OS and a lot of different features within that. Open horizon is about how do I go for the haters, you know, anywhere and you know, both on the, from a controller standpoint and you know, an agent that enables that, you know, Beatles got, you know, some of that element but then also how do I tie that into cloud services? You know, that was initially contributed by, by due. Home edge is, you know, package all this up and let me position that into the home into consumer applications. You know, how do I create like a home server that delivers different experiences around segregating video within the home, you know, AI for natural language processing, et cetera, you know, tying all these services together and then Fledge and edgex are all about the application plane. How do I create all, merge all of this fragmented data from devices out in the field, you know, the closer you further you get to the left on this equation, the more you get into many, many different protocols for connectivity. There's 10s that matter on the right, there's thousands, you know, on the left when you get into proprietary. And so both Fledge and edgex are focused on, you know, that bridge between, you know, IOT data and creating more, you know, normalized data on the top end, you know, to feed into other services, feed into the service provider edge. You know, all of the projects have kind of a core focus. There is some overlap between the projects, but the whole point between LF edge is bring, be inclusive, bring projects in and seek to harmonize over time. You know, look where it matters to, you know, to harmonize and make more common foundations, but there's also necessary trade-offs. I mean, you know, Fledges is focused on, you know, use cases is very focused on industrials, not to say that edgex is more general purpose, also focused on industrial, but there's necessary trade-offs in terms of high performance, which is a bit more on the Fledge focus, high streaming data, but then you lose some of the portability, some of the flexibility in terms of architecture. Edgex is focused on high flexibility, you know, very, very granular microservices and they can kind of mix and match based on need. And so that's, there's just different approaches across the projects and, you know, we definitely, you think it's important to come at it from different angles and see how things can bridge over time. So the other thing that I would mention, I know this is near and dear to your heart is State of the Edge.  Yeah, sure. I actually think we have a slide, yeah, maybe the next one. So as I mentioned in, at the beginning, I chair the State of the Edge Landscape Working Group and this is a project for anyone familiar with the CNCF landscape, which most people I think in this audience will be. This kind of echoes the efforts there and focused on the Edge ecosystem. So this is a database driven framework where we position relevant projects, technologies, operators within the Edge ecosystem and it's an ongoing effort. If you'd like to get involved, we can provide the resources for that as a follow-up through LF Edge. But yeah, this is, it is, as you said, near and dear to my heart and would love some more contribution stuff. So maybe moving on, Vikram, you know, based on where you guys are currently operating, what you see, where these projects fit into the continuum, what are you seeing as the major trends in Edge computing? So this is a broad question, Molly, but I can answer some of the questions. What do we see coming through these are the use cases, right? And what people are investing money in? So from our perspective, we see a lot of things are driven now based on automation structures, based on ML driven structures. So you will see that trend continue to grow. It is something which is natural for people to optimize around thanks to pandemic, now those digitization efforts have accelerated even harder because people cannot be together. So people are doing this at a rapid pace. The need to upload all this content and consume it in a closed loop is actually now big driver for most of the things we are seeing in the commercial sites, right? And if you look at any package with 5G and emerging connectivity technologies, I think it becomes a powerful story that you could actually offer somebody the privacy they need, the latency sensitivity they have and get the burden of installing the gas servers in their facility to actually moving nearby somewhere, right? Whether it's a Google cache or whether it's somebody else's cache, it's a material, but it'll save them a lot of headache because now they have a secure line to connect to those servers, right? And it's actually providing them the same latency which they will probably go to the factory for. But the consumption is actually driven by the application space, right? So it's actually truly about what particular use cases will hit the market first. And when we see that need and the digitization need the automation structures, we are seeing ML pipelines being more and more robust, right? That distribution is putting robust in our scheme. Also the gaming industry has come along a long way because people are playing more games now because they're at home and they want immersive experiences and they are sitting in their living room. And that demands something, even on the fixed side, right? Something which is nearby to serve that uplink content in a way that's reactive, right? So you see a lot of the accelerated devices on these things, right? Which kind of consumer hardware, it's very hard to buy, right? But the subscription models are evolving where people just buy that like Netflix content, right? And it served our Google cache or somebody else. So we would see more and more of these trends and more and more investments coming in from service provider side to actually take on more and more slice of that value chain whether it's gaming or whether it's a Nepal pipeline from an industry. So they'll become part of that value chain system which they are not, right? So that's what we are seeing at least from our perspective. We would love to hear more because we're not in every business but like what people are seeing on the user side and device side. And we would like people to actually tell us more about what they would like to see in a paper as well as reflecting that trend, right? So maybe Jason, I'll throw that same question to you. Where do you see the edge heading? Yeah, so I mean, first it's clearing up the confusion. I mean, that's what we're of course, you're working to do with taxonomy and get people to think about it more holistically and rather than this kind of singular vision from the way that you're approaching it. You know, we wanna see this transportability of applications. You know, I think we're gonna see more and more of that over time as people realize the different paradigms. Advanced class to me is doing the analytics of the analytics so that you actually kind of look at based on where I'm running a workload, you know, am I getting the best cost to performance benefit or do I need to run? Maybe I'd move it up or down the stack a little bit. We're gonna see more and more compute happening in constrained devices. So tiny ML is a major trend that's popping up.  Tend to be fixed function. The Hey Alexa for your smart speaker is a great example of that.  But so we're gonna see more there. So it's, again, it's about that continuum. You know, a lot of the IT players, you know, come from that background when they look at it, all compute's gonna happen in the data center on heavy servers and all that. It's like, no, no, it's actually gonna be happening, you know, more and more across the spectrum. So I think that's a big trend. When it comes to AI, so Vikram, you talked about, you know, AI and machine learning and some of those trends, anything related to computer vision, we're gonna see more and more happening at the user edge, you know, on-prem. You know, of course, when you're in a vehicle or, you know, with a mobile device, inherently it needs to hit the service provider edge. But if you have lots of streaming data, whether it's video vibration analysis, so high bandwidth vibration data, that's gonna want to tend to move closer and closer to the source because it, and then you run your models there and do analytics and then only push events to the backend. It's very offensive to move streaming data around if you don't need to. So that kind of gets into the, you know, that definition of edge necessary and feasible. So we're gonna see those trends. The other big thing that I think we're gonna see is, you know, with, you know, I mean, 5G is such a broad topic, but this notion of private 5G and CBRS and sort of new bands opening, I think not only is 5G gonna enable new experiences, but we're gonna see a lot of business model changes. You know, the ability to kind of build your own networks, drive new experiences around it will change, you know, kind of who's doing the work, who's offering that business beyond kind of a traditional telco. Obviously it's a big opportunity for telcos as well, but it's gonna change like the landscape of who's delivering networks. And then that goes beyond just kind of the IT sense, but also into people that are used to the industrial world. You're seeing more and more industrial providers starting to either build their own networks or starting to partner with telcos because they bring the expertise out in the field. You know, how do I not optimize a network for a factory floor?  So we're just gonna see in terms of general trends, not only workloads and creative distributed, but more collaboration across different people in the ecosystem by necessity because we're just seeing this mashup of services and applications and whatnot. So I think that's gonna be a key part of it. And also, you know, a perfect example of why we need more of an open common foundation, why you need transparency in that way. Because it's just gonna get more and more complex. The real long-term is starting interconnecting different ecosystems and driving entirely new business models, and that's not gonna happen without some sort of open foundation. Right, right. And I know the community is planning to do a follow-on white paper. How does one provide feedback or get involved in that project? Vikram, you wanna take that one? Sure, so just to finish off what Jason said, as you saw, like on the user edge, he was also saying there would be a lot of ML and a lot of transactions like that and there would be privacy-centric moments where the streaming is so high that they probably don't want to use a cloud. And that's where I think the beauty of this paper sits and that's where the collaboration sits in. Whether the inference transactions are gonna happen on an end device or a training is gonna happen in a private colo, these are the things which will evolve into the next phase. But if you look at the basis of these things and if we agree on basic terminology and technology for that matter, if it's just gonna be a container who's gonna run an inference engine on the device, it's the same technology which is gonna be a training container running in the back end somewhere. So then the life becomes much simpler for people at least like us who can collaborate across somebody supplying an IoT ML, a use case. How do we take that ML use case and do the useful work which is needed on a service provider edge rather than actually trying to reinvent the same things across these verticals. So just to wrap things what Jason had said, I think it is very important, the same drivers which we are seeing in the market for ML, for tiny ML, or whether it's a tensor for transactions, the same drivers I think Jason is seeing in his market. And if you see that common pattern, I think all we need is harmonization around that. So the best way for us to work together is participating in these conversations which we write. We don't want to write only our opinions on these papers, we want to collect from the market, what do they really think they need to write. And a lot of companies are developing solutions around each of these four categories which we highlighted and the interleaving is happening. So my guess is if we see the next phase of this paper developing, you would like more participation, there's a tech architecture working group which everybody can sign up, right? It's awesome, it's actually mentioned there. So if you go below this link, you can actually subscribe to our weekly meetings. And then we actually set up an agenda and we deliver a paper in few months, right? So which comprehensively addresses all these things which we are talking about. But now I think we want to move on from addressing edge continuum, basic terminology to actually solving some of the use cases and how we actually interleave across these layers of edge continuum we are talking about, right? And that's what we would love to see how is an output from this paper. And if somebody has any thoughts, opinions, please join this working group and we'll be more than happy to work together. Yeah, that's great. And maybe Jason, any other closing thoughts there? Yeah, I know, I think it's all about getting the community consensus. Obviously we wanted to establish a baseline with this taxonomy first, kind of frames the conversation. But as Vikram said, the next version of the paper which we're spinning up now is getting into more, a bit more of the technical side of things, more of the architecture, more on the use case side and some of the unique trade offs as you span this continuum. And we're just looking for feedback. So I think that this, getting on the same page in terms of terminology, the work that you're doing in state of the edge, important. We're definitely seeing it improve. The next big thing is of course use cases, practical application.  All new market trends tend to start with people talking about the technology, start talking about, hey, buy my platform. What am I supposed to do with it? Well, I don't know, just buy my platform. And when you know when something starts to mature, when people start getting into real use cases and practical implementation of it. So that's the goal here is just to demystify things even further and just get people involved in participating. So definitely encourage that across the community and the market. Yeah, certainly. Well, thank you both for the overview and the insights today. I think that wraps up our session for today. Again, if you're looking to get involved with the next phase of the next white paper coming out, the link is listed there on that slide. And thanks again to everyone for joining. And I hope you have a great day and a great conference. Thanks, Molly. Thank you. Thanks, Margaret.


Can you just describe what what today was like Aaron Boone called you do you have any inkling that you might be starting tonight? there was a little bit of We had a little bit of information last night that said he was kind of feeling down on the weather and that it was gonna be a Bullpen game, but I had no clue that I was starting until Probably around like one or two today So I think we you know it's hard to prepare for something like that It's kind of just like I treated it almost like getting called from the bullpen and just coming in the game Is it easy to do it that way? Yeah, I think that was kind of the best way to go about it I think my mentality was I want to kind of Set the tone early and get some zeros on the board and kind of put the bullpen in a good spot And I was trying to go for but I think that Mahia AB kind of took me out of it But yeah, I felt like that was kind of the best mentality to have was just going into it as my name's called In a six or seventh inning and just go out there and get some outs How much confidence can you gain from an outing like this where you make the start and successfully navigate yourself through three? Yeah, I think a ton. I think I'm just in such a good spot right now like mentally. I feel like Every time I go out there or whenever my name is called I feel like I'm just Just very comfortable and I think last year and in years past It was almost like, you know You almost put too much pressure on things and you try to force things to happen and kind of right now I just feel like I'm playing baseball and having a lot of fun doing it And so I'm just going to continue to keep doing that


Hello! Welcome to Database of the Week. I'm Todd Quinn at the University of Mexico University Libraries. This week we're covering stage research methods. This database provides various types of books and articles that cover methodologies for qualitative and quantitative research. You can browse by topic area or disciplines. You can look at the books and references and one of the things you'll notice in here if you scroll down a little bit, the books and references are broken up in different areas and there's two areas called the little green books. These are books specifically on quantitative research and then the little blue books which are very specific on qualitative research. Both these sets have been around since the 70s. They provide all kinds of information about various methodologies. You can also do a search on a methodology. I'm just going to say interviews. Then when I go through here, I can minimize my results by using any of the tools on the left side. I can select a title. You can also notice where is this methodologies under the title. So if you wanted to say qualitative interviewing, I'll select that. It's only going to find me titles that meet that criteria. So instead of 980, now I have just 41. And here we are getting some videos and I can go back up and say I really want to look at the books. I'll say all books. Apply filter. Now I'm just going to maybe have to British at it. Let's go with conducting research interviews. And then when I select the title, you can look at the summary of it. You can look at the table of contents and the subject list. You may want to create a profile so then you can save items if you want to come back over and over again. If you look at tools, one of the things is you can see how methodologies are mapped together. It's sort of in a line or overlapping. Project planning can be very useful for graduate students so you can walk through the steps of a research project, among other things here. So if you're looking for books on methodologies, this is the place to start.


What happens after people endure distress and anguish in the midst of conflict? When do they recover from the effects of violence? Is it once they've been taken to a safe zone? When they have adequate food and water? Meeting physical needs is an important aspect of recovery, but it's only one part. Without addressing mental health and psychosocial needs, recovery isn't complete. The effects of horrific events can last long after the violence ends. These events can live on in their minds and interrupt every aspect of life. They might be physically present, but often these health issues make it impossible to feel fully present in daily activities. Sleep becomes more difficult. Relationships with loved ones become strained. If mental health and psychosocial needs aren't met, people can even turn to substance abuse or self-harm as a means of coping. Treating these needs isn't a nice to have. It has the power to save lives. That's why, in addition to meeting physical needs, we at the International Committee of the Red Cross provide mental health and psychosocial support to all victims of violence, because we believe that recovery isn't possible without it. Whether their struggles are due to trauma they endured while being detained, emotional distress because of a loved one who went missing, whether they're a victim of violence, including sexual violence, are wounded or disabled, have been affected by emergencies, or are the ones providing support despite being affected themselves. Our mission is to ensure that people can draw on resources they already have or identify additional support needed to recover. Sometimes this looks like doing in-home visits, raising awareness with communities to de-stigmatize certain issues, or peer group support, where people can connect with others who are experiencing similar struggles. If the resources and support systems don't exist in their area, we'll help develop them. The right support allows people to move forward. We want to make that a reality for as many people as possible. Mental health and psychosocial support is essential for recovery. Learn more at ICRC.org


Good afternoon and all good morning and welcome to the first of these analyst debate webinars between myself and my colleague in Toronto, Canada, Colin Szczewzynski. I think one of the reasons Colin and I decided to do one of these debates is I think first and foremost they work better when you can interact with the other person. It's also I think an easy way to invite debate between you the client and us the analysts and have a general discussion about conditions in the market, trading set ups and what have you. But before we really get cracking on this I just need to make you aware of this risk warning. A general disclaimer so that anything that you hear on this webinar is not trading advice. Really it's just a debate to sort of talk about ideas as to what's going on in the markets and you know potential trading set ups as to where we're going to go to next. So let's just quickly get rid of that and now we can crack on. So welcome everybody. As I say, my name is Michael Houston. Most of you probably know me already and Colin, my colleague is also on the line as well. Hi everyone, thank you all for joining us today. Okay so Colin, we're a little bit offline about what equity markets are doing and whether or not we thought that they were starting to top out. And it does seem to be some evidence that that might well be the case. However the economic data that we're seeing out of the US at the moment still continues to give conflicting signals. I mean that industrial production data that we saw earlier today was absolutely horrible and yet the weekly jobless claims were fairly good. So I think we're still in two minds with respect to what the Fed are going to do or continue to do and we've obviously got the Fed minutes next week as well. So what are your thoughts on the S&P and the Dow? I think at this point in time we're seeing that the S&P and the Dow are both totally exhausted. They've gone, they've been going consistently up since the fall of 2012. They haven't had any major correction in a serious way in a long, long time. It's all been propelled by two things really, the recovering economy in the US. But the bigger one has just been this steady inflow of QE money finding its way into the stock market. When you put that kind of money into an economy it's a lot faster to lend it and put it to work in the market than it is to lend it and put it to work in the regular main street economy. So we've had this huge run in the markets here and things are just looking tired. The Fed has started tapering the seasonal inflows of capital from people putting money into retirement accounts, the tax deadlines have all passed in the UK and the US and Canada and elsewhere. So that spring winter flood of money is starting to dry up and markets are just looking really tired here. We had a new all-time high for the Dow industrials. It was confirmed by the transports which should have been fantastic and the stock market should have been going up 100 points a day instead of down 100 points a day the last two days. So we're really looking at an exhausted market here. Good news is not pushing it much higher and it looks like it could be due for a bit of a seasonal correction here. Now you were talking to me earlier about the NASDAQ and obviously the Russell 2000 as well. And we've made new highs on the S&P and the Dow. But if you're looking sort of across markets those two indexes are actually already rolled over. They both have. The Russell 2000 has been trending lower for quite a bit of time and where it looks like we're seeing a head and shoulders top in the NASDAQ. Absolutely. So I want to talk about the NASDAQ first and what's most significant here is. Do you want me to give you control? Yes, please. I will give you control. There you go. Okay. And I will take control. Okay. So if we look here at the NASDAQ it had been in quite a long uptrend here. And we're seeing what's really a classic head and shoulders top. If we look here we've got a left shoulder back in January. The head of the pattern at the beginning of March. And this kind of, we really had more of a rolling right shoulder here where you've almost got to triple three times where you've hit the same resistance level. It's just not working. And this is in around, I'm sorry, I'm just going to get my cross hairs. The cross hairs at bottom, down the bottom. Ah, thank you very much. There we go. So I'm just going to bring my cross hairs open here so that this resistance level here around 3,600, 3,625, 3,630, it keeps bumping that and it's just not going through. So the left shoulder, right shoulder, a similar type of level, 3,630 here. And what are we getting back today? Again, NASDAQ's rolling back under 3,600 today. This is important because what you've got in the NASDAQ is a lot of high momentum, high value companies. You're seeing a definite over the course of this year, a definite contraction in valuations. People aren't paying up as much for high growth companies as they were willing to do last fall. So what are we seeing? We're seeing the pullbacks in Facebook, in Twitter, in Tesla. I'm just going to feel that control from you. Okay, go right ahead. Carry it, go on. I'm going to put this on the chart forums, ladies and gents, so that you can see it. Colin, you carry on. Okay, great. And so all of these momentum names and even things like Bitcoin, remember you hit $1,000 a few months ago, it's now down closer to $500. I mean, all these things that had gotten really, really frothy are starting to pull back and that's suggesting that it's indicative that the hot money that was going into the hot plays, there's not as much of that anymore and they're starting to fall under its own weight. And the markets themselves falling under their own weight and you're seeing it in the breadth as well that the mid caps, the small caps that are higher weighting within the Russell 2000 are starting to go down. And all you've got left is the narrowest of indices like the industrials and the transports which have, I think, 30 and 20 stocks respectively. And it's only the narrowest indices that we're making new highs and the broader indices they're rolling over is generally indicative of that we're at a turning point in the market. And I think also another clue that markets or investors are conflicted with respect to the direction of equity markets at the moment is the performance in the bond markets because the bond markets are going extremely well bid. And I'm going to show you my 10-year note chart here and I think that's very instructive because for quite some time we've been trading below the 200-day moving average and yields have actually been going up now. We've broken away and we've aggressively broken away and that suggests to me that we're probably going to see 2.4 or even 2.3 on the 10-year. Now if that is the case and we see higher bond prices then we're going to see a significant move lower I think in Dolly N. We saw overnight that those GDP numbers for Japan were very, very good and you would ordinarily think that well, yeah, Arbonomics is working happy days and that should then give the Nikaya boost but it's had the exact opposite effect. Why is that? Well, I think the GDP boost was largely as a result of a lot of people or a lot of consumers basically front running the retail sales tax hike. So they brought forward an awful lot of their consumer spending and I think the fact that the GDP number is so good it means that the bank of Japan won't do any more easing and that is going to be yen positive dollar negative. So I think that's why you're also seeing the breakout towards the upside in U.S. Treasury prices and as a result you're seeing yields come lower as well. But I think there's also a broader question there. It's the fear of deflation in not only in Europe but also across the globe and I think that's what's also driving yields lower not only in the U.S. but also the U.K. and pretty much Europe as well. And what's your take on that Colin? Yeah, I think you're absolutely right. That's why we're seeing the ECB certainly talking about more stimulus going forward. I think it's as much driven by the fear of deflation as the sluggish economy and trying to get things moving there. I mean the Europe has had a sluggish economy for quite some time but the ECB has basically been letting it kind of work itself out and only now they're starting to talk about getting more aggressive and others so I think it's the deflation worry and that's why I think we're seeing the money starting to flow back more recently again out of the high and frothy stock markets back into some of the more defensive markets that had come down lately. So you're seeing U.S. bonds starting to pick up. We've seen gold is having up and down days but it at least looks like it's kind of stabilizing and certainly interest coming back into the yen as well. And also Canada is starting to look a little bit better supported despite some of the what I should say choppy nature of the jobs data that we've seen out of North America. I think one of the things I've noticed about the Canadian jobs data is how different it has been from U.S. jobs data and we've got a nice little breakout here on the EuroCAD. I don't know whether you guys have been watching this but this is something that I posted I think a couple of days ago and actually talked about on my weekly video this head and shoulders reversal that we've broken out of and basically this is a very simple technique that I use for measuring a target for the head and shoulders reversal. So what I'll do is first and foremost I'll identify it. One of the ways I can do that is using pattern recognition and when I use pattern recognition on Euro Canada what I was able to do or what it was able to do was it was able to identify that pattern for me so I was able to see it right there once it scanned the chart. What I was then able to do is by using my Fibonacci projection Fibonacci price projections option here I basically select this high here and I basically project down to the base and then project lower from the breakout point and that gives me my targets. So my target for this head and shoulders breakout now is around about $145.50. So as long as we can push below this series of lows in February which I will just now get rid of this. There we go. This series of lows in February which is around about $149.00 If we look at the chart forums again, there it is. Okay so this was something I updated on the 13th on the Monday so we need to break below $149.00 to confirm a move towards the 200-day moving average and this trend line support which if I now zoom it out all the way back to 2012 basically comes in round about the same level. So that's a really nice head and shoulders that's sort of been borne out by this move lower in the Euro and certainly this morning's GDP numbers out of the Euro area Germany notwithstanding would seem to suggest we're going to get something from the ECB. The only really debate really surrounds us as to what we're going to see. I don't think we'll see any QE but I certainly think we'll see a movement on rates. What's your view on that? I think you're right. Can I take control back again for a second Michael? You can indeed. Let me just assign that back to you. One thing that's important and that Michael's highlighted here and I think it's important for us to note as traders is that whenever you're looking at a pattern or at a chart the more things that come together to suggest that something could happen the greater the level of confidence. So in this case for example when you're looking at projecting a price where you have a Fibonacci and a trend line coming in closer together or what I call clustering when you start to see a number of things pointing to a certain area markets can be psychologically drawn to that point so it's quite significant. The other thing I wanted to highlight here was of course we've got the shorter term, we've got the price pattern. The other thing that we have in here is on top of having a head and shoulders top and some shorter term trend line breaks if we look down here at the stochastics we also see that we have a pretty significant and been going on for quite some time a negative divergence. So you've got lower highs in the stochastics and it's been similar in the RSI and meanwhile you had higher highs in the pair and that negative divergence tells you that there's a shift in momentum going on so you've got the negative divergence telling you momentum is changing from upward to downward you've got a massive head and shoulder top which is a big reversal pattern and you start breaking the neckline there so you've got a lot of things are starting to come together to suggest that this trend is changing. Yeah I mean that's absolutely right and that's one of the things that I also look at I also look across asset classes so when you're looking at say the euro it's important to not just look at the euro-dollar but it's also important to look at the crosses as well so if I could just still control back Colin. Absolutely. And just turn that off. What we can do now is let's shut that off and let's look at euro-ozzy because I think euro-ozzy will probably give you a similar sort of shake-up with respect to a top-forming pattern. That looks like it's already even completed. I think that's probably completed and reached out and now we're looking to test that support line from those lows in the middle of 2013 last year. So we've broken below the 200-day moving average and now we're testing this key support line here. Okay so we've looked at that now let's look at euro-dollar because I think there's potential for a bit of a support level coming in round about the 200-day moving average which is round about here and we've held above that low that I identified earlier which is round about 136.40, 136.50. If we also look at the 200-day moving average that comes in at round 136.20. So if you take these two pies here I've drawn it through that peak there we didn't close above there and we closed significantly below there. That was obviously Mr. Draghi as you can see from a very long shadow on the top of the candle there and then draw a line through these lows here then there is a potential double-top formation forming there. So what's giving me pause about this particular one is the fact that the oscillator is very very oversold and we also remain above support of 136.40 and we also remain above the 200-day moving average. So you've got two support zones starting to come into play and if you are short then you really need to be careful about being overly short particularly given the fact that we are at two month lows on euro-dollar. Ask yourself how much of this euro-down move is priced in. Yes we look at Euro-Canada and we can see Euro-Canada has broken out and Euro-Canada could go lower but it could be a Canada move not a Euro move and therefore you could get a bounce in Euro-dollar and Euro-Canada could still go down. So I think it's important to look at that because you have to look at the potential interest rate policies of both central banks as well as obviously the linkages between the two or the different crosses. It's a different story for euro-stirling. We can see that here because we pretty much know that the Bank of England and the European Central Bank are on completely divergent monetary policies so let's tidy this chart up a bit because it's looking a little bit tatty at the moment. We're in a downtrend. I'm going to make sure that I can anchor my trend line onto the top of these highs there and then I can basically draw that through there. So we're trading in a nice little down channel there. We've had a very, very strong rebound yesterday but we've pretty much come all the way back. Now that's not to say that we haven't found a short-term base down here and we can't squeeze back up again. I certainly think there's potential for us to come all the way back to around about 82.20 given the fact that it was quite a big support area through these levels here and when I'm looking at a chart for support and resistance levels this is something that I look at in a significant amount of detail. I look at the peaks and the troughs and I look for what Colin has mentioned earlier, clustering, a clustering of support and resistance levels which essentially tells you where the congestion points are with respect to highs and lows. So if I can basically extend that to the left like that you can see that we also found a little bit of a high and a little bit of a low support there and there and there. So if you've got any positions or short positions in euro sterling then I would certainly look at putting the stop-loss either above 82 or above this trend line resistance which is coming down from the march highs. Is there anything that you'd like to have a look at Colin or talk about? Could you go back to your oise for a second, Michael? I can indeed, yeah. So you want me to see control? Yes, please. I wanted to mention something in terms of trading and confidence. So we had this big run here. Oh, sorry, I'll just wait a second. That's all right, there you go. Coming back. Giving you control back. Thank you very much. So we had again another head and shoulders top pattern in the euro oise and what I wanted to note here for traders to consider is that when you see a pattern forming there's three different times that you can put on a trade. You could either go in ahead of time and say, okay, well this looks like a head and shoulders top. To me, here's a left shoulder back in December. Here's a head back in January and then I'm sitting here in March and gee, this looks like a right shoulder forming to me. Maybe it's time to go short. The second time where somebody might consider putting on a trade is on the breakdown. This neckline here, which you broke quite decisively right at the end of March. And the third time is on the retest. So you saw that it came down here. You had a bit of follow through. Then you came back and this is about 140, 149, 00.  You've retested, which is also interesting because it's a nice big round number, another part of clustering. So you've got 150 here. You've come back, you've retested it and look at this. Now you're down and now you're into new lows again and your downtrend is actually accelerating. So what happens, obviously, clearly, is that your measured move from your pattern would stay the same. So the difference between whether you go ahead of the breakout on the breakout or on the retest is your return potential to the same point is lower.  So if you went short at 155 or at 150 or waited in maybe 149, 50, so your return potential to the target is a little bit lower, but your confidence is higher. You're in a right shoulder. You don't know if it's going to be completed or not. You're on the breakout. Well, you've seen a breakout, but you've given up that it's improved your confidence because you've had the breakdown, but it's decreased your return potential a little bit. And then on the retest, in this particular case, we got one, the risk of waiting for retest is that you don't always get a retest, and sometimes the trade gets away on you. But certainly in the markets, there's always another trade coming, but that's an interesting... It's a tradeoff there that's worthwhile for people to consider when you're looking at patterns. I think that's absolutely right. I think you hit the nail on the head there, Colin. You know, I think it's easy to sometimes jump in because you're worried about missing the move, but I think it's always better to be cautious and make the mistake of jumping in too early and then finding that you can't run the stop as much as you'd like and actually getting stopped out prematurely. I always work on the basis, let the market come to you because there'll always be another trading opportunity. And they're like London buses. We have an old joke here in the UK. You don't see a London bus for about 20 or 30 minutes and suddenly three of them come along at the same time. It never fails, it's the same here. You know, trading is a bit like that. Sometimes the best position to have is no position at all and then suddenly you'll see three great trading opportunities and then you'll spoil for choice. So I think it's always important to remember that just because you can't see a trading opportunity right now doesn't necessarily mean that you're not going to see some trading opportunities in the near future. Okay Colin, was there anything else that you wanted to look at? Shall we look at gold? Sure, let's take a look at gold. That's always a nice old favourite. That's been extremely choppy today. I'm going to look at that daily chart there but before I do that, let's just pull this to one side and we'll also do an ordinary chart. Just do an ordinary chart like that and then we'll do slightly shorter term. There we go. As we can see from that, we've been a bit choppy over the last four hours but for me, this longer term gold chart tells me that at the moment we're in a massive consolidation mode. If we look at these very, very long shadows on the daily candles, we can see here that there's certainly an awful lot of what I would call pent-up demand down there because a long shadow on a candle tells me that the markets are very, very worried about being overly short. On the top side, we've also got the 50-day moving average acting as a significant resistance level. So let's go and basically stick this in here and here and now there's a potential trading opportunity. They're on the top side. We've got resistance just below 13.10. We've also got the 50-day moving average so we've got a very, very small window there and we can see that on the left-hand side, 13.11, around 13.15. We need this to break out, I think, probably within the next day or so. Don't we, Colin? Because I think if it moves into the apex here, it'll generally peter out. The thing with triangles, and I like triangles, is you're really looking to trade the break because they're impulsive moves. What we don't want to have happen is for this move to just basically peter out into the apex. Now the apex is all the way over in June. So I think if we're going to break out in gold, we either break to the upside and the way to then project the target for gold is again use the Fibonacci projections option here, the Fibonacci price. You basically take the move here, which is the horizontal move and that's the base of your triangle and you then project up like that or you project down the other way. So if this breaks out to the top side, then our target, our minimum price objective, is $1364, $1,364. In a simple measuring technique, you basically take the base of the triangle and you project it upwards from the breakout point. If it breaks to the downside, you do the exact opposite. So let's do this the other way around. Let's project it downwards. So rather than going from the bottom to the top, let's go from the top to the bottom. We'll do that and then we'll do that there. So if it breaks to the downside, then we're looking at $1226 as the minimum price objective and that's essentially how you play triangles, but a triangle needs to break out between 50% and 75% of where its apex is. So really this triangle needs to break out within the next day or so, otherwise what we're going to continue to do is essentially trade sideways. There's certainly a significant amount of support on the gold price around about this $1270, $1265 area. I'd like to think that we're getting ready for a move higher or lower. The oscillator or the slow stochastic would appear to indicate that we're fairly neutral and it's probably more likely that we're just going to trade into the apex and continue with the current range. But it's certainly worth something keeping an eye on and if we do close above the 50-day moving average, I would like to think we could have some potential to move higher because I think if anyone's looking at that chart and they're short of gold, they probably put their stop-loss above the previous size of this month in May, which are around about $1,320. Yes, indeed. I think what we're seeing here in gold is we're definitely in a range-bound market which also is important for when you are trading and looking to set stop-losses is that you need to consider that rather than looking at this chart and thinking, you know, gee, can gold take a run back up at $13.90? It's adjusting the expectations that if we do get a break here, well, maybe we'll get a retest of $13.30 and because gold is kind of range-bound, that's what we need to be prepared to settle for and in which case then what's your downside. So say if you're here and you're measuring and if you were looking at a current price on gold sitting right on $1,300, your initial resistance is up here around $13.15 and then up here around $13.30. So where are we within our trading range? Well, we've got a low here around $12.70 and our high is around $13.30. So if we're just going to be within our range here on $13.00, it's a $50.50 proposition. It's $30.00 up. It's $30.00 down within this range. So then we ask ourselves, well, what would make things a little bit more interesting? So let's just... I'm just going to drop these lines on here so we can see the channel. So there it is there. Now what makes this a little bit attractive? Well, one thing we do have this nice trend here of higher lows, but it's offset by the trend of lower highs. If you get a breakout, then you're likely you've got a pretty good chance at the top of the triangle. If you don't get a breakout and you stay here underneath that $13.00, your first support around this trend line is about $12.80 and then you've got more around $12.70. So it's a matter of... in this particular case when you're playing this kind of market, it's shortening your time horizons and shortening your objectives. Is there a smaller trade within this that might work as well for people to consider? There's always opportunities. It's just that sometimes the opportunities are bigger than others. Alright, cheers. Thanks Colin. So we've looked at gold, so we think there's probably potential for a little bit more upside potentially in that, but overall I think we're both agreed it's probably going to continue to trade in the range that it's been trading in for about the past five or six weeks. Let's have a quick look at sterling because we haven't really had a look at that yet and we've got the Bank of England minutes next week. Can I say one more thing on gold, Michael? Sure, yeah absolutely. Do you want to control that? No, I just wanted to say... if you're at 1,300 it's a 50-50 proposition, but if you figure that this trading range is going to continue for some time, if you drop back to 1,280 and you're in a 1,270 to 1,330 trading channel, your odds change dramatically. If say you go back to 1,280, you've got $10 downside to 1,270, you've got $50 upside to 1,330. The trade then starts to look a lot more interesting from the long side. Similarly, if you took a run at 1,320, 1,330 it may look more interesting from the short side. Anyway, sorry Michael, please continue. No, that's fine, that's no problem. So let's look at cable and we can see from this cable chart that despite the fact that the Euro dollar is starting to look a little bit heavy and equity markets are looking fairly heavy, cable continues to look fairly resilient. It's finding support. We did break below this trend line support from the March lows earlier this week. We are now trading below that, but we are finding a significant area of support around about the 50 day or just above the 50 day moving average, which is around about 1,6720. In my morning comment this morning, and actually in my morning comment for about the past couple of days, I have identified 1,6720 as a bit of a level and that's available on the blog. I tweeted out around about 6.30 in the morning of the major currency pairs, sterling Euro dollar, yen and cable. I talk about the major support and resistance levels to give an indication of direction and certainly against the dollar, the pound has held up much the better of say the example to Euro. But we are approaching a very, very key support level. So unlike Euro dollar, which is broken below this trend line support, the pound hasn't and we've got trend line support from the November lows currently coming in around about current levels of 1,6710, 1,6720. So that's certainly worth keeping an eye on. Certainly on the daily charts, even though the direction of travel does appear to suggest that we could well be looking to head lower. But I think a lot will depend on next week's minutes because I think that we're going to see some descent between policymakers about when to raise interest rates. Certainly I think the unemployment rate that we saw, the unemployment data that we saw out this week speaks to a direction of travel, 6.8%. Average earnings was slightly disappointing and I think that's really what drove the pound down on Wednesday. And I think more than anything, we've got CPI next week. I think more than anything, the Bank of England is not targeting CPI anymore. It's targeting average earnings growth and I think they won't be too bothered if CPI falls back to 1.4, 1.3, 1.2. I think they will be concerned if average earnings starts to push too far away from CPI. So if we go above 2%, we go to 2.5 and we go to 3%, I think you'll see concerns rising that maybe interest rates may come sooner rather than later. But for the moment, the odds are, given what we're seeing at the moment and given what Carney said yesterday, we'll probably see it after the election next year, which is probably entirely what the coalition government really want to happen. I don't think they want to see a rate rise before the election. Okay, what I'm going to do now, guys, is I'm going to wrap up the recording for this week. Wind it up. You're more than welcome. We're going to carry on talking if you want us to. If there's anything that you want us to cover, any market that you want us to cover, use the chat feature right now. We're going to carry on, but we're going to stop recording simply because the file size is too big and then it becomes too big to upload to YouTube after we've done it. So for the purposes of the recording, thank you very much for your attendance until the same time or a similar time next month. We'll definitely do this again. Thank you all very much for joining us today.


The three key tools to keep your TEFL class managed. The way a teacher manages the classroom has a profound impact on the success of the class, and the ability for students to achieve their learning potential. Successful class management is made up of multiple different facets, that all work together to achieve a positive learning environment. When executed correctly the use of body language, the way the class is arranged and the maintenance of discipline work together to create a successful class. 1. Body language. 2. Grouping. 3. Discipline. Effective management of the classroom is one of the most important factors in a teacher being successful with a class. Through the use of body language, class grouping, and discipline, the teachers give themselves the greatest chance of achieving this. An effectively managed class through the use of these methods, provided the best possible learning environment for students, and gives them the chance to maximize their learning potential in the classroom.


Now, location and space requires two or three coordinates, which we can study using vectors. But to simplify the maths a bit, we're going to look at the case when location needs just one coordinate. Once you're comfortable doing the maths with one coordinate, you'll be able to move on to working with more dimensions. And in fact, a single coordinate is enough to keep track of speed along a fixed path, such as a car on the highway, a train on its tracks, or an airplane on a straight flight path. So to describe where a train is along a track, we just have to write down where the centre of mass is at each point in time, x as a function of t. There are a few decisions to make first. We must decide where the zero point along the track is, and maybe also what will be the zero point in time. And then we have to decide which direction along the track is going to be positive. It doesn't matter too much how you choose these things, you just have to be consistent for the whole time you're working on a particular problem. Imagine I record the position of my train as a function of time, and when I plot this information it looks like a straight line. Let's suppose the time axis tick marks are one second each, and the x-axis tick marks are 10 meters. Then we can calculate how fast the train is going as delta x over delta t, and for example we get six metres per second. I can choose a longer time interval, say three seconds, and see that the displacement is 18 metres, so the velocity is still six metres per second. In fact any time interval I choose will give me velocity equals six metres per second. The velocity is constant, and a graph of the velocity as a function of time is just a horizontal line. Now if we think about acceleration, there's no change in velocity, and so the acceleration is zero. Now let's look at a graph of x that is not a straight line. We see from this graph that the train travels 10 kilometres in a 10 minute interval, and we can calculate the average velocity for this trip as 60 kilometres per hour. This is the slope of the line that joins the point at x for time equals zero to x at time equals 10 minutes. If the train had had a constant velocity for those 10 minutes, the x of t curve would look like that red line. Now what further information does the blue curve tell us? If we take a much smaller time interval at the beginning here, there is no change in position, so the velocity is zero. And if we do the same at a later time, we see a steeper slope. These slopes calculated for very small time intervals are called the instantaneous velocities. The lines that just touch the curve here are called tangents to the curve, and this is something you'll study more in calculus.


Thank you very much for this opportunity to share what we're doing the Open SES project with you guys. So let me explain a little about what the Open SES project is about. It's actually a Venus Foundation project that a lot of people don't know about. So that's why I'm here to talk to you guys. And the Soda is actually a rebranding of the project or a change of the transformation of the project from Open SES to Soda. And I'll explain later why we are doing that. And part of the reason is because as the title says, we are going on to the path to data planning. A lot of people may not have heard of FutureWave. So FutureWave is actually in the news every day and CNN and so on. So FutureWave is the US R&D arm of PowerWave. So I'm basing on Silicon Valley, the US R&D arm of PowerWave. So if you look at this picture, this shows all the kinds of popular applications that everybody runs nowadays. Big data, IoT, machine learning, and AI. And the data that you have usually is either stored on premise or in the cloud. And on the right hand side, your applications may be running on containers, virtualization, cloud or even edge.   So the problem with this picture is that actually a lot of these solutions that you have in there are spot solutions. So I was giving this same talk in a group called in San Diego. And after this talk, a person from South West Airlines came up to me as Data Solutions Architect. He was saying, this is the exact problem that they're having in South West Airlines. So the problem here is that there are a lot of spot solutions. And the difficulty actually in this picture is not about how you transform the data or how you provide the replication for the data. The problem here is how do you connect all these different pieces of solutions together? Okay, that's the key problem. Okay, so you have all these final solutions. So the division that we are trying to do is sort of is actually to create a seamless integrative framework that connects all these different pieces together. So the data that's on your left-hand side, whether it's stored in a cloud or on a premise, can be protected, can be replicated, can be transformed seamlessly from the applications that run on the right side. So besides that, in order to do that actually, you need things like telemetry, you need things, a standardized way of collecting data for telemetry. Automation and orchestration to move the data through the different flows. Intelligence and AI to make your operations more efficient and multi-cloud to connect to your different AWS cloud, Google cloud and so on. So the vision actually is to allow you to store, run and any data anywhere. And that's what data autonomy is about. So thanks for instance Kubernetes, the CSI, I mean container storage interface. What it tries to do there is actually try to standardize the ways Kubernetes connects to storage. So the problem with this solution or with this standard is that through this standard interface, each storage vendor creates their own kind of a solution. And the solution itself is kind of like a micro ecosystem. You have like your data in vendor A that sits within vendor A and you may be able to replicate data to the cloud in AWS, but the data in the cloud can only be recognized by the same vendor solution. So similarly, I mean vendor B solution, vendor C solution, I mean each is actually still a silo. So it does not solve the problem of trying to actually remove this silo kind of a solution. I mean silo kind of a problem. What Soda tries to do is that it actually sits in between CSI and storage. So it kind of abstracts the storage from the Kubernetes, all these parts and applications. And in a way, besides unifying the orchestration which allows you to actually move data across different kinds of vendor storage, it also unifies the data that's in the cloud. So it's not about each vendor putting its data in the cloud, it's about all the data that you have of premise, no matter who's been, I mean where the data comes from, it goes into this cloud and it can be accessed by anybody. Okay, so that's the unified orchestration part that we try to do there. And besides that, the telemetry, intelligence and multi-cloud that we have in there. So Soda tries to make a mobile for Kubernetes. So this is the framework for Soda. So a lot of projects since here, I mean right now is the biggest project. In the Linux foundation, it's probably the world right now. So everybody's talking about cloud native, Kubernetes, cloud native and so on. But our problem is not just the cloud native. I mean there are a lot of enterprises out there that are still using OpenStack. They're using VMware, using bare metal kind of stuff. So Soda's goal is to be able again to enable data mobility for any kind of applications. So it's a data mobility framework. And this picture itself, what you see is that the blue parts here are actually the projects that have been developed by the OpenSS project. So the OpenSS project, the main mission initially was just to connect the different kinds of storage together to a GSA control plane so that it can connect to different kinds of storage and allows OpenStack Kubernetes and so on to use the different kinds of storage. But again in Soda itself, the goal is actually to enable data mobility. So I mean we are going to be working with creating a project for like the edge to enable data mobility between the H&D premise of the HDD Cloud. The Soda Flake is actually an in-cloud management module. And the data store project, this is something that is in preliminary kind of our discussion. So I'll talk more about it in the next slide. So the data store part, what we are trying to do is actually to create a data kind of solution for data streaming, for data analytics, for machine learning and AI. So the possibility of this came together because in the OpenSS project what we did was that we created a multi-cloud kind of data controller. And China Unicom, which is probably the second biggest telecom company in China. They have an internal project, an internal solution that they use for object storage. It leverages the self-storage backend to allow them to, through a single interface, be able to scale the backend using multiple kind of self-clusters. So China Unicom has donated the project to Soda. And what we are doing right now is to integrate this interface together with the OpenSS multi-cloud data controller project, which is the Gelato project. And with this, we are able to do something that's very useful to our end users. So what it does is that it allows through a single kind of interface be able to let you store your data either on premise or in the cloud. And you can control it through any kind of data policies. And so one of the other things that we're going to do is there's going to be a search engine. There's a company that we are talking to that has a search engine that they are open sourcing. So we're also very happy for them to be joining Soda. But I can't mention that they are the company yet. So with that, I mean it's going to make this whole Soda as a data mobility for any kind of framework. It's going to really make it come true. So the other use cases that we have are like for Yahoo Japan, they have like 52 clusters of OpenStack that they use like the Cinder, the monitor project for the file provisioning. And also they are adding more, I mean for cloud native, they are starting to deploy Kubernetes cluster. And in fact migrating some of the OpenStack clusters to Kubernetes also. So the Soda what it does is that it actually consolidates all these different kinds of different silos in 2.1. And then with this, the different OpenStack or Kubernetes clusters can actually use the kind of storage as a unified pool. And resources can be allocated and reallocated very easily. This multi-cloud use case is by entity communications. The use case is like they use AWS, I mean AWS is a partner of entity communications. And to support that, they develop a cloud controller for AWS to actually move data between their data center to the AWS cloud and AWS cloud to some remote sites. So the problem with that is that then they have the silent partner, Google, and then another partner, Microsoft. So with that, everything that every single cloud partner that came in, they have to do the same thing over and over again. And besides that, what they have to do is also like, if we add up their service, they have to redevelop things for it. So that's why I mean entity communications is actually live review Soda to try to solve this problem. Okay, so all these use cases that I talked about, they are not in production yet. Okay, there are things that we are doing for this end users, but they are being tested but not in production yet. So another use case is Toyota, the data life cycle kind of a need. So what they do is that the autonomous vehicle that they have, I mean the sensor data that they have, they want to move it to the edge. And they need a way of like moving the data between the edge to the data center for primary storage. So at the edge, they do things like high performance kind of computing. Then in the primary storage, they do things like machine learning and AI kind of stuff. And then as the data, and the data that's stored there, they store it for like half a year. And after half a year, they want to move it to like secondary storage. And the secondary storage, I mean, they're going to use it for like long term kind of machine learning and stuff. And this, they're going to store it for like, I can't remember the period but it's about two years if I remember correctly. And then after two years, the data is like too old, so all they need to do is to archive it either to the local storage or to the cloud archive. So and then the data that they have to archive and store, retain over there is actually like a period of like 15 years, which is how long they expected power to last. So this picture, I mean OpenSDS is supported by all these different companies. And as we move from OpenSDS to Soda, because the original goal of OpenSDS again was just to define a control plane for storage. But Soda has grown a lot more than what OpenSDS is doing, and that's why we are doing that, doing the transformation. And most of these companies that you see here will continue on to Soda, but some will not. So the mission of Soda is to foster an ecosystem of open source data management and storage software, again for data autonomy, create a digital platform for cross-products collaboration. Because we see a lot of, part of the problem out there is that there are actually a lot of great open source projects out there, but which is actually playing within themselves and they, for instance, like set this up, if you want to replicate some of the stuff like OpenUMD, it doesn't work at all. So we want to act as an intra-platform to actually foster that kind of collaboration. And ultimately what we want to do is actually to surf out and use this to provide ability and to have solutions as well. The goals of the source project, so it's going to be open in any way. The difference between our project and a lot of other projects out there is that our roadmap is actually driven by our end-users. I mean the requirements come from them and we prioritize the roadmap according to the requirements from the end-users. And really, this part is actually about Soda already. This is a certification program that a lot of our end-users are asking for. So in the existing world, when they want to purchase any kind of storage or data kind of solutions, they need to specify like pages of specs and requirements for the product or the solutions. So what they hope for is like in future when they want to order something, I mean when we get a purchase order, all they need to specify is that it's going to be Soda already. So Soda already is about conformance, it's about compliance, interoperability and so on. And then also we have come up with a specs for like how to certify the service providers and also to trade administrators and developers. This is no different from like any of the other end-users foundation kind of certification program. So this is something that we are hoping to do. And then the governance model, this again is similar to a lot of the end-users foundation projects. The main steering committee is the advisory board, the technical steering committee, the support of our end-users advisory committee and the outreach committee. And then the architecture workgroup that will oversees the collaboration or the design across all the different projects, the quality workgroup that will actually define that Soda-ready program and the different six across the different technologies and industries. And also the regional committees, like in Japan actually we have a really healthy community here. So we do have things for that. So the timeline for the project, I mean we kind of started Open SDS back in 2016. Our growth was not as spectacular as we hoped in like the high-region project and so on. But we do hope that this is a new start for us. So we are having a formation meeting actually tomorrow at the Sony HQ across the street. And then we're going to have Soda Forum which is being held here in room 406. So if you are free you can join us. And then the first release of the Soda, which has been developed since the Open SDS project days is titled. And actually we will have the integration between the S3 multi-cluster self-project by China Income and what Open SDS is doing. I just verified with the CTO of the China Income. This is actually a project that's in production and is currently storing like four petabytes of data. So this is a really reliable kind of opportunity. And in Q1 we'll be releasing our second release of Soda. So we have every quarter there's going to be a release. So Soda Lake is not really a fixed project name yet, but colleagues Soda Lake for now. It will start supporting streaming. And we're going to do a Soda Forum in Amsterdam, QCOM. So and then in Q2 itself the Soda already we expect a specifications draft to come together. And then Q3, the main software release will be, we'll do another forum in Shanghai, QCOM. And then the Soda Edge will come in Q3 itself. And then together with like the search engine, how to support and then the live cycle. So all of it comes together. And then finally at the end of the year we're going to do another forum, Soda Forum in India. And then also in Austin. And the release that we have, I mean the Soda Lake we expect it to be able to support AI and machine learning kind of our applications. So this is the program for the Soda Forum. You'll start tomorrow afternoon at 2.30 here. So if you're interested, you can register there. It may be a bit hard to read but basically slash Soda Forum 19. Just go there. Or just come look for me. You'll be free to get by me. So these are the different speakers. Most of these speakers actually come from companies that we participate in the project. And in the evening, I mean you can join us for the forum do. Come and join us for the evening reception. This starts from 6.5 until 9 o'clock. We have a lot of great food. Like what I was saying, we're going to have a formation meeting tomorrow morning. So this will be held in Sony headquarters. If anybody is interested in like your representing a company to join. Actually, we already have a full house of representatives from over 20 companies. If you're interested, let me know. We'll try to squeeze you in. So this is kind of like the high level kind of agenda. And the high level of this is actually at the end we get a tour, get a tour of the Sony square in the building. So the OpenSS has done a lot of activities around the world, from Barcelona to India to Tokyo. Tokyo we come and do a lot of things here, China and so on. We welcome you guys to join us. I mean to help us with this data autonomy kind of movement. So thank you very much.