Okay. Welcome to the talk on cutting climate costs with Kubernetes. Hi, I'm Shiva Rezaei. I'm Iranian. I migrated to the U.S. 11 years ago and I just graduated with the CS and Psychology degree from Harvard School of Continuing Education. And I worked in many different industries, but this is my first tech job as a developer relation for CDERA Labs. So you've been warned. Just adjust your expectations. Hi, I'm Steve Francis, the CEO of CDERA Labs. I graduated with a CS degree and a law degree a lot longer than Shiva did in Australia. So I've spent most of my career as an SRE, working for SaaS companies. And then about 15 years ago, I started my own SaaS company, LogicMonitor.com, a data center monitoring SaaS company, used by PayPal, Netflix, eTrade, at least it was at the time when I was there. And while at LogicMonitor, I worked with Andrew Reinhard, who was doing our migration onto Kubernetes eight years ago, something like that. And then Andrew went on to start an open source project, Talos Linux, which makes deploying and managing Kubernetes easier for the work he was doing. And then he started a company around that about four years ago, and I've been CEO of that for the last three years. So now I can confidently say how little I know about Kubernetes, but little more every day. Okay, so today we're going to talk a little bit about our company and our products and briefly. And then go into how we can cut climate costs with Kubernetes. We're going to do a canned demo and then take questions after that. So SideroLab, as Steve just mentioned, was founded around Talos Linux, which is a Linux distribution written from scratch just for Kubernetes. And it's like Linux and Kubernetes had a baby and send it to Avenger Training. And then it came out very fabulous, but only does Kubernetes. So there's no SSH, no bash, no package manager. And it is API-driven and configured declaratively just like Kubernetes. It's very minimal and little surface for tech. So then the other products that we have, we have SideroMetal, which is cluster API provider for bare metal creations and management of clusters using CAPI. The other, this mic is so loud, the other product that we have is Sidero Omni, which is a SaaS simplified cluster creation. It's a SaaS that simplifies cluster creations and management on bare metal edge devices or cloud devices. And it, how it does it is by booting off ISOs and AMIs and, or other image. And then it goes, uses API or UI to create the clusters in few clicks. If you have not used it, you should try it. It's few clicks, it's much easier. So I know from my days when I worked as an SRE that the easiest way to solve scaling problems is to have a provision. Especially if you're talking about bare metal, like back in my day, it was like, well, we're going to need more servers. So you order them from Dell and they'll show up in two months. And then it takes another two months to get power from your data center and networks provision. So to avoid those things, you just deploy more than you needed at any given point in time. I know when I ran data centers for an ad tech company, we could get by, like we had peak demand during, you know, US East Coast morning time. And so during that time, all, you know, several hundred servers were all almost all busy running a pretty high CPU load. But outside of those times, we could have got by with probably a quarter, maybe even less of the servers at any one point in time. But we didn't, we left all hundreds of servers running on all the time, all consuming energy, all generating heat that the heat required more cooling, which consumed more electricity. So not very efficient studies show US data centers contribute between one to two percent of all US greenhouse gases. So that's a pretty big opportunity to make a sizable target. So this talk, I think in, is in the program is about using cluster API to solve climate change. We changed that. We initially thought this was going to be a cappy talk, but because our largest customers like Nokia do in fact run sedero metal, which is a cappy provider for their metal. And so, you know, having Nokia be able to power down part of their servers and fleet, great idea. But we also have a whole bunch of customers that use Talos just by itself without a cappy to run relatively static clusters. And then we have, as she mentioned, we have Omni, our SaaS for Kubernetes and Edge devices. We have customers running hundreds of clusters on that. So we wanted something that could be more universally applicable than just cappy. So we can find a way to shut down extra servers. We want to do it for everyone. So what we came up with is not cappy, not cappy specific. It's not even Talos specific. So it can be used by everyone, whether they're running sedero metal, Omni, Talos, or even if they're foolish, one of these other operating systems that is not Talos. If they're not doing that, you're going to ask, what are they doing? So there are various systems in Kubernetes that make things dynamically adjust to the workload. However, none of those met our design goal for this specific, which is being emissions aware and broadly applicable. So those options are the horizontal and vertical pod autoscalers and cluster autoscaler, which quick review of those. So horizontal pod autoscaler will add or remove pod replicas in response to the workload. And so if there's not enough, it will add. If there's too many across all pods, then it will remove. The vertical pod autoscaler will change the CPU and memory request and limits of a pod. So it doesn't schedule more or less, it just changes that specific pod. And neither of these two will affect the infrastructure to achieve what we want to do, which cluster autoscaler does kind of. So cluster autoscaler automatically adds or removes nodes in a cluster based on all pods request resources. It looks for unschedulable pods and will scale up clusters if needed. It also tries to consolidate pods that are currently deployed on only a few nodes and if it can free up. And this is a large part of what we are doing here, but it's not a mission aware, so it doesn't check what's happening with what type of energy we're using. And for that, it doesn't consider priorities in a scheduling, so we have to consider that. And bare metal only works with CAPI managed clusters like sidero metals. We wanted something more broadly applicable, so that's what we did. Yeah, so we would have had to write a scheduler anyway to work with cluster autoscalers, so we decided to make it simpler and just write an emissions aware scheduler. Kubernetes allows you to run multiple schedulers, which is a good thing. So you can apply specific schedulers to specific deployments, and this is good because it means you can use the generic Kubernetes scheduler to run your critical workloads and your control plane and everything like that, and then your workloads that you are possibly less critical, you can apply a custom scheduler for. So the default scheduler, yeah, we just left alone. So at a high level, what this project does is it checks the emissions on the grid in the area where your data center is, and gets back an answer of zero to 100, where zero is the energies all renewable and clean and sunlight, and 100 is the worst.  So then it compares the emissions that are currently going on to the pod priority, and only runs pods if they are a higher priority than the emissions. So the scheduler has three components. It has the scheduling logic, pod manager, and a node manager. The scheduler looks at pod priorities and emissions. We will go through the actual process in the demo, but it will decide whether a pod worth running in the face of dirty energy. If your pod is time critical, you'll want to make it sure it runs even if it costs an owl or two, but given lower priority pods could start when energy is clean, then later the energy changes and what you're going to do. So we need the pod manager to evict those ones. So it will take care of that. And then to save energy, the node manager will go check, okay, what is happening? If the nodes are needed, they will power them. If they're needed, they're on, but if they're not and they're idle, it will turn them off. The way you create a scheduler that behaves differently than a default Kubernetes scheduler is to use plugins that can change the behavior at different extension points, sorting the queue, pre-filter, filter, and so on. So if you don't override the extension by using one of the plugins, it will just use the default which will do the upstream scheduler code running, and that's perfect, that's nice, that gets what we want. So we just affect the ones that we want and the rest will work perfectly. We want to affect the pre-filter plugins in this project, and our plugin filters whether a pod should be run by comparing the emission and emission level and the priority of the pod. Yeah, so just a note about the cluster topology that's in use in this demo. We're going to show off a Talos Linux feature, but not just because we think it's cool, but it is, because it's actually applicable in the energy-saving use case. So the way this scheduler works is it powers nodes on and off by connecting to the BMC, the Baseball Management Controller, using IPMI. So in order for that to work, wherever the scheduler is running, it needs network access to the BMC cards of the servers that are going to be powered on and off. So normally that's not a problem, because clusters tend to run in the same data center. What we often do at CDERA Labs is not that. Talos Linux has a function called kubespan, which transparently encrypts traffic within a cluster when it's needed to. And this is great because it means you can run your control planes and workers in multiple locations. So one thing we often do is, you know, if we're running big, beefy nodes on, like, equinex metal, they're large and powerful and can do a whole lot of CACD stuff, very powerfully, but they're way overkill for a pretty small cluster that doesn't do a lot of dynamic changes. So we run our control plane nodes in some way cheap, you can get small virtual machines in Amazon or Azure or in this particular one that's in Vulture, and then the workers somewhere else on bare metal. But this also gives rise to an issue because the kubespan takes care of all the traffic within the cluster, but the BMC nodes aren't within the cluster, so they're not part of the kubespan mesh, the wire guard overlay. So in this case, we actually had to run the scheduler on a worker node that's in the same data center so that it was behind the firewall so it can actually attach to the BMC nodes. So that's kind of why it's not just a simple deployment on the control plane which you would normally do. So we will now deploy our new scheduler and see what it does. Basically, these are the steps that you will need to go through to deploy this. It's all, you know, up there on GitHub and there's a readme and documentation and stuff, but these are the high-level steps and we'll go through all these things kind of one at a time. So you will need to create a what-time account to access the real-time energy emission in your area by an API. What-time is a non-profit and writes on their home page. You can enter your zip code and it will give you which we did for Chicago this morning and it will tell you how it is and Chicago is not that bad, not that great either. So the next step is creating priority classes because Kubernetes loves complication or abstracting depending on how you look at it. You can't set priority on a pod directly. You have to create a priority class and then reference that priority class in your pod creation manifest. So in this case, we're just creating two priority classes. One is high priority with a value of 100 and one with low priority class. The high priority with a value of 100 and one with low priority with a value of zero. So the high priority one is going to run basically no matter what the emissions are, ours be damned and the priority of zero is only going to run if the energy is completely renewable and coming from the sun and wind and nothing else. So we create the priority classes and then... Yes, and then we create the classes defined by the usual kubectl apply and that will go through. All right, so now we've defined our priority class, we've created our priority class. We now need to say, all right, this particular BMC is associated with this particular piece of hardware and the way we do that is we just annotate the nodes. So here we're just adding in three annotation endpoints for three annotation values for each node. So the BMC's IP address and the username are password to access that particular BMC for this particular node. So you do this for each particular node in your cluster that you want the controller to manage. We also need to edit the demand set to configure it with the what time credentials, which you'll see here. I don't know if you're blocking it, it's down there. And then we are going to do, I think, San Bernardino. Yes, I'm literally in front of it. And that is done. Then we'll go to the next step. Now we can apply our demand set and we get all the fun outputs showing that it is configured. Okay, so now we've deployed it. What happens? So we've got our scheduler. As we've said, the scheduler just checks the current emissions from what time for the area you've said your data center is in. If there are pods that are in the scheduling queue with a priority higher than the current emissions, they'll pass the pre-filter and continue on with the scheduling process. If there are pods that are less than the current emissions, they will not pass the pre-filter. So that's all the scheduler does. And then the pod manager basically checks any pods that are running now, if they are less than the current emissions, they'll get evicted. And then the node manager does one of two things. It turns on or it turns nodes off. It'll turn a node on if there are pods that have passed the scheduler and waiting to be run, but there's nowhere to run them. If that's the case, it'll say, all right, great, the scheduler has decided these pods are worth running. Given the current emissions level, I should turn on another machine and it'll do that. And then it'll also say, okay, if the pod manager has decided all the nodes on this machine are worthy of being evicted, I can shut down that machine. To apply the scheduler to deployment, I'll set the pod priority in the deployment YAML file. Okay, let's play it again. We're just adding a scheduler name and a priority class name. The scheduler name is the scheduler we defined before on the priority classes, in this case, high priority. Okay. Then when we deployed, a high priority will cause it to be scheduled, regardless of emissions. And if there is nowhere to run, it will cause the node manager to power on another node. And this is exactly what we just explained. We can see that one of the nodes at the bottom. I don't know if you can see, but we see. Right down here, it's switched from not ready, scheduling, disabled to ready, and in response to the need to run this pod. Okay, another one. So yet a low priority pod, which just got deployed, the workload was created, and then now we look at the pod status. We will see the high priority one is running. The low priority workload is still pending. So now if we describe that low priority pod, why is it still pending, you may ask. And it's upon to the next one. So we can see it failed scheduling. That's why it's stuck in pending because zero of the nodes are available because the pod priority of zero is lower than the emissions index of 44. So in this case, had the pod priority had a priority of 50, it would have run. If it had anything less than 44, it doesn't pass scheduling pre-filter, so it fails scheduling. And here we see if we change the priority of the running deployment to low priority, it is evicted. Low priority and get pod shows that I think we're doing get pod now, and we'll show both pods are now pending. And because that's now freed up a node after a few moments, it will the node is powered down and it is now idle. Then node switches from ready to not ready with scheduling disabled. It just changed here. So that pod, that node no longer had anything running on it, so the node manager said great, I can shut you down, save energy. Okay, so we kind of, this is a part of the project that still needs work. We used a poor man's bin packing system. We take advantage of two different attributes of the Kubernetes scheduler here, the default Qsort and a different option for node resources fit attributes. So our scheduler uses the most allocated option for node resources fit. This basically means it sticks things on machines that already have other things on them. Normally by default, Kubernetes will spread out the workload amongst all the workers in the cluster. The most allocated option says if you've got whatever say five nodes and one of them has a job running on it and you get another deployment, it'll put it on the same machine if it'll fit. So it tries to put things where they're most allocated. And the other attribute is the default scheduling system schedules high priority jobs first. We didn't do anything for that, that's just default Kubernetes, default priority sort. So basically if you turn on the cluster and there's a variety of high priority jobs they'll all get scheduled first and with the most allocated, they'll all get scheduled first on the same node. So this means you'll get some nodes that have all the high priority jobs, the medium priority jobs and the low priority jobs and then the low priority jobs get evicted, that'll tend to free up an entire machine. So that's kind of like a poor man's bin packing. It's clearly not perfect, it's not dynamic. It doesn't take account of taints and tolerations and resource constraints and some things having GPUs and different machine classes but it works if your workloads are fairly consistent, your cluster's consistent, it'll work. So it's definitely not perfect. So that's kind of it, things to improve. Everything. Bin packing, yes, certainly. We don't have full integration with Omni at the moment or cluster API or the cluster roto scaler, but these are things we can add in and we plan on doing things like this certainly and we'd love to get ideas from the community. Yeah, so good use cases for this would be generally any bare metal workload that has a periodic demand. Things that can be time shifted, things that aren't time critical. Not things that are dealing with interaction with humans because they tend to need to happen when the humans need it. But anything that's like a batch job bank financing that happens after hours if it needs to happen in a 12-hour window, you'd rather schedule it in that 12 hours in California that's usually in the day time, that's when solar energy is online and not kind of late at night. That's all we got. Now we can take questions or we'll change the slide to the, well we will have access to all of this links and we have a happy hour after, but we'll take questions now. Yeah, the GitHub repository is there, that's where the project is we would love you to rate our session highly. But you can rate it lowly too. Higher would be better. And we are doing a customer mixer customer mixer after this at Simone's bar in an hour, which is not far away. But we would also love to open it up and hear questions, feedback, discoveries thoughts on the talk we just gave. Yes, I don't know how the microphones work. Do you go to a microphone or do the microphones come to you? Two, one over there, one over here. Does your current implementation have any sort of mechanism for ensuring a maximum wait time, like if the local grid goes down? Well if the local grid goes down, then your data center's offline. The clean energy grid. Okay, yeah, no, it doesn't. That's actually, that is exactly a point of improvement I had in there that I took it out because I thought it was a bit complicated, but apparently it's a real use case. What we were thinking is having the priority of a job kind of increment over time, so if you schedule a job at ten, it'll only work when the energy's clean, but if the energy grid never gets that clean, your job's never going to get scheduled. So you may want a process that says jobs that are using this scheduler that have a low priority every ten minutes their priority goes up by one and eventually it'll get run. But that's not implemented at the moment. Yeah, thank you. Oh, I should look at the slack too. What sort of audit trail exists so I can see what the impact was and sort of try to understand we were able to save this much energy or some metric that I can show back that says, yeah, we actually did something and it worked. Yeah. That's a great idea. We would know that information from within the Kubernetes space, but if you're monitoring your power draw within your data center, you can definitely do it from that side. The system can report up, you know, this number of nodes were powered off at this time, but it doesn't know if it's a 10,000 watt server or a 500 watt server. We're using, it's not polling, it's using the... Yes, yeah, we do. Yes, yeah, so the question was that if we're using logging events then those metrics are things we can use. Did you consider using Carpenter as the whole pod lifecycle, like shutting them down and bringing up new nodes? Uh, no. But I can't tell you why. I just said take questions. We didn't say we're going to answer it. It sounds like there's a huge overlap with the whole consolidation, it's an AWS thing and they take money as the kind of input parameter. You make it cheap, you make it green. Yeah, yeah. Like we said, there's overlaps with the cluster autoscaler from Google, there's overlaps with Carpenter, there's overlaps with Gardner. But this was... We tried to avoid adding in access complications. So this is kind of like just looking at emissions and scheduling in that way. And so there's certainly room to integrate it into these other systems. I think that would actually be really helpful, but at the moment this is kind of the standalone project. Yeah. Sorry. Oh, I'm on the other mic. Sorry, I jumped over here because it was easier. I was also going to say with Carpenter, I work on the Carpenter team and there's not actually hooks available for something like a data center, like API endpoints. You'd have to have something like Cappy there to be able to call out for a fleet of machines. You need both extensions of that available before you integrate it with Carpenter. My question with the shutdowns was, like a rack where I could tell, let's say I actually have 50 machines that can go away and there is overhead available. I would love to be able to shut down a rack in the data center so I can turn off AC to portions of the building because that extends how much power I'm saving for something like a batch job. If I'm only using the batch jobs for 50% of the data center at a time, is any of that also a co-location of servers available in there? Not as it is, but that would be doable if you annotated your servers with rack location and then you had your node manager prioritize common rack locations first. I was thinking rack and heights of servers because the AC costs of lower versus higher in the rack would also be valuable to say, let's turn off the high ones first because they take more to cool. That would be the talk number two for the next North America or we all meet in Paris in six months. Give us some time. Are there data sources that support energy outside of North America? I was messing with it and I threw in some UK zip codes just for fun. What time doesn't I don't know if there's a equivalent API outside of the US. Because also probably each country and continent has their own metrics and different things. It is possible to find each individual ones and then put all... I would actually think it's probably easier in European countries because the countries tend to have unified power grids as opposed to the US which has fractionated and crazy and private enterprise power grids. I think maybe green pixie in the UK might be the supplier there. And some probably don't even have anywhere to report. You don't get information if you're in Texas. I was thinking you're wrong but not make it political. Anyone else? I would like to say who uses Talos Linux now? Show of hands. Some. Good. What are the rest of you doing? You're doing it wrong. I guess that's it. Five minutes of your day back and you can use that time to commute over to Simone's Bar or wherever your next event happens to be. But if you are with us, Simone's Bar at 6.30. Thank you for coming. Thank you very much.


Hi, my name is Joe and this talk is on our paper that's been accepted to Crypto 2021, lattice reduction with approximate enumeration articles, practical algorithms and concrete performance. This is joint work with Martyn Albrecht, Shibai and Jan Wiley. So before we start, I'd like to give you a high level summary of what this talk and what this work are ultimately about. This is a talk about blockwise lattice reduction and in particular, we focus on the role of an algorithm called enumeration inside these blockwise reduction algorithms. And crucially, what we're really going to focus on is how changing how these articles behave inside a blockwise reduction algorithm can change the running time by an exponential factor. There's obviously some clear motivation that's needed for this work. And in particular, you can view this via the fact that lattices are the basis of five out of the remaining seven schemes in the NIST PQC process. Understanding the difficulty of solving the problems upon which these schemes are based, such as the LWE variance and entry, is crucial for setting cryptographic parameters. If we don't truly understand how hard these problems are to solve, then we may set either parameters too large and compromise on efficiency or too small and compromise on security. And although there are many approaches to solving these problems, the best practical attacks against these schemes require blockwise lattice reduction. So before we begin, we'll go over some polymeries. So if we have some set B comprised of vectors B0 through to BN minus 1 of some linearly independent vectors, then we refer to the set of all interlinear combinations of the vectors in B as the lattice L of B, or simply L. You may also have heard of B by a different word, which is referred to as a basis, but for the sake of this talk, we shall just refer to it as B. Lattices have an associated invariant known as the volume, and you can see that this is efficiently computable for some set B when the vectors are linearly independent. This volume also has a nice geometric property as well. It corresponds to the density of the lattice. If your volume is small, then it implies your lattice is very dense. Whereas if you have a large volume, it implies that the lattice is rather sparse. When thinking about solving cryptographic problems, typically speaking, we focus on the shortest vector problem. In this work, we focus on a variant of the shortest vector problem known as Hermite SVP. In particular, the definition of this is if we want to solve delta approximate Hermite SVP on an n-dimensional lattice L, we require to find a vector V that has norm less than delta times the volume of the lattice normalised by the dimension. Typically speaking, one might consider delta as this square root of N over 2 pi eta. The reason for this is because this is exactly the leading factor as predicted by the Gaussian characteristic. Because this value depends solely on the dimension of the lattice, for the rest of this talk, we'll simply refer to this as GH of N. So just to give you a pictorial representation of what a lattice looks like, you can see on the left-hand side here that we have a regularly repeating set of points, and you can also see on the right-hand side we have a picture that shows the shortest vector in this lattice. Geometrically speaking, you can view the volume as the area of one of these squares that appear on the slide. So for example, if you took a point of the origin and went out to the point that is closest to it on the right-hand side, then this also forms part of a square. And the volume of this square is the volume of the lattice. When attempting to solve HSVP, typically what one does is replace the exact problem within approximation. And the typical way to do this is to embed an exact HSVP solver inside some block-wise reduction algorithm. The intuition behind why one might do this is because you're replacing the basis by a set of short, nearly orthogonal, linearly independent vectors. And the reason for this is you're trying to balance out the contribution of the lengths across all of the vectors in your basis. In particular, we take the basis for some lattice of rank n and decompose it into projected sublattice of rank k. And the way that we form these projected sublattices is if we have some projected sublattice l of ij, we project the vectors in the basis from bi up to bj or thug and liter the span of the previous vectors in the basis. And this provides a very powerful time quality trade-off. If the rank of our projected sublattices is close to the rank of the lattice at large, then the output basis is more likely to be short and near orthogonal. By contrast, if we make k rather small, then the problem will be faster to solve because it's less challenging. But the quality of the output basis is unlikely to be as good as we would want. So there's a real trade-off here between how large we choose these blocks to ensure that our output basis looks a certain way. But we also need to balance this against the running time. One way to gauge the quality of these algorithms is using a measure called the root Hermite Factor or the RHF. And put briefly, the root Hermite Factor of some lattice l is the length of the first vector in the lattice divided by the normalized length that we might expect. So this term in the denominator here is the length you might expect if the norms of all of the vectors in the lattice were evenly distributed. Because this quality is independent of the rank of the lattice, we normalize by this term 1 over n minus 1. But this is just to normalize the quality for what, so we can compare across many different lattices. One blockwise reduction algorithm that features quite a lot in the literature is called BKZ. And what BKZ does is you divide the lattice up into blocks of size k, and you first find the shortest vector over the first k elements, and then you shift one along by projections and find the shortest vector in that sub-lattice. And you continue this process many times until you end up with a basis that looks a certain way. There are some theoretical guarantees on how BKZ operates, but you can essentially think about each block having the shortest vector in that block in the first position. It can also be useful to run BKZ on these predicted sub-lattices in a recursive sense, so that you have some guarantees about the shape of the basis or how the norms are distributed before you start doing the rest of the work. But again, this is another trade-off that needs to be considered, because pre-processing also comes with the cost. Once you have a blockwise reduction algorithm such as BKZ, you do need an exact solver to reduce the blocks. And in this work we consider an algorithm known as enumeration. Enumeration works by walking across all of the points inside a ball of some radius r that intersects with the lattice. So we're looking for all of the lattice points that are inside some ball with a particular radius. If you center this ball at the origin, then this has the same effect of making sure that all of the vectors inside the sphere are less than length r.  This algorithm has been very well studied, and to date the best known variance of this algorithm run inside BKZ in time 2 to the power of k log k divided by 8 times some exponential term. But crucially they also run in space polynomial in the rank of the lattice. In this work we're focusing on improving this exponential term without compromising on the root-hermite factor. We want to make sure that no matter what we do here we maintain the quality of the basis as output by BKZ. One classic approach to doing this is known as pruning, and intuitively the way you can think about this is that the previous explanation considered finding all of the vectors inside this set s intersect L. But not all of the paths in the enumeration tree are likely to lead to solutions. The enumeration tree is very large in size, and we don't expect there to be that many short vectors. As a result, in a typical approach is to cut off some of these paths that we don't need, and this is referred to as pruning. We simply cut off branches in the enumeration tree that are unlikely to lead to short vectors. There are many different approaches to pruning, but the most popular one is referred to as extreme pruning, and this can provide a speed-up that is exponential in the dimension of the lattice. We'll come back to how extreme pruning works later, but the idea is that one sets a parameter that is rather aggressive, so you expect to cut many, many paths, and whilst this drops your success probability exponentially, the cost of re-randomizing your basis and then running pruning over that basis again is cheaper than the speed-up that one... Oh, sorry, it's less expensive than the speed-up that one gets, and thus you still end up with a speed-up even though you're going through this process of randomizing the basis, re-reducing it, and then running an extreme enumeration ever. Now, there are other approaches to this problem, and a rather natural one is to merely loosen the requirements on the solution, so in particular it may be wise to accept a longer vector from our enumeration oracle. We refer to this process as relaxation, and so just to give you an example, we might accept a vector that is at most a factor alpha, which where alpha is some constant greater than one, longer than is predicted by the Gaussian heuristic. This was recently studied by Leon and Winn, and they showed that if one of relaxes enumeration in site BKZ, then you can get a speed-up of around 4 alpha squared times 1 minus rho all to the power of k divided by 4, where rho here denotes a pruning parameter in the very 0 to 1. As rho approaches 0, the pruning becomes more aggressive, but the speed-up also increases. One disadvantage here though is that we're accepting a vector that is further away from being ideal, and this can lead to our final basis being less strongly reduced. And so the approach we take in this work is we ask, what happens if we increase the size of the blocks to accommodate the loss in quality that we get by using these approximate oracles? So as before, we'll let alpha denote the approximation factor that we're willing to accept. Our approach is to replace enumeration in block size K by enumeration across a block of size the smallest integer greater than K, which we denote as K alpha. In particular, this K alpha should be chosen such that the root-hermite factor does not increase. In other words, we numerically solve equation two at the bottom of the page here, where we're looking for a value K alpha, such that when we find a vector of length alpha times GH of K alpha, normalized, we do not increase the root-hermite factor. And this K can be, or this K alpha can be uniquely determined. The way to solve this is to set this equation here up as an equality, and then round the value of K alpha up. So our main result for this work is we show that the speed up from relaxed enumeration is enough to overcome the extra cost from increasing the block size. This speed up holds for both fixed pruning parameters, i.e. when we choose row, and the numerically optimized pruning parameters that are chosen by the FPI-LLL library. For those of you who aren't aware, FPI-LLL is a lattice reduction library that has been used in many different cryptographic works, and one of its crucial features is that it sports a numerically optimized pruner that, given some basis, will compute optimal pruning parameters for speed and quality and success probability. Just to go into more detail quickly, if we assume the enumeration in rank K costs 2 to the power of C0K times log K plus C1K plus C2 many polynomial time operations, where C0, C1 and C2 are just some constants, then if we have some fixed parameter row, we can solve alpha GHK alpha HSVP in rank K alpha, faster than solving GHK alpha HSVP in rank K by a factor of at least equation 3. Just to guide you through this a little bit, it's the term on the left-hand side that really matters here. So it's this alpha to the power of a half minus C0 eta K. Here eta is some parameter that is inside this range here, but you can see that this really isn't as important as the alpha to the power of K term, essentially, that we have at the beginning. Our proof technique works by showing that we have a bound on how large K alpha can be compared to K. So in particular, we don't expect K alpha to be much larger than K, and as a result, we can prove some inequalities based on how large the enumeration tree will be for K alpha relative to enumeration in rank K. So just to show you some results quickly about how this works. So in this table, we have this term here, T alpha, which denotes the time for solving alpha HSVP in rank K alpha, and T1 is the time that it takes us to solve GH SVP in rank K. And we've just chosen some fixed pruning parameter here, rho, which equals 0.01. In this table, you can see that we have an alpha term on the left-hand side that just shows the results for differing values of alpha. And then in the middle of this table here, you can see both the time complexities for what running enumeration in rank K alpha with alpha GH SVP, and how this compares to the baseline in the third column. You can see here that we get exponential speed up as alpha increases, which is exactly what our previous theorem showed. But crucially, this does not yet approach the state of the art. So as I previously mentioned, the leading term in the state of the art is K log K divided by 8, whereas here we have K log K divided by 2e. And this second term as well is undesirable, simply because the approach of extreme pruning in practice provides a larger second term. So the natural question now is whether we can do better. And the answer is yes, you can. So firstly, a reasonable approach is to try and use F pi LLL to compute the pruning parameters, because it may well be that for some fixed value rho, the pruning parameters that are chosen are not optimal. And secondly, we can also try to integrate the super exponential improvements from this work that's cited here at the bottom, which I'll refer to as ABF plus 20 for the rest of this presentation. The crucial part of that ABF plus 20 is that they provide an algorithm that does achieve the state of the art with a leading one over eight term. And thankfully, these ideas appear to be somewhat orthogonal. And we found that we can achieve a speed up over the state of the art when using numerically optimal pruning parameters and the improvements from ABF plus 20 whilst achieving the same root Hermite factor. So here we have some simulated costs for achieving the same root Hermite factor, which in particular is K to the power of one over two K. And so you can see here the simulated costs with our line fit that if we have out three equals 1.05, we end up with a cost of K log K divided by 8 minus 0.596 K plus 12.09. And we have a similar cost for our three equals 1.2, but you'll notice that the the coefficient in front of the lower order K term here has decreased. And so we have achieved an exponential speed up. But of course, we also need to be careful, because as I've previously mentioned, when we relax enumeration, we can end up with a lower cost, or sorry, a lower quality solution. And the way we do this is we increase the rank of our enumeration oracles inside BKZ. In particular, we define a new BKZ variant that extends the BKZ variant from ABF 20 or ABF plus 20, sorry, simply by adding alpha as an additional parameter. As a further speed up, we also used our relax oracles during the pre-processing stage when it's advantageous to do so. The subtlety here is that when one pre-processes a block, it is typical to shrink the block size quite a bit. So if you're pre-processing a block by using BKZ, if you shrink the block size too much, relaxation may no longer benefit you. And as a result, when it is no longer advantageous to do so, we simply use the default setting. But when it is advantageous to do so, we use these relax oracles. There are lots of parameters that can be customized in this BKZ variant. And so to avoid going into detail here, we provide a full table of configurations in the paper. So this is just to show how this work and ABF plus 20 have moved the state of the art in the last few years. So as reported in ABF plus 20, the running time provided by the FPLLL library achieves time k log k divided by 2e minus 0.995k plus 16.25. ABF plus 20 from last year achieves a running time of k log k divided by 8 minus 0.547k plus 10.4. And in this work, we do not move the leading term at all, but we do exponentially accelerate the enumeration oracle by decreasing the second term here from 0.547k to 0.654k. This is all for achieving a root home factor of k to the power of 1 over 2k. So this is exactly what we would expect for BKZ asymptotically. You can see here in this graph that we have managed to lower the cost of achieving lattice reduction in certain positions. So for example, if you consider 2 to the 256 operations, you'll notice that artwork moves the crossover point to around rank 500. Whereas just two years ago, it was around about a rank 400. And indeed, we make a larger improvement compared to ABF plus 20 versus ABF plus 20 against the baseline provided by FPLLL. There is a following takeaway from this, and that is that this work does not utilize the regular re-randomization provided by extreme pruning. So as I mentioned earlier on, extreme pruning has this property where one aggressively sets pruning parameters. And if a solution is not found, one re-randomizes the basis and then repeats the process. Our work doesn't do this. And in particular, we believe that this is due to the expensive pre-processing that we need to ensure that our algorithm works. So we may yet find further speed-ups if it is possible to find a way to reconcile these strategies. If pre-processing can somehow be preserved whilst doing this regular re-randomization, then it may be that a further speed-up is possible compared to our approach. And so just to demonstrate this via a graph, you can see here that the expected number of solutions, and thus the success probability is roughly constant as the dimension of the lattice increases. So you can see that it roughly covers around one with some noise. And this is the same for all values of alpha. This is essentially a proxy measurement for the success probability because we would expect the expected number of solutions to decrease as the rank increases because the success probability goes down. That is not what we observe here. And so there still may be some speed-ups by forcing this number of solutions to decrease as we increase the rank. But despite all of this, this work does not invalidate any existing NIST security claims. Although we may have moved the crossover for enumeration for two to the 256 operations from rank 400 to rank 500-ish, the singly exponential saving is still far faster than enumeration. And none of the speed-ups in this work are enough to lower any security claims for any of the NIST-based crypto systems. With that, I would like to say thank you for your attention, and I look forward to answering any questions you might have on Zilith.


Without any further ado, I'm going to move to our first speaker, welcome Irene Patrick. Dr. Irene Patrick is the Senior Director of Industrial Innovation Internet of Things group at Intel. Intel being our most recent Platinum member actually, so we were delighted to have Intel join at that level in April this year. Irene joined Intel in 2015 and focuses on emerging technology, social and global trends, and their combined impact on the industrial space. Her work highlights the industrial Internet of Things, edge computing, the transition to intelligent manufacturing, and the needs of the future workforce. 3D printing and distributed manufacturing and the new business models that are enabled by intelligent manufacturing. A long list of things are involved in there Irene. Today Irene will look at what it takes to drive and scale a project in the Internet of Things space. So a warm welcome from, virtual welcome from the open group to Irene Patrick. Over to you. Thanks. So today I'm going to share with you and I've left lots of time for questions and if you have questions as I'm speaking please interrupt. I'd love to have this be a discussion rather than a formal presentation, talk at them kind of a thing. So what I want to talk to you today about is the the challenges with really standing up projects we call it projects at the edge of understanding. So when I take AI and when I combine it with IoT technologies what does that really mean for standing up a project for really making, having it be successful and then having it be able to scale. So what I'm going to share with you today is a lot of work that we've done over the last couple of years with over 400 participants. So this is it's not my opinion specifically, it's not Intel's opinion, it's really practitioners who are technologists in manufacturing companies and the technologists in ecosystem companies that support them. In the effort to be brief and piffy I've tried to make a lot of this very specific but I put material in the backup so if there are things that interest you there is additional material in the backup if you if you wanted some of the details. So I'm going to talk about a couple of things. First of all we did two years of study my face my colleague safe McCrary and I of over 400 people we call it thinking in the wild so to speak. In phase one we really looked at what the industry, what was happening in factories, what was going on within the factory walls but also thinking from the factory floor to the C suite in terms of factory operations and what we found in that study in 2018 is that it really was the co-evolution of workers and manufacturing operations. When we looked at that we said well that's great to know but how can we as a technology company how can we better understand what strategies and tactics actually help us accelerate these smart projects and that's sort of what we refer to as the AI plus IOT projects we just call them smart projects to make it easier. So we have a series of six different seven different reports that are out all of which or all but one of which is available publicly so if you're interested in the backup there is additional information about those reports but today I'm going to focus on one of the challenges of taking these smart projects and what is from the technologist's point of view and I have a graphic in the back that talks about who those technologists are and why they matter and what kind of backgrounds they have but right now what I want to do is really focus on what I'll call the smart project mindset and the smart project mindsets are important to understand because they really emphasize who's the expert innovator who's the innovation maker what about the production person what about the process watcher and to really understand how to stand up these projects we have to understand the kinds of people who are participating in the the the expert learner is really all about smart innovation and ideas around manufacturing this person this expert learner on the left is probably the most seasoned person that you would see and this person is somebody who teaches him or herself as they go and is always looking for new ideas and new ways of doing things these people become very important on teams however we also have to think about the fact that these people can sometimes get ahead of their technologies the the innovation maker really is much more around I'm the deep person who wants to try new ideas and I really want to have I've got too much chat going on here I really want to have a better sense of of the production I want to have a better sense of the manufacturing and I want to understand how I make it how to improve those processes the production champion really is the person who understands the production environment very very deeply we would call this the OT expert and then you've got those process washers over there on the right hand side and those process washers really are a little bit more conservative than the other three mindsets and those process washers are working looking for where is risk going to be introduced into my process how can I make this robust how can I not bring down the rest of the factory or the rest of the line and I implement this so you can see that all four of those mindsets become very important different stages of these smart projects what do we know about the projects and about the challenges well on the left hand side and this actually surprised my colleagues faith and I when we look at the recent project types much more of them are in production than we anticipate almost 61 percent of them are in production we I'm going to come to this point in a moment but we think that a lot of those production projects that people describe to us last year in our study really have to do with some of the low hanging fruit that I could implement on an existing machine you can see however that there's a large percentage of pilot prototype and proof of concept projects that are being done that are really pushing the envelope more than those that are in production right now and I'll call to mind there were 193 technologists that were responding to this so we're not talking about a small sample and those technologists are spread across a wide variety of industries and a wide variety of roles and companies interestingly the areas of biggest challenge that that technologists told us when we talk about the different areas of a project it turns out that the biggest challenges are coming as deployment and maintain maintenance comes up and we think this is really important because most of the technology companies are really focused on how do I define the program the project how do I architect it how do I develop it and develop the tools to support it and then we sort of say okay we're done now manufacturers go and deploy it and we think that that's a really serious problem because the majority of problems are happening at the deployment level and at the maintenance level that the sustainability aspect and and the reason for this we think is captured in that far right graph the expertise versus the responsibilities we're really seeing people trying to deploy smart projects where their expertise far exceeds I mean far underperforms what they need to do so for example when you get down to things like real time or when you get down to things like computer vision you can see the experience the people who have experience in the buff color are far fewer than those who are actually responsible to deploy it and we think this is a serious issue certainly my own teams are now looking at the kind of developer toolkits that we need to be producing and the kinds of supporting materials we need to be making available but we think that the ecosystem in general needs to be much more aware of what we call an unanticipated developer whose expertise doesn't quite keep pace with the responsibilities that that developer or a deployer is responsible for so this one is actually related to what I was talking about earlier when we pull these 190 some technologists less than 50% as they thought their smart project of today had a 50% shot of making it to production and we think this is a stunning number it might not surprise many of you however because we see pilot purgatory but but we have to then say well what causes pilot purgatory why is it so difficult to go from project at the pilot scale to actual scale and then long-term sustainability and what we're finding what technologists told us is the problem is that it's not a single problem it's really a holistic problem in other words I have to be thinking about people processes organization culture and organization structure and the technologies and each one of those in and of itself has its own problems but when you put them all together in these smart projects we find that it becomes really challenging and when I when we think about on the right hand side the top factors that people told us we're driving this complexity not surprising the majority said operational technology is a real serious problem my company and my processes are different than other companies in different sectors we're all dealing with legacy systems but look at how far people come people become a difficult challenge because they have a skill set different a skill set a lack of skill sets they don't have a statistical mindset or a data driven mindset and then you think about enterprise systems well enterprise systems have been around for a long time the problem is that smart technologies when we start thinking about these projects we don't necessarily think all the other systems they have to touch and so our our our respondents told us it's really a system of systems approach and to get the sustainability I have to be looking at at the outset I have to be planning for all of the systems that my smart project is going to touch or rely on to actually provide insights and because of that participants told us technologists said we need a clear roadmap to go from pilot or poc to scale once we have that clear vision of what digital technologies can do for our company then we can begin to work incrementally and this becomes really important because you'll often hear fail fast you often hear start start small fail fast we believe it's think big and then start small fail fast learn quickly so it's a little bit different but if you don't have a real appreciation for where you think digital technologies can make a difference then everything looks like progress until it doesn't connect okay so what does this mean well this really means that uncertainty is the new normal and I'll indicate you're going to see quotes of several different times here in this presentation these are all quotes from technologists directly if we've made any kinds of additions you see it in little carrots so these are their verbate and words and this person this program engineer in actually an operating technologist in a manufacturing environment says the biggest challenge is uncertainty in the technologies they just keep advancing and we need consistency so what they're basically saying is there's a mismatch between what ecosystem partners are giving us and what manufacturing technologies can actually consume so 55 percent of the technologists said we're wondering how was the technology going to integrate and scale in their target environment and do we have the best mix of this technology for their purpose would a project deliver real benefit in the long run and this becomes really critical because often we think about smart projects and their point value when we think about ROI we don't think about how to re-envision the process so we we calculate we tend to calculate ROI in very what I'll call myopic terms and that's a serious problem and technologists themselves recognize if I can describe business value I'm unlikely to get my project funded and then the last one is what resources are really going to be needed and what will the workflow look like and this is an interesting one because IoT projects span a lot more traditional functional responsibilities than a more traditional upgrade would and so what we see is technologists working with people who are out of their functional silos who are out of their general work stream and workflow of people they collaborate with and so you've got a lot of relationship building that has to happen at the outset to really think about all the places this technology might touch the the operations because of that some of the projects really are a different breed they they really the technologists said smart technologies may be thoroughly described and I would question that because we're not hearing a lot about thoroughly described smart technologies but what is it described what people really care about is what's their interaction with other technologies and this person links it to it's like a pharmaceutical which is well described but which can have unexpected consequences when combined with other pharmaceuticals and we're hearing this is one of the biggest challenges to successfully going from pilot or PFC to scale and that is I didn't anticipate all of the other changes that would be necessary to truly achieve the ROI that was predicted often by vendors by the way I didn't really think about gee what will I need to have is as as databases that will have to interact what will I have to have this bandwidth if I have constrained bandwidth how will I have to think about computing at the edge versus sending all my data to a cloud if I have constrained bandwidth how to identify which data really matters versus which is sort of interesting to have and so the biggest challenge that we're seeing frequently for projects that don't go to scale is that we didn't think carefully enough about the infrastructure needs underlying that technology that smart technology so the smart project even when it's successful often doesn't yield the ROI we want to see so here's what we call the eyeball but this really captures we ask technologies to tell us what was their primary responsibility in terms of technologies that they were using on these smart projects and then to tell us what other secondary technologies were they required to know to be able to deploy that smart project so the way to read this is the larger the dots around the circumference the more common that was called out as a primary responsibility by technologists the lines between them indicate the amount of time that particular technology was paired with another and what you can see here and we took out all of the secondary relationships these are only the primaries when we had the secondary relationships in there it was a black circle and what this really calls to mind is that solutions aren't a single technology smart technology smart projects require combinations of technologies and frequently combinations of vendors and this becomes really problematic for a manufacturer trying to stand up a smart project when managing those vendors finding those vendors is often a really out of their comfort zone so so these technologies come from multiple vendors and the legacy systems and the infrastructure in which these processes sit didn't anticipate all of these interdependencies so it's not just that I have a legacy system or a legacy piece of equipment or that I have constrained infrastructure resources it's that I haven't even thought about all of the places these smart projects will touch so this is one that came very clear and these are once again these are technologists telling us this isn't purchasing this isn't a senior leadership CEO level this is the technologists themselves and we basically are finding that the vendor relationship which has driven most of our our interactions has really been a transaction it's been a handoff of goods and services for a particular fee under a particular contract during a particular time what we're finding with smart projects is that that's a recipe for disaster it really is much more about building a relationship with the partner who can share goals who understands the vision who has a clear idea of what you're trying to accomplish as the manufacturer and who has a a commitment to helping you see this through so it's not just a set it and leave it and and good luck it really has to be much more of a partnership and what we're hearing from technologists those projects that were the most successful really had an ongoing partnership management approach where they work with vendors and they work with them over time so at different phases of a project you might have different needs from a vendor and your vendor has to be as committed to that project as you are and understanding that upfront and building that into your contract language becomes really important and then you have to this is this is one that's getting companies into trouble right now manufacturing companies we have to be balancing our external sourcing to stand up smart projects with our internal resources to grow bench strength one of the things technologists have told us is that my company is so worried about being late to the party or missing out that they're bringing an external consultants they're bringing an external experts to stand up these smart projects and gee i as the expert in my company and left doing the grunt work and so i'm doing boring projects and i'm not learning how to stand these smart projects up and so we think that companies need to be aware of this this this tension because getting things done quickly doesn't always get things done in a sustainable way so here's another one when we think about the the role of vendors and the interaction between vendors and and manufacturers here's a consultant a strategist from a consultancy saying i think the biggest single request i would have is that documentation and literature improved and what do people really want well they want to get out of the marketing hype they want to get out of the jargon they want to get past high level two paragraph descriptions of their projects too much deeper pilot descriptions so i want to i want to hear not just success stories that are at a very high level i want to hear what didn't work so people want easy to understand actionable up-to-date content and messaging and and these these these things are things i think you can use as a way to say is my vendor do i have the right vendors is my vendor really being as um as let's see as responsive to me as possible so content that bridges technical silos also becomes very important remember i said earlier that i have content i mean i have people working on smart projects but often these are are creating teams that are outside of my normal working groups so i have to have content that helps bridge some of those technical silos it has to be much more detailed with better examples and tutorials with real-world examples and then the other two things that are important is companies technologists have told us that forums are very important when they think about where to go for information they're really not thinking about i'm going to go to my vendors website and i'm going to read all their marketing materials they they really want interactive peer-based communities such as open group to provide them with the ability to to be interlinked to have discussions where you can actually sort of discuss both the pros and the cons the positives and the negatives and you can do it in bite-sized chunks so i can get material just in time when i'm ready to consume it and and i think that technology companies have done a pretty poor job of this overall my own included by the way so here's another piece of it that's that's very important to standing up smart projects and that's the complexity takes engagement not just buying we hear about buying all the time but it's not that's not enough and in fact i'm working with a very large company right now helping them think about their digitization strategy and taking some of the work we've done and saying how could we roll this out for our own company and and what they're finding is people even when they have an appetite to change are fearful of some of that change and and really want a chance to work much more at the beginning when strategies are being talked about and discussed than they want to have strategies imposed on them so what are people asking for well first of all to get engagement you have to provide context i really have to understand what groups need to interact and i have to be able to tell those groups why they need to interact i have to find much more collaborative approach that brings these teams together and it creates a set of stakeholders that share a common vision that share a common mission and often those stakeholders are in a what we call a tribe they're in a much less formalized group than we're typically used to working in and then finally we found that the best projects really focus on the why of projects to bring others along in other words why am i undertaking this what value will it have why do i think that you as a particular line worker or technologist should become involved in this how will you benefit how will our company benefit so we really have to get past the we can do this three times faster or we if we introduce this particular type of compute we can process things more quickly that really it is considered behind the scenes i really don't care if i'm the manufacturer i really don't care if i can do it two times faster or 2.2 times faster or three times faster what i care about is what results i can get more quickly and how those results lead to business value and so we have to change the way the conversation is and we have to do it very early and we have to do it continuously over time so it's not a one and done here either often in fact this was true of the very big company we're working with a heavy manufacturer they had change management plans but those change management plans didn't happen until after they were down the pike of saying here's what we're going to do and oh line workers here's what we're going to provide to you and what they found is that those change management plans were inadequate to really create the engagement necessary to accelerate the adoption so what really sets successful projects apart is the ability of the early project team to ask the right questions at the start of the project and i have a list of questions that people told us were the most important things to address at the beginning of the project at the in the backup in the interest of time i didn't put it into the main presentation but i think it's worth a really careful look because many of these questions are what are we trying to accomplish so they'll start with the technology they start with what are we trying to accomplish why does this bring business value to it how to who does this bring business value so who are the first kinds of stakeholders should we engage only then do we start talking about what possible technology alternatives might help us who are the vendors we might go after and why and then how are we going to build relationships with those vendors so that this smart project isn't a standalone island in our manufacturing plant that we can actually figure out how it links over time to other kinds of projects so i'll leave this is the last slide that i have is as part of the major main presentation because i wanted to leave time for questions so i wanted to leave some takeaways so if you are a technologist either within the manufacturing operation or in an ecosystem company vendor that supports them there's some things we found that are really differentiating between those that are very successful and those that are less successful and the first one is you have to be able to move quickly without falling into that scheduled trap of oh my gosh i can't get behind schedule early on projects tend to be a bit behind schedule because people had over over rose the expectations of what was possible you need to focus on the problem and you need to be able to figure out how you're going to measure that value and you need to think about how you can do that in a minimum viable approach you can always add on bells and whistles over time as you become more comfortable with the base solution it's much more difficult to take a very complex solution and deploy it in a way that you can achieve all of that value upfront we've not seen that happen very very successfully by too many companies you need to aim not just for buy-in but the engagement that i was talking about and you need to find and nurture a tribe and and what is a tribe a tribe is a group of people who come together with a combination they often organize in less formal ways so they're not necessarily all answering to the same functional silo they're often not all of the same pedigrees or background or experience and this tribe comes together because they have a belief that this smart project can really make a difference you need to nurture that tribe because that's the group that actually creates the the bridges the barrier spanners over time and you need to think about not just who's leading the initiative but who's going to influence it and how do i bring those influencers on early so that they can become engaged and influence the rest of their colleagues who need to be bought into this over time you need to cultivate what we call connection bridges and once again i've got very deep descriptions of these in the backup but the connection bridges are really the people who bring an interesting view but they also bring a view over time across functions and often across organizations and and we have heard from technologists particularly vendor technologists that understanding who the connection bridges are is critical to a to accelerating successful deployment of smart projects and then finally you need to embrace what we call connection independence these are the people who are skeptical who bring a skeptical point of view and who question things in a way that moves the project forward because we've considered things that might not work at the beginning so if i have a very rosy view of the project i tend to miss where i might stumble these independence actually help in creating a lot of the upfront planning and upfront discussion that we've heard is so important to these successful projects so with that i will open this up to to discussions and hope we can have a an open discussion so how would you best like to organize this discussion i mean thank you thank you very much that was a great job we had lots of comments in the chat in the q and a say unfortunately pay attention to the man present so i missed a lot of those comments no well and that's fine because the way we're going to do it is um the the q and a channel has been used so i've got a i've been keeping a collection of uh or adding to the collection of comments for you so i'm afraid it's it's just you and me but the the comments are from all over the place so um i'll do my best and even though you've allowed us uh a plenty of time i'm not sure we'll get to all of them but okay see just somebody has asked can i put up the questions while i answer questions so let me go to those questions there's the questions okay yes right so um yes so that's that's those but before i finish thank you very much for your for your thoughts and uh and your presentation today very interesting lots of people saying there's so much information in here just to repeat all the slides will be available to everyone who's uh who's here today and has registered um during the course of next week probably wednesday next week we also have a report uh behind this that's i don't know 60 or 70 pages that companies are welcome to request and i will be happy to send out that's great that's great to question her so let's go straight into the question the first one uh first one that came in is um do you see a gap or a disconnect between a technology expert and an industry domain expert and if so how do you close that gap oh yeah that's the well in smart projects that's the iqot divide that we always talk about right most of these smart projects require not just understanding a deep understanding of the process but understanding how information technologies can support changes to that process but also some of the vulnerabilities that information technologies introduce so you have to have the security and you have to have thought about it up front you have to understand the the linkage between different databases we're finding that even simple things like i have the wrong labels we call things differently across functions and so you need to have a lot of those discussions up front so that you're bringing these different groups together who are going to use the smart technology and they're having discussions i'll use a very specific example from this large company we're working with um they found that even when they talked about inventory uh they were we talking about growth or net where we talk so there are just little things that don't sound like hard problems but when you start trying to say what my dashboard should look like then you really have to have a single source of the truth so we find that the earlier the discussions happen the better so that's how to bridge it but there's definitely a gap um very few we call them DevOps doers very few people are really highly skilled at both it and ot the opera the specific technologies i mean specific processes if you have one boy hold on to them because they're they're as precious as gold right now right that's good to know and on the on the using different labels point um talked about um uh a comment stroke question really there are three open group standards omi um let's see where are we it's it's moved up in the in the list now um we we have some internet of things standards inside the open group omi um and odef being odef in particular being about uh getting the consistent labeling uh have they been used or do you are you aware of those and if not we can help you out in how we spend a lot i personally don't spend my time doing a lot with standards i have colleagues who are deeply engaged we have them deeply engaged with open group in the open process automation forum for example yeah and in the emerging one around um uh modeling in the in the petroleum industry but but so we believe heavily in standards open technologies open source kinds of things can only work with standards uh so so we're a very strong proponent of that it's sort of the unsung hero because no offense standards take a long time to develop and they're not really exciting or sexy and so they don't get the attention that they should in many companies uh and so when we talk to companies we talk about that as sort of a a table six if you don't have the standards you're not going to have interoperability right right now odef was the one i uh i omitted but well one of the things that we're doing is we're trying to create standards quicker and make them sexier so maybe maybe we can fix that maybe we can fix that so um next question what's a reasonable time for failing fast oh boy um well let me let me back up from that because what we've been told is that because many of these early smart projects in particular the last ones who've been stood up in the last couple of years they really were stood up by people who were really well meeting very enthusiastic but the senior leadership tends to see those as being black holes like the money pit i have to keep throwing more and more money at it and the reason that happens um is because i the technologist didn't adequately anticipate the infrastructure support needs and so when you say how long before i i stand i i claim defeat and say this didn't work um very few projects will be scrapped 100 percent but but teams have to range pivot but the early teams you saw a lot more pro smart projects just being scrapped because they couldn't make it work they couldn't make it work within their system they couldn't make a talk to other things it became a terrible sink of subject matter expert time we're seeing less of that as companies get better at deploying smart projects we see more pivots and shifts than we see a whole scale throw it out right so i mean a related question um is one of the origins of the problem the original project specifications and the scope of the work absolutely almost almost 100 that or i've chosen a vendor that i'm really comfortable with because i've been working with them for decades but that may not have the specific expertise to stand this up so i i rely on a vendor uh that i have a deep relationship with who might not be the right person or i look for a a i look for a piece of a solution from a vendor but i haven't adequately thought about what other things that piece will have to be integrated with essentially a box of Legos if i just dump the box of Legos on your desk i haven't really given you value and so those are the two places we see a lot of companies struggling right now let's see you talked about the importance of the relationship with with vendor do you have any guidelines for creating that type of that type of relationship for these projects we actually do have some very specific um suggestions that that uh technologists gave us about how to be a good partner if i'm a manufacturer or how to appear as a good partner for a manufacturer if i'm a vendor and it almost always relates to uh am i providing value am i describing value in in in concrete terms not just in technical process terms uh am i willing to work with you to help scope the project am i willing to talk with you about some of the pitfalls in fact what we're finding from an intel perspective at least is we spend so much of our time in an advisory capacity rather than in a sales capacity because it turns out that the advisory capacity is essential right now and she'll companies get more comfortable and then the other thing technologists have told us is if they if your vendor can't show you something concrete that don't be fooled by slideware right yeah okay um let's see um question uh wonderful overview lots of insights can you explain a couple of failures or situations where not meeting the desired results um where you didn't meet the desired results and explain why it happened and how it was put back on track where others in a minute or less um we have a few we have a few minutes actually but but yes it's about five minutes left right so um i'm trying to think of a good example so i'll give you an example of one that was the failure and i'll talk a little bit about why that was the failure and i'll refer to this large company again because we're doing good we're working closely with this company uh not on a for-fee basis we're working to really understand what the digital digitization process really looks like from the inside of a company so it's almost a piece of our research like a deep dive case study right and and i'll give an example since i didn't mention the company or the sector i can do this um they bought in a brand new piece of equipment that they work with the vendor who actually had deep process knowledge in their industry not quite as much savvy around the it end of things they brought this in and this piece of equipment six months later is still sitting off to the side not integrated into their process not plugged into the other data systems and and the main reason the main reason why and they're not sure they're ever going to get it off by the way and this is this is a company that's a big this is a big expensive piece of equipment but but several things happen number one they looked at the ROI with only that point solution of what where it was going to be plugged into their line they didn't look at how that ROI was going to impact the rest of the line so that's one the second thing they didn't do is they really that data labeling problem became a really serious issue so if i don't create one source of the truth then my end user my manufacturing staff can't create really robust dashboards that are customized to their specific needs and so they found out that even when i had this thing plugged in i might be able to get some good data from it but i really couldn't integrate that data into business insights so so that so so how are they going about it now they're actually taking a very long term view of their equipment which by the way is going to be in service for probably 30 years minimum and they're saying what do i need to do now and how do i future proof this in such a way that i'm always driving business value so they changed the conversation going forward but i don't know if that was ever that one piece of the equipment i just talked about i don't know if it'll ever be fully integrated into their into their planning right right you might be giving some clues about the sector with that with those as well um uh you described a number of blockers with technology like poor documentation would you say these problems are general to all iot technologies or are some worse than others or put it put another way are some technologies particularly problematic from this perspective yeah i think i think at this point data center technologies are pretty well understood we've been using data center we've been using cloud kinds of technologies for quite a long time relative to edge technologies for example um certainly edge technologies which are the hot thing right now don't have enough non marketing non jargon kind of information around them 5g communications technologies you're going to see a real heat up between cloud service providers and telecom providers i don't know that i don't know that manufacturers and technologists who need to consider these alternatives i don't know if they have the language to actually ask the good questions or to understand the explanations that are given so those are the those are the couple that i think are pretty problematic right now okay and we have time for for one more question i think um let's see there are so many um i have to answer offline too if you want to okay that would that would be very helpful if you can um okay you mentioned the importance of tribes do you see value in formalizing these in organizational structures like in the spotify model no right great short answer i don't it's because as soon as you formalize them you create a who belongs and who does not and that assumes you know what that smart project or set of projects are going to need over time and that you've anticipated them all up front what we're finding is that's just not a very good model that these really require um learning across functions learning across expertise levels and expertise domain um the big company that we're working with actually called there's the trailer tribe they had a trailer on site that these people worked in and learned from each other including consultants they brought in and and so we we think these tribes tend to evolve over time as the projects themselves evolve right right that's a great way to end iran really appreciate your your information and sharing your experience and imagine a big round of applause coming your way from from the it's so thank you very much appreciate it i appreciate it


We'll come back for more XO ZEERS content today's video. We're going to discuss the top XO ZEERS tips for 2021 So you've heard it. It's already 2021 and it's the first day of the year happy new year to everyone if you've noticed that my display has changed so for comments and suggestions, please write them down below and When we're talking about Tips for the game for beginners especially This is or this would make them or help them catch up with players who started way before But way before today, especially during the launch global launch Korea launch and this would give them more efficiency in playing the game So to start off we'll have SES efficiency first So ZES efficiency is tied to your currency called ZES So here it is. So ZES is actually being used Mostly in summoning or recruiting for heroes So here you go for summoning of heroes and for acquiring Heroes or fate course or fate of the heroes or all kinds of heroes. You'll need this so period My advice to you so that you won't be able to you won't be able to be delayed and this one is try to prioritize using ZES Number one ask the community that you are in for extra exos heroes for advice on what to build or What to prioritize once you've summoned your first couple of heroes? Also try to prioritize these gold Fate course if you want to summon but not the black fate course at the meantime because you're starting out the game Try to more or less save your ZES for the best Fate course available. They're usually gold or sometimes they're blue, but I'll try to focus more on On gold for the meantime for the other Components it would really depend on really on what or which characters would you want? To to carry the game again. Ask for advice before you build your team ask people ask people in your group your air squadron because When you start the game ZES would be pretty much very available very available a lot of sources But as you progress in the game it it's availability actually goes down So I would suggest also that you don't pull on this one the string of creation re because this is actually a frustration for beginners and you would really consume your ZES here So much that you'll be you know most players Are not are no longer Are no longer interested in playing the game So this one I would put a pause on first no string of creation re for the meantime so again Try to get some tips from the group that you are in and Or if you don't have a group try to join a group for this one the next would be Team building flexibility since since you guys will be starting today You pretty much have enough flexibility In order to build your team because way back when I joined the game during global launch at around end of May or beginning of June The the meta was not yet really formed and right now Once you join the game there are there is a lot of heroes that you can select the meta is also is already established kinda with a little bit of movement and Now you it would be easy for you to prioritize which heroes would you want to pick and build on? So that is your advantage now as beginners team building flexibility The next would be unlocking PvE PvE content rather than PvP So for PvE content, I'm actually referring to your daily quest your campaign here and especially your challenges especially here during growth because most of them the materials that you'll be needing Will be here in growth. It's named growth for a specific reason because you need to be able to progress as far as possible So here we are at a sample of one of the challenges in growth. So for every stage Every requirement is actually, you know, it actually increases up to the last stage So the the requirement would be heroes and how strong they are and the war and the rewards actually increase from simple equipment up to You already have star fate here and you have This is your star crystal. So these these these what do you call this these? Materials you will be using in leveling up improving your characters or heroes you will be equipping them you will be Leveling them with materials Blessing enhancements and whatever things you do with your heroes. So it's important that you unlock challenges first Okay Also, my next tip would be focusing on one signature force So for signature force, if you want to see signature force You can actually take a look at signature force here. So try to focus on Developing one signature force first because it would be you know, it would be It would be rather very hard for you to to get a lot of general shards or or summon heroes or general heroes More than more than one And also try to check your existing roster on which faction that you are You currently have and focus on the generals that you should get for that faction. For example for Greenland You will need Bathory to enhance Greenland. So you have to have Duplicates of Bathory for you to get enhancements going up to level four As of now, so you will have you would want to reach this one if you have if you have mostly Greenland heroes You can check out the green hat Greenland heroes in their sorting When once you you you sort your heroes according to nation So you would want to reach this and you would want to reach copies Sorry, you would want to get copies of generals for you to level up in signature force Okay, so the next one would be for me for golden clocks This actually is applicable to everyone not just beginners, but also for advanced players because your golden clocks will be used in recruitment of your generals, okay generals in particular and and And other heroes I'm saying generals because before you recruit for your nation recruit There's this this thing called your mileage So for your mileage for for for every hundred fifty mileage You get to get a special fated hero selection ticket So you can select a general from this ticket provided that you accumulate 150 tickets before you pull for at least one or before you pull for everything because The mileage actually only is good for 30 days once once The third 30 days is up the mileage is going to reset So try to make sure that you have first 150 before you pull to ensure that you can get at least one copy of a general For nation recruit. Also, I'll put a link up there for my Farming for golden clocks so that you would have more details on how to farm for golden clocks Also are my next tip would be farming for generals early Because you will be starting today It's it's very important that you farm as soon as possible So nation recruit tickets would be one the next would be you're in the market for heroin store So if you can see here, you can actually get shards of generals so that you can put in your signature force and You actually can Farm them, you know slowly, but surely also you also have it here Under the currency for beads of greed so you can actually get shards as well and also you can get General heroes in your door to creation when you fuse or when you What do you call this once you re-recruit a hero? fated heroes you can actually put here one and two and Once you combine and convert there's actually a chance that you'll be recruiting generals here So let's skip this and See what we've got So not a general it's Emma But you can do this as long as you have enough fated or extra fated once you've leveled up your fated heroes already the the last one would be in your Eternal bond. So this actually is just a log in log in reward for 30 days So currently I have a bond with Dorka. So if you can see my progress Once I've finished the 29 consecutive days or 28 20, sorry the 30 at 30 consecutive days Dorka will be given to me as a reward So it's important that you log in for 30 days then once this is done Another set of heroes for eternal bond will appear So Also, try to check out my video on how to farm generals. I'll put it up there for you to check out next would be Um, not every beginner will have will have good, you know good heroes to use Um, you'll have to be contented with some of the heroes that you get But it's also important that you check out some hidden gems and My your question might be where to find this hidden gems of heroes check out my video as well I'll put it on the link up up there so that you could check out my hidden gems So we I have two videos for that one part one and part two um My next tip for you guys would be the daily grind It's always important that you establish yourself a routine for the daily grind Making sure that you've covered everything that you have Acquired everything that you have finished everything especially when you're here already in the world So you have to check your campaign your daily quests your challenges Uh, even even the palace Even the airship you have to establish a routine on how to go about and finishing all the tasks that you have per day and also Don't miss a day um, just just just don't miss a day because You're trying to catch up on the other players that have played the game already Don't miss a day play every day get the login rewards Do the eternal bond because once you miss a couple of days you miss on additional materials Or things that you will have to you know that you're grinding every day Every day is actually a chance for you to improve on your roster improve on your heroes improving your gear um and everything so My suggestion is play daily. Do not miss a day And also one other tip is the game is purely on rng So it's to put it simply it's purely luck Even if when you summon four heroes It's actually mostly luck So you have to you have to Make sure that you you know, you don't expect a lot when it comes to summoning Um, especially and also in combat. It's also rng So luck plays a big part in this game one more thing to To to remember which is very important is to choose Your squadron well Because your squadron will actually help you in your progress as well They will help you in acquiring zest, which is a big part Um, they will uh, your attendance here will give you rewards For your premium. This will give you golden clocks And for your normal donation, this will give you levy stones so um Choose a group that is active shoes and shoes and air squadron with which communicates on on a daily basis constantly and helps each other because um Currently my air squadron. I've been here since probably I started the game And I love the the synergy that we have in terms of um, completing raid completing battle Because part of the part of your participation for air squadron is your raid and your air squadron battle and Also, they have this thing called your air squadron hideout in which uh, you'll be able to share things with your fellow squadron members Just to remind you guys about the game The game is about being patient patience Getting towards your ultimate goal because this is not a game that you can finish in one day or even one week or even You know even in a month Probably if you spend a lot of you know a lot of zest you could you could hasten your progress But for those who are free to play The game is played because of you being patient Being patient of your rng your luck your progress Acquiring heroes eventually you'll be able to get them all as long as you have the patience And as long as you Like to grind the game Uh on on a daily basis as a beginner the content is pretty much So varied that you would be very busy in playing this game And also my last point is Enjoy playing this game because some of the beginners that I've seen Usually would like to you know re-roll Re-roll your first hero. It's actually not a problem, but my point is This game will eventually give you Uh all of the heroes that you need So I actually made a made a Uh made a mistake in summoning for the wrong hero when I started But I ended up you know getting the heroes that I wanted eventually and I even corrected My wrong start in this game So for all the beginners out there who are starting exos heroes this These are my you know my Tips for you if you want to ask me for more questions You might want to put that in the comment section or try to check out the description I have there a facebook group for chat in which you can share or ask tips from all of the members and also from me Anyway guys again happy new year I hope this video has helped you and also encouraged you to play exos heroes today or starting today Um hope 2021 will be a better year for us This is the warden you all stay safe and I'm out