{"id":269146,"date":"2023-09-11T12:33:36","date_gmt":"2023-09-11T17:33:36","guid":{"rendered":"https:\/\/www.webscale.com\/?p=269146"},"modified":"2023-12-29T15:30:53","modified_gmt":"2023-12-29T20:30:53","slug":"roadmap-to-the-adaptive-edge-balancing-cost-efficiency-usability-and-performance-tradeoffs","status":"publish","type":"post","link":"https:\/\/www.webscale.com\/blog\/roadmap-to-the-adaptive-edge-balancing-cost-efficiency-usability-and-performance-tradeoffs\/","title":{"rendered":"Roadmap to the Adaptive Edge \u2013 Balancing Cost Efficiency, Usability, and Performance Tradeoffs"},"content":{"rendered":"<p><i><span style=\"font-weight: 400;\">The network edge presents new challenges in optimizing application deployments, as the deeper an application is pushed into the internet, the greater the associated costs to run those workloads. As part of Kubernetes on EDGE Day at KubeCon + CloudNativeCon event in Europe, Webscale shared a set of optimization considerations and strategies that allow workloads to be as close to users as possible, bounded by a business value construct. At the same time, we discussed the importance of maintaining simplicity in the usability of the system, so that developers can specify how deeply they want their application to run without overallocation of resources.<\/span><\/i><\/p>\n<p><span style=\"font-weight: 400;\">This blog post presents an excerpt from Webscale\u2019s presentation, discussing the use of Cloud Native Computing Foundation (CNCF) core technologies, including Kubernetes and Prometheus as a base, to establish a framework for evaluating projects within the CNCF landscape and their suitability for edge use cases. The session also examined Kubernetes Federation techniques, multi-cluster orchestration systems, and traffic direction and service discovery strategies against a selection criterion to assist architects in making \u201cgood-fit\u201d decisions.<\/span><\/p>\n<h3><b>Building the Roadmap: Where to Start<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">When we talk about balancing performance against cost, we\u2019re talking about running hundreds of instances of an application at the edge to deliver, on average, shorter distance, lower latency, fewer dropped shopping carts, and so on. But there\u2019s a cost associated with that \u2013 not just in terms of cycles, but in terms of operating all those clusters.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">While you might love to run clusters everywhere, the cost is likely too high for that to be a realistic option. So, how do you get the effect of running everywhere without actually running everywhere? First, let\u2019s establish our bearings by considering some of the \u201cwhat ifs\u201d at play and of note:<\/span><\/p>\n<ul>\n<li><span style=\"font-weight: 400;\"> \u00a0 <\/span> <span style=\"font-weight: 400;\">What if we could be adaptive?<\/span><\/li>\n<li><span style=\"font-weight: 400;\"> \u00a0 <\/span> <span style=\"font-weight: 400;\">What if we could move the workload to where it needs to be?<\/span><\/li>\n<li><span style=\"font-weight: 400;\"> \u00a0 <\/span> <span style=\"font-weight: 400;\">What if we could maintain a set of running workloads that are optimal for particular needs \u2013 optimal according to distance from an end user, optimal according to budget, etc.?<\/span><\/li>\n<li><span style=\"font-weight: 400;\"> \u00a0 <\/span> <span style=\"font-weight: 400;\">What if we could continuously adapt that set of running workload locations \u2013 adapt it according to signals such as health signals, utilization signals, etc.?<\/span><\/li>\n<li><span style=\"font-weight: 400;\"> \u00a0 <\/span> <span style=\"font-weight: 400;\">What if we could use predictive analytics to know ahead of time that we can shut down a workload in one location, start it up in another, and continuously revisit this set of locations and update it as time goes by?<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">That\u2019s the idea. Consider this as a rough outline for the roadmap you\u2019re building for this first solution for an adaptive optimized edge.<\/span><\/p>\n<h3><b>Surveying the Landscape<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Now, let\u2019s survey the existing CNCF landscape and how it can be leveraged here. We\u2019ll use Kubernetes, running a containerized workload, as our starting point.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">There are still other pieces we\u2019re going to need:<\/span><\/p>\n<ol>\n<li><span style=\"font-weight: 400;\">We\u2019re going to need to be able to run our workload on multiple clusters.<\/span><\/li>\n<li><span style=\"font-weight: 400;\">We\u2019re going to need to be able to move workload from one cluster to another.<\/span><\/li>\n<li><span style=\"font-weight: 400;\">We\u2019re going to need a way to solve the problem related to signals and optimization.<\/span><\/li>\n<li><span style=\"font-weight: 400;\">And then finally, we\u2019re going to need to be able to direct traffic. As a workload moves from one location to another, based on the signals, we will need to redirect the traffic to follow along.<\/span><\/li>\n<\/ol>\n<p><b>Multi-Cluster <\/b><span style=\"font-weight: 400;\">\u2013 The multi-cluster problem is well addressed in the CNCF landscape. There are a number of solutions out there; some of them are forks from the Federation special interest group. Karmada is just one suitable example we can use, as it allows you to run and manage multiple clusters without the cognitive load of having to think about managing each cluster, individually. That solution is ready to go.<\/span><\/p>\n<p><b>Moving Workload<\/b><span style=\"font-weight: 400;\"> \u2013 With regard to moving a workload, Open Cluster Management is another tool that lets you manage workloads running on multiple clusters, and administer the multiple clusters themselves. But it adds something different \u2013 it adds a placement decision. This gives you the ability to customize where your workload runs. It\u2019s a code you write yourself and then provide to Open Cluster Management to tell it where a workload needs to be prioritized. Using this capability and the addition of signals (which we\u2019ll get into more below) you can develop the ability to place a workload.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The thing about Open Cluster Management\u2019s placement decision, however, is that it operates on the initial deployment of that workload. It doesn\u2019t do what we want, which is to revisit it on a cadence. And since it doesn\u2019t revisit the decision, you\u2019re going to have to somehow add that capability to open cluster management to rerun this placement, and then actually move the workload.<\/span><\/p>\n<p><b>Signals and Optimization<\/b><span style=\"font-weight: 400;\"> \u2013 This is where we\u2019re left empty handed. While CNCF does have an optimization section, it\u2019s devoted towards optimizing within a single Kubernetes cluster \u2013 all the examples are about optimizing for cost (how to run this cluster in an optimal way, minimizing compute resources, etc.). What we want to do is optimize across multiple clusters \u2013 and we also want to take performance into account. There\u2019s nothing in the landscape that provides that.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">So, what now? Unfortunately, you\u2019ll need to develop a custom solution.<\/span><\/p>\n<p><b>Directing Traffic<\/b><span style=\"font-weight: 400;\"> \u2013 Finally we come to directing traffic, and now we\u2019re back to having a good representation within the CNCF landscape.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Core DNS is a programmable DNS solution that will route traffic from an end user to the nearest location where that traffic can be served. However, DNS has common issues that come into play. For instance, an ISP, as a piece of the DNS stack, may decide to cache a single DNS answer for its entire user base \u2013 and therefore not provide the necessary user location granularity; you really do want each end user to be routed to their closest location, not to the closest location of some other end user in that same ISP\u2019s network.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">And then of course, there\u2019s the issue of time to live (TTL). We want use the capability we\u2019re developing here to make highly available services, but if a TTL isn\u2019t being honored (say you\u2019ve asked for a minute and an ISP is giving you an hour), then that\u2019s the best you\u2019re going to be able to do; in the worst case, your switch over time would be an hour for directing traffic. In short, DNS has issues.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Kubernetes global load balancer, however, brings anycast into play for us. Anycast is a capability built into IP that\u2019s purpose-built to route traffic from an end user to the closest location where that workload is being served. And Kubernetes global load balancer will use Border Gateway Protocol (BGP) protocol to control the anycast capability of IP, which is the perfect solution for what we want to do. You can learn more on this particular strategy in our recent series on<\/span><a href=\"\/blog\/building-for-the-inevitable-next-cloud-outage-part-1\/\"> <span style=\"font-weight: 400;\">Building for the Inevitable Next Cloud Outage<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h3><b>Charting Your Course to the Adaptive Edge<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Now, if you take a step back, what you have developed is a high-level roadmap for all the pieces that you need, pulled from the CNCF landscape, to build an optimized adaptive edge. With this you will be able to balance performance against cost to get an optimal solution. You\u2019ll get the effect of running everywhere without actually running everywhere \u2013 and without the cost of running everywhere.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">That said, while we\u2019ve been talking about performance against cost, it\u2019s also important to note that businesses are unique. Businesses want to put themselves forward in a distinctive way \u2013 chart their own course, so to speak. Maybe you want to prioritize something else at your business. Maybe, for example, you want to minimize your carbon footprint. You could build into your optimization engine the ability to prefer green data centers. Or, let\u2019s say that compliance is important. You could build into your optimization to prefer PCI compliant or other types of compliance. Ultimately, your business can customize the optimization engine as needed to suit your purposes.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Webscale CloudFlow\u2019s<\/span><a href=\"https:\/\/www.section.io\/adaptive-edge-engine\/\" target=\"_blank\" rel=\"noopener\">\u00a0<span style=\"font-weight: 400;\">Adaptive Edge Engine<\/span><\/a><span style=\"font-weight: 400;\"> intelligently and continuously tunes and reconfigures your edge delivery network to ensure your edge workloads are running the optimal compute for your application. If you\u2019re as excited about discovering the adaptive edge as we are, we\u2019d love to chat with you further.<\/span><a href=\"https:\/\/www.webscale.com\/contact-us\/\"> <span style=\"font-weight: 400;\">Get in touch with us today<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The network edge presents new challenges in optimizing application deployments, as the deeper an application is pushed into the internet, the greater the associated costs to run those workloads. As part of Kubernetes on EDGE Day at KubeCon + CloudNativeCon event in Europe, Webscale shared a set of optimization considerations and strategies that allow workloads 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