Enterprise Cloud and Transformation
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Enterprise Cloud and Transformation
Kubernetes: The Modern-day Kernel
Nov 15, 2019


In the lead up to KubeCon + CloudNativeCon North America 2019, we are posting a series of blogs covering what we expect to be some of the most prevalent topics at the event. In our first post, we walked through the journey from the monolithic to the microservices-based application, outlining the benefits of speed and scale that microservices bring, with Kubernetes as the orchestrator of it all.


Kubernetes may appear to be the new software-defined, a panacea like software-defined networking (SDN), famously personified at Juniper by JKitty—the rainbow-butterfly-unicorn kitten. But you know what they say about the butterfly effect. When the Kubernetes kitty flaps its wings…there’s a storm coming. Kubernetes is indeed amazing—but not yet amazingly easy. Along with a shift to a microservices architecture, Kubernetes may create as many challenges as it solves.


Breaking applications into microservices means security and networking are critical because the network is how microservices communicate with each other to integrate into a larger application. Get it wrong, and it’s a storm indeed.


This week at KubeCon, Juniper is showcasing solutions in the areas where we’re best known for engineering prowess: at-scale networking and security.


As luck would have it, these coincide perfectly to simplify the challenges that Kubernetes creates. Let’s look at some a little closer.


The Opportunities and Challenges of Microservices and Kubernetes

A well-architected microservices-based application could shrug off the loss of a single container or server node, as long as there was an orchestration platform in place to ensure enough instances of the right services were active to allow the application to meet demand. Microservices-based applications are, after all, designed to add and remove individual service instances on an as-needed basis.


This sort of scale-out, fault-tolerant orchestration is what Kubernetes does with many additional constraint-based scheduling features. For this reason, Kubernetes is often called the operating system kernel for the cloud. In many ways, it’s a powerful process scheduler for a cluster of distributed microservices-based applications. But a kernel isn't everything that an application needs to function.


In true Juniper form of solving the hardest problems in our industry, we are engineering simplicity — tackling challenges in storage, security, networking and monitoring. We know from our customers and own experience that if you’re managing your own Kubernetes deployments, these challenges need to be squarely addressed in order to successfully manage this new IT platform.


One way to tackle problems is to outsource them. Kubernetes as a service is one approach to simplicity, but it also comes with trade-offs in cost and multicloud portability and uniformity. Therefore, operating Kubernetes clusters will be in the cards for most enterprises—and Juniper is here to help.


Don’t Limit Your Challenges, Challenge Your Limits


Kubernetes operators often deal with its challenges by limiting the size of clusters.


Smaller cluster sizes will restrict the security and reliability blast radius and such pigmy-scaled demands on monitoring, storage and networking look easier to solve than one larger shared cluster. In some cases, each application team will deploy its own cluster. In other cases, each development lifecycle phase—dev, test, staging and production—has its own Kubernetes cluster. All variants aside, many Kubernetes operators are deploying small clusters; for example, 10-20 nodes and a few applications in each one. Yet, Kubernetes can scale a couple orders of magnitude beyond this.


The result of many small clusters already has a name: Kube sprawl.


While there may be benefits of small cluster design, this approach quickly introduces new challenges, not containing complexity but merely shifting it around. The operational challenges of managing many clusters also means the added juggling of more Kubernetes versions in flight, upgrades and patching. Not to mention, more engineers to do all of this or the added task of building your own automation to do so.


Moreover, there is the obvious drawback that since each server or VM node can only belong to one Kubernetes cluster, efficiencies are lost that a larger shared cluster would afford. Resource efficiencies and economies of scale come when there is a great variety of applications, teams, batches and peak usage times.


If a small cluster is just running a few apps, it’s unlikely that the apps will be diverse enough to steadily use its whole pool of resources, so there will be times of waste. Operators can try to make the number of cluster nodes itself elastic like the containerized applications, but this orchestration is difficult to automate and build per the unique demands of applications inside each small cluster. Given each Kubernetes cluster needs at least three server nodes for high availability, that also is replicated waste across the number of clusters maintained.


Many clusters create new challenges for developers. While cloud-native tools, such as microservices tracing, exist to benefit developers, these and other middleware services are generally designed to work within, not across, clusters. Other new tools like service meshes can be more complex when federated across clusters.


Applications Span Edge Clouds and Multicloud, So Too Must Cloud-native Infrastructure


Kubernetes is good at managing a single cluster of servers and the containers that run on them. But some applications will run in multiple clusters to span multiple fault domains or availability zones (AZ). This isn’t multiple small clusters inside the same data center AZ, but rather about spanning data centers for additional availability, better global coverage and user latency.


Solving security, networking, monitoring and storage per cluster is a first step, but strong solutions should deal with the challenges of federating multiple clusters across AZs, regions, edge cloud and multicloud. Here again, Juniper has some market leadership to show off at KubeCon.


Security and networking become more complex in this scenario, as they play a more important role in global application architecture. The network must link together microservices, as well as multicloud. Defense in depth must protect one cluster, but defense in breadth is another requirement needing to equally enforce policy end-to-end in addition to top-to-bottom.


You Can’t Always Leave the Legacy Behind


In the real world of enterprise, the services used by cloud-native applications will range from "that bit still runs on a mainframe" to "this bit can be properly called a microservice." So how can organizations provide a secure, performant and scalable software-defined infrastructure for an application made out of radically different types of services, each running on multiple different kinds of infrastructures and possibly from multiple providers located all around the world?


Here again, solving security, network, monitoring and storage for Kubernetes is all well and good, but what about managing the legacy in VMs and on bare-metal? Many new-fangled tools for Kubernetes end where Kubernetes does, but applications don’t. Moreover, operations don’t and yet another tool for ops teams to learn and deploy creates additional burden instead of reducing it. Technology and tooling today must solve new cloud-native problems, but the best tools will solve the hardest problem today: operations simplicity. This can only happen by spanning the boundaries of new and old, and maintaining evolvability for the future.


Solutions for Operational Simplicity


By this point, it’s easy to imagine what’s coming next. Juniper has been successfully building high-performance, scalable systems for a long time.


Stay tuned for our next blog that is set to explore how Juniper brings operational simplicity to Kubernetes users and beyond. In the meantime, find out more about Juniper’s cloud-native solutions at juniper.net/cloud-native

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