Why Kubernetes Is Vital for Moving Cloud Native Technologies To the Edge

Many businesses and developers want to replicate the perks of cloud-native development near to the data source. As a result, the adoption of Kubernetes as a critical component of edge computing is growing. However, several challenges threaten to act as a roadblock to Kubernetes adoption, such as the presence of legacy systems that are not in harmony with containerization. Let’s look at why Kubernetes promises to be a vital cog in the edge computing ecosystem and the challenges that lie in the path to Kubernetes adoption.

Not all edge computing environments are amenable to cloud-native technologies, but extending cloud-native technologies to the edge will benefit small-to-medium-sized Linux-based compute nodes used to aggregate business-critical data and have enough memory to support virtualization. In addition, they stand to gain from cloud-native development practices that can continuously update and reconfigure edge applications.

During the shifting of workloads to the edge, what’s needed is a standard operational paradigm that can automate processing and instruction execution as operations and data flow between edge devices and cloud. Kubernetes provides this shared paradigm for all deployments in the network, making applying policies to the entire infrastructure easier. Kubernetes’ cluster-based architecture and self-healing capabilities make it the cloud-native technology of choice for processing of data at or near the edge.

Why Kubernetes?

Kubernetes is an open-source ecosystem that enables the automation of deployment, scaling, and monitoring of containerized applications.Like its application in the cloud, Kubernetes can run containers at the edge, letting DevOps teams spend more time augmenting resources and less time merging heterogeneous operating environments. Kubernetes’ universal control plane works with any underlying edge infrastructure, simplifying workload deployment and management across varied edge environments. It can balance traffic and minimize latency, a requirement of all edge workloads. It also makes regular updates for edge applications easier by acting as an environment for deploying DevOps CI/CD pipelines.

Kubernetes can manage thousands or even millions of connected devices, sending terabytes of data and accessing services such as real-time analytics. Since it uses automated management, Kubernetes can respond quickly to any changes at the edge. It can scale applications up or down as per the demand, restart failed applications, balance loads by shifting workloads between different servers in a cluster, or reroute traffic to an alternate site when a specific edge location goes offline. In addition, it can deploy new application versions or new containers as needed. Workloads run independently and can be restarted or recreated with no effect on end-users and operations. What’s more, its ability to manage applications running on multiple clouds across different providers’ infrastructure ensures scalability beyond the edge.

Learn More: Why Managed Kubernetes as a Service Should Be a Part of Your DevOps Strategy

Architectural Patterns for Kubernetes at the Edge

To handle containers at the edge, a highly fault-tolerant architecture is required. There are many possible ways to build an edge architecture. For example, it can run solely on end-user devices or as a traditional data center populated by conventional servers, which happen to be closer to end-users than traditional cloud data centers.

 Edge infrastructure has three layersOpens a new window . The first layer includes a centralized cloud and data center. The second layer is responsible for data aggregation and transfer of data between cloud and edge nodes. Finally, the third layer or the last-mile edge layer acquires and processes data at the edge. Kubernetes is the standard orchestrator between all three layers. Thus, in a Kubernetes edge computing platform, edge nodes are simply an extra layer of IT infrastructure alongside an organization’s cloud and/or on-premise data center architecture. As a result, admins can replicate cloud-like automation in the management of workloads at the edge layer.

Three critical factors are essential for success when working with edge workloads and interconnecting them to cloud services: low latency, data privacy and bandwidth scalability. Typical architectural patterns that show how Kubernetes is used to meet these requirements include:

Kubernetes Clusters at the Edge

This pattern is for production in resource-constrained deployments, which involves edge nodes with low capacity resources. It uses a lightweight Kubernetes solution like k3s to implement a minimal version of Kubernetes in a single server. Here, the whole Kubernetes cluster is deployed with edge nodes. Thus, K3s essentially shrinks the Kubernetes footprint to ensure the same benefits are available to more limited compute clusters at the edge.

Kubernetes Nodes at the Edge

When the infrastructure is limited at the edge, this architectural pattern uses Cloud Native Computing Foundation (CNCF) project KubeEdge to place a Kubernetes node at the edge. The main Kubernetes cluster resides in the cloud or the on-premises data center. Here a Kubernetes control plane manages the edge nodes, including containers and resources from the cloud, enabling support for varied hardware resources at the edge.

Learn More: Why Are Tech Leaders Placing Their Bets on Kubernetes Control Planes?

Challenges of Extending Kubernetes to the Edge

Several challenges must be dealt with before Kubernetes is to become a full-fledged edge computing platform. First, many industrial operators still run legacy applications critical to their business but are not in harmony with containerization. Even though these apps will run on VMs, the data center solutions that can support VMs and containers cannot accommodate the smaller footprint of IoT edge devices. Another limitation of Kubernetes is its inability to accommodate individually diverse security considerations of various IoT edge compute nodes. Finally, there is a cultural shift in moving to a cloud-native development environment. Switching to a system of modularized applications and continuous delivery can become a challenging transition for developers accustomed to working with control systems isolated from the Internet and updated only if necessary or for those still using a waterfall development model.

While Kubernetes has the potential of becoming the preferred edge computing solution, it is still missing some essential functionality, including device discovery, governance and data management. It cannot quickly and easily understand all the resources (and their capabilities) available in the edge infrastructure.

Kubernetes requires the capability to help admins define the placement and prioritization of workloads based on the troupe of nodes in multiple physical locations instead of individual nodes running in the exact location.

 It needs to determine where jobs need to run across multiple clouds and edges based on workload and governance requirements. Ideally, developers should have the luxury to introduce containerized workloads anywhere they want in the cloud-to-edge continuum. The platform should provide a standard interface for managing across the edge, a public cloud or a data center, to enforce governance and compliance.

Another missing feature is sufficient multi-cluster management; Kubernetes support it, but it is not that strong a feature. As a result, it is a challenge for organizations running separate clusters in each edge location to isolate workloads and keep management of large-scale environments simple.

Developers often face the challenge of achieving data transmission with low latency between central data centers and edge locations. Kubernetes on its own will not optimize data transfer. Deployment of Kubernetes with data fabric solutions can solve this problem, but integrating them is a challenge. However, developers can achieve it by making it easier to tell Kubernetes which internal traffic is more critical and needs to be prioritized. While Kubernetes can balance traffic coming from external endpoints, it is not necessarily that proficient in handling internal traffic.

Learn More: Cryptominers Are Using Hildegard Malware to Target Kubernetes Clusters

Are We There Yet?

Cloud and edge will go hand in hand in the long run, with workloads and applications at the edge that require high bandwidth,low latency, and strict privacy. As more organizations adopt and endorse Kubernetes-based cloud-edge paradigms, the ecosystem will mature even further. What is needed are edge platforms that can offer DevOps teams tools to make the most of Kubernetes without any of its complexities.

There are now over 500 companies contributing to the Kubernetes projects. So eventually, all of Kubernetes’ challenges can be solved, as long as developers are willing to invest their efforts in making it the full-fledged edge computing platform it promises to be.

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