Weighing the top seven Kubernetes challenges and how to solve them

While Kubernetes offers many business benefits, it also has potential pitfalls. Discover the top seven Kubernetes challenges and how to gain control of your container environment.

Kubernetes has become the leading container orchestration platform for organizations adopting open source solutions to manage, scale, and automate application deployment. Adopting this powerful tool can provide strategic technological benefits to organizations — specifically DevOps teams. At the same time, it also introduces a large amount of complexity. This complexity has surfaced seven top Kubernetes challenges that strain engineering teams and ultimately slow the pace of innovation.

What is Kubernetes? And how does it benefit organizations?

Kubernetes is an open source container orchestration platform for managing, automating, and scaling containerized applications.

Containerized microservices have made it easier for organizations to create and deploy applications across multiple cloud environments without worrying about functional conflicts or software incompatibilities. This ease of deployment has led to mass adoption, with nearly 80% of organizations now using container technology for applications in production, according to the CNCF 2022 Annual Survey. However, as the use of containers has grown, so too has the need for more effective management of these highly distributed environments at scale.

To manage this complexity, teams have turned to container orchestration solutions such as Kubernetes. The platform aims to help DevOps teams optimize the allocation of compute resources across all containerized workloads in deployment.

While a handful of these solutions exist, Kubernetes has become the de facto industry standard, offering the following benefits:

• Container deployment. Container orchestration platforms automate daily operations for processes such as the re-creation of failed containers and rolling deployments. This helps to avoid downtime for end users.
• Automated scaling. Kubernetes enables efficient resource utilization by easily scaling applications and services based on demand.
• Self-healing. The platform will automatically restart, replace, or kill failed containers, as well as reschedule unhealthy pods and manage node failures. This key feature helps in maintaining availability and reduces the need for manual intervention.
• Extensibility and technology ecosystem. As an open source solution, Kubernetes has a large, active, and growing ecosystem of extensions, plug-ins, and technologies to enhance its capabilities. The ability to extend functionality across a wide range of use cases allows teams to tailor the platform to meet their organization’s specific requirements.

The top Kubernetes challenges and potential solutions

Despite its benefits, Kubernetes has some potential pitfalls that engineering leaders should consider when managing the complexity it introduces. The top seven Kubernetes challenges include the following:

1. Complexity. Kubernetes environments tend to be complex, multilayered, and dynamic. This creates limitations and blind spots when it comes to observability. Teams often need to know where to look to find issues and resolve them, but that can be time-consuming in large-scale deployments. Comprehensive platform solutions that provide full-stack monitoring with AI at their core can automatically detect anomalies and provide root-cause analysis to prevent future issues.

2. Networking. Large-scale, multicloud deployments can introduce challenges related to network visibility and interoperability. Traditional ways of operating networks using static IPs and ports simply don’t work in dynamic Kubernetes environments. Container Network Interface (CNI) provides a common way to seamlessly integrate various technologies with the underlying Kubernetes infrastructure. Additionally, service meshes — such as those offered by Istio, Linkerd, and Consul Connect — help to manage internetwork communication at the platform layer using purpose-built application programming interfaces.

3. Observability. While there are many observability and monitoring tools on the market today, most are specific in nature. A full-stack observability platform such as Dynatrace provides easy access to the three key types of monitoring signals — logs, traces, and metrics — in context. Automated data collection correlated with topology information, real-user experience, security events, and metadata makes it easier to identify issues and determine effective remediation paths. With the ability to monitor resource utilization metrics such as CPU and memory in real time, teams can optimize their operations, resulting in reduced cost and greater overall efficiency.

4. Cluster stability. Kubernetes containers are naturally short-lived and ephemeral, meaning they’re constantly being created, altered, and removed. This creates challenges in monitoring and debugging distributed applications at scale, often resulting in reliability issues. Effective monitoring, logging, and tracing mechanisms need to be in place to identify and resolve issues quickly. Additionally, ensuring cluster stability through monitoring critical components at the control plane is essential to preventing failures. To minimize negative effects, consider setting limits and alerts on CPU and memory resource requests.

5. Security. Kubernetes security incidents are primarily related to pod communications or misconfigurations that ultimately lead to delayed application deployment. Pods are not isolated, which leaves them vulnerable to malicious actors. Bad actors can use misconfigurations to gain access to sensitive data. However, Kubernetes configurations are complicated, which makes managing them at scale almost impossible. Network policies can restrict pod communications, and teams can use pod security policies to ensure pods are securely configured.

6. Logging. Logs provide critical visibility into the ongoing health of Kubernetes clusters. While Kubernetes makes it easier to generate logs from the various components and layers of a cluster, challenges remain in aggregation and analysis. Solutions that offer log management and analysis capabilities can help to streamline these efforts and provide actionable insights to teams maintaining the health of the workloads and underlying infrastructure.

7. Storage. Containers need to spin up and down easily. Therefore, they are built to be non-persistent by design. However, applications require persistent data to run successfully in production. Traditional storage solutions were not created to address these requirements, which are common among modern deployments. To ease the pain of managing storage at scale within a Kubernetes environment, Kubernetes has released features such as Container Storage Interface (CSI), StatefulSets, Persistent Volume (PV), and Persistent Volume Claim (PVC).

How a cloud-native observability and security platform can overcome Kubernetes challenges

To adequately address the complexity introduced by Kubernetes and other cloud-native technologies, organizations need more complete solutions that provide end-to-end, full-stack visibility, advanced performance and security analytics, and automated workflow capabilities all in a single, comprehensive platform.

Dynatrace integrates extensive Kubernetes observability with continuous runtime application security. This combination helps organizations more effectively meet business goals and minimize risk. The Dynatrace platform offers the following benefits:

• Automated observability at scale. As organizations deploy Kubernetes clusters across on-premises, cloud, and edge environments, end-to-end observability becomes mandatory. Dynatrace OneAgent and open ingest deliver the deepest and broadest observability on the market, with hundreds of out-of-the-box integrations covering the complete Kubernetes ecosystem. Dynatrace Grail unifies observability, security, and business data at a limitless scale for any analysis at any time.
• AI-powered analytics. Dynatrace provides precise and explainable answers in real time, identifying performance issues and anomalies across the full Kubernetes technology stack. Davis, the Dynatrace AI engine, helps organizations understand and optimize Kubernetes platform health and application performance, enabling IT teams to proactively pinpoint and rectify performance issues.
• Platform and application security. Optimized for Kubernetes, Dynatrace Application Security automatically and continuously detects vulnerabilities and protects against injection attacks that exploit critical vulnerabilities, such as Log4Shell. These capabilities remove blind spots, ensuring development teams aren’t wasting time chasing false positives and providing business leaders with confidence in the security of their organizations’ applications.
• Acceleration of innovation. Dynatrace enables IT teams to shift their effort from code maintenance and troubleshooting to innovation. Leveraging Dynatrace’s analytics and workflow automation enhances software quality, minimizes the mean time to resolve anomalies, and detects security vulnerabilities in near-real time.