Mastering Kubernetes Management: Challenges and Best Practices

Kubernetes empowers you to reliably operate and scale cloud-native apps, but it can be daunting to manage your Kubernetes clusters and their associated infrastructure resources. The need to maintain consistent configuration, enforce correct security policies, and gain clear visibility into your clusters and their workloads is an acute issue that should be addressed before you start using Kubernetes in production—otherwise, you’re liable to suffer from usability challenges or even costly compliance breaches.

This article will explore these problems and share best practices to help you effectively manage Kubernetes in your cloud environments. Afterwards, we’ll explain how Rafay’s enterprise Kubernetes management platform helps you standardize your K8s processes and achieve continuous governance. Let’s get started.

Understanding Kubernetes Management

Kubernetes management is the discipline of designing, implementing, and maintaining strategies that grant you cohesive control of your clusters. The complexity of administering the Kubernetes control plane, worker node fleets, and other components such as networking infrastructure and storage volumes demands a unique management approach so you can visualize where your resources are located and ensure consistent configuration.

Effective management is also an essential step towards unlocking the full efficiency potential of your clusters. Gaining centralized oversight of your Kubernetes infrastructure makes it easier to gauge cluster utilization, for example, allowing you to scale down over-provisioned clusters to realize cost savings.

Many teams start managing Kubernetes using standard tools such as kubectl, kubeadm, and the controls offered by cloud platforms such as Amazon EKS and Google GKE. But these facilities are primarily designed to support cluster and worker provisioning tasks, not ongoing monitoring, security, and compliance. They don’t scale to multi-cluster scenarios where you’re operating several clusters across different clouds, so it’s important to implement your own solution to achieve coordinated multi-cloud, multi-cluster, and multi-tenant Kubernetes management.

Kubernetes Management Challenges

As we’ve outlined above, Kubernetes cluster operators often find it challenging to implement a robust management approach. Some common problems include:

• Scalability Issues: Kubernetes makes it easier to scale workloads, but managing auto-scaling, right-sizing, and efficient use of different node instance types isn’t always straightforward. Even when auto-scaling is enabled, improper settings can lead to resource wastage and excess costs.
• Multi-Cloud Management Complexities: You can increase redundancy by creating clusters that connect nodes from multiple cloud providers, or by provisioning multiple clusters in different clouds or regions. However, this often leads to networking, access management, security, and monitoring headaches, as well as difficulty handling the subtle behavioral nuances that exist in each cloud’s Kubernetes implementation.
• Security and Compliance Concerns: Kubernetes security is complex—it’s important to set up zero trust mechanisms including RBAC (role-based access control), etcd encryption, networking policies, and security admission controllers, but this can be intimidating and difficult to administer at scale. Kubernetes lacks built-in tools to audit whether protection is correctly configured.
• Resource Optimization: It’s important to optimize the use of available cluster resources by setting appropriate constraints on your workload deployments. Yet it’s often difficult to accurately gauge how much CPU, memory, and storage your workloads actually require, or track how this changes over time.
• Monitoring and Logging Difficulties: Clear visibility into cluster operation is a prerequisite for making informed optimizations around performance, security, and compliance; visibility also provides vital context to developers and operators working to debug problems that occur in the cluster. It’s often challenging to collect and utilize this data, especially when multiple clusters or clouds are involved—Kubernetes doesn’t include built-in observability capabilities.

None of these challenges are insurmountable, but they should be acknowledged before you embark on your Kubernetes management journey. Now let’s take a look at how to solve them using best practices and purpose-built tooling.

Best Practices for Managing Multiple Kubernetes Clusters

The following methods will help you to manage multiple Kubernetes clusters efficiently and effectively. While this isn’t an exhaustive list of techniques, it provides a starting point that ensures you’re covered for the most important fundamentals. You can check out our whitepaper library to find more Kubernetes management guidance.

Implement Robust Security Measures

Comprehensive security measures must be a key component of any Kubernetes management strategy. This applies whether you’re operating a single cluster or a fleet of hundreds, but multi-cluster scenarios are more complex and will demand tools that can support you in auditing and strengthening your defenses.

At the most basic level, you should be using mechanisms including RBAC, Pod Security Admission standards, and network policies to prevent unauthorized cluster access and maintain consistent workload security standards. It’s also important to set up a secrets management solution—such as AWS Secrets Manager or HashiCorp Vault—to ensure API keys, tokens, and credentials are kept securely and then safely disseminated to your clusters when required.

Beyond this foundational layer, use of cloud security posture management (CSPM) tools such as Wiz and Check Point allows you to detect and remediate live threats in your clusters, irrespective of cloud provider. Dedicated security suites provide the tools to triage, monitor, and resolve new vulnerabilities everywhere they’re detected, without making you hunt across different clouds. If you’re not operating at a scale where CSPM makes sense, then regular use of a self-contained scanner such as Kubescape can help you find misconfigurations, known vulnerabilities, and compliance breaches without the weight of a separate cloud platform.

Ensure Consistent Cluster Configuration

Consistency is key to Kubernetes cluster management success. Naturally, there’ll always be some differences between your clusters: they’ll run different workloads, may have unique node types, and might belong to separate cloud accounts. Nonetheless, many of your clusters will have much in common with each other because the same security policies, governance frameworks, and observability platform connections will generally apply to them all. This sharing will also include many cluster-level settings, such as the API server options that are enabled.

Maintaining consistency as your cluster fleet grows depends on the integration of management tools that can solve these issues for you. It’s possible to partially achieve this yourself, such as by using the Cluster API to reproduce cluster configs on different clouds. However, dedicated platforms like Rafay make it much easier to create clusters from templates, centrally manage all the clusters you’ve created, and then deploy config changes across your entire fleet.

To mitigate the risks of developers taking actions that cause config drift, consider exposing cluster access as part of an internal developer platform (IDP). Self-service platforms grant devs autonomy while protecting your clusters from unauthorized access and modification, making it easier to maintain consistency.

Establish Continual Monitoring and Logging

Observability suites reveal what’s happening in your clusters, letting you track resource utilization and investigate the root causes of problems. However, it can be difficult to coordinate access to metrics and logs when you’re working with multiple independent clusters at scale. Once again, the end objective should be a centralized platform that gives you one place to collate, search, and analyze trends in fleet observability data.

Thanos is one of the leading solutions in this space. It scales the popular and familiar Prometheus time-series database—often found in single-cluster monitoring scenarios—to support consolidated querying of multiple datasources. Once you’ve set up a Thanos instance, you can install Prometheus in each of your clusters, then connect them all to Thanos to inspect collected metrics.

Whichever setup you use, real-time continuous logging mustn’t be overlooked when you’re operating Kubernetes. Although faults in a correctly configured cluster should be rare, when an error does occur then missing observability data can make it much harder to apply a timely resolution.

Utilize Automation to Maximize Scalability and Efficiency

Automation is the best way to optimize efficient cluster management. This applies equally to provisioning, scaling, and maintenance operations.

Utilizing IaC tools such as Terraform lets you declare how your clusters should be configured, then automatically apply the changes necessary to achieve that state. This method lets you rollback to previous iterations, or easily start a new instance of a cluster based on existing configuration.

Cluster scalability can be enhanced by enabling cloud provider auto-scaling options. From Azure Kubernetes Service (AKS) to DigitalOcean Kubernetes (DOKS), all major managed K8s services include auto-scaling to automatically add and remove nodes as utilization changes. This can prevent incidents caused by resource exhaustion in times of peak demand; it also helps minimize your operating costs by letting clusters scale down when use subsides, all without requiring any manual intervention.

Effectively Leverage Multi-Cloud Platform Capabilities

Several platforms are available to ease the set up of multi-cloud workflows, with many specifically designed to support Kubernetes requirements. These services deliver an all-in-one approach to cluster administration by integrating config management, cross-cluster networking, and security controls into a single solution.

Tools such as Rafay consolidate your clusters into a single logical environment that you can manage from one surface. Taking the time to implement a multi-cloud platform can deliver huge savings throughout the lifetime of your clusters by providing convenient access to automation, self-service environments, and config standardization.

Furthermore, it’s important to continually assess which cloud platforms best satisfy your Kubernetes infrastructure requirements. Effective use of multi-cloud requires each workload to run on the most suitable cloud—too often, teams end up defaulting to a previously used provider, even if a better alternative is available. Once you’ve implemented a multi-cloud management platform, you’ll be able to efficiently optimize placement for your clusters and associated resources, without being constrained by historical precedent.

Rafay’s Advanced Solutions for Kubernetes Management

Rafay is an enterprise PaaS solution that provides a complete multi-cloud, multi-cluster Kubernetes management platform. Built for platform teams, Rafay empowers you to centrally manage Kubernetes infrastructure, take control of security and governance, and facilitate simple self-service cluster access for developers and cloud users.

Benefits of using Rafay for Kubernetes cluster management include:

• Scalability and Automation: Rafay can scale to support hundreds of individual Kubernetes clusters within a single Rafay account. You can operate your Kubernetes workflows with seamless cluster upgrades, fleet management commands, and reusable provisioning templates.
• Security and Compliance Management: Rafay delivers enterprise-grade security for your clusters and their workloads. Our platform protects mission-critical production apps that demand the highest levels of protection.
• Multi-Cloud and Hybrid Cloud Support: Rafay has fully integrated first-class support for multi-cloud and hybrid cloud workflows. You can effortlessly control all of your clusters within the platform, regardless of the clouds they reside in.
• Integrated Monitoring, Logging, and Analytics: Rafay’s monitoring suite makes it simple to analyze cluster activity and utilization. You can easily see how many projects are deployed, where they’re located, and if any clusters have reported a healthcheck failure or alert. Rafay has built-in integrations with Prometheus, Grafana, Elasticsearch, Kibana, and more.

These capabilities mean Rafay’s platform has helped propel industry-leading platform teams to take control of Kubernetes management at scale. Verizon chose Rafay to operate a fleet of hundreds of clusters, Moneygram uses Rafay to gain a unified view of clusters across its AWS regions, and SonicWall used Rafay with Amazon EKS to accelerate its Kubernetes delivery timelines by 50% within three months. These success stories prove the real-world impacts of aligning Kubernetes operations around a purpose-built multi-cloud enterprise PaaS.

Practical Insights and Tips for Optimizing Kubernetes Lifecycle Management

The need to consolidate, centralize, and unify clusters and clouds is the key takeaway to remember when mastering Kubernetes management. Unless you have a consistent approach to configuration, security, and monitoring, it’s challenging to manage large Kubernetes environments without risking reliability issues and compliance breaches.

Tools such as Rafay, Terraform, Thanos, the Kubernetes Cluster API, and managed cloud Kubernetes services make it possible to gain control of your clusters, wherever they’re located. Together, they facilitate standardization and self-service access—both critical capabilities that make Kubernetes usable at enterprise scale.

Kubernetes success isn’t just about tooling, however. Beyond the technical best practices we’ve discussed in this article, you should also ensure your infrastructure is fully documented so developers and operators are supported with the resources they need to effectively perform their roles. Establish a Kubernetes center of excellence within your organization to promote adoption, guide training initiatives, and cultivate healthy cluster management practices. Although this step is easily overlooked, its implementation sets you up for long-term success.

Conclusion: You Need Powerful Kubernetes Management for Cloud-Native Success

This guide has examined some key Kubernetes management challenges and how you can resolve them with Rafay’s Kubernetes platform. Successful cluster operation depends on the presence of robust observability, security, and scalability controls, while multi-cluster and multi-cloud scenarios require additional coordination to enable centralized oversight and prevent configuration drift.

We’ve seen that it’s crucial to choose the right Kubernetes management solution that will support you to achieve these outcomes. Rafay’s PaaS for Kubernetes management lets you manage your entire Kubernetes fleet with ease, allowing you to interact with all your clusters from one dashboard and CLI. Rafay also grants autonomy to developers, platform engineers, and operators by promoting isolated multi-tenancy and zero-trust cluster access.

Ready to standardize your cluster operations? Explore Rafay’s PaaS for Kubernetes management by starting for free or emailing [email protected] to get your demo.

Additional Resources and Further Reading

• Rafay’s PaaS for Kubernetes Management
• Article: Navigating Container Management Challenges – Strategies for Security, Integration, and Troubleshooting
• Case Study: Verizon Chooses Rafay to Manage Hundreds of Kubernetes Clusters
• Whitepaper: Best Practices for Kubernetes Cost Monitoring and Management
• Whitepaper: The Definitive Guide to Kubernetes Automation and Governance for Platform Teams