Optimizing Operations: Effective Kubernetes Best Practices for Platform Teams

As the cloud-native ecosystem expands, organizations often find themselves at a crossroads when beginning their Kubernetes journey, unsure of which path to follow. With numerous options and considerations to weigh, navigating this landscape can be daunting. However, understanding the key questions and priorities is essential for effectively implementing Kubernetes and optimizing operations for platform teams.

Kubernetes is a game-changing technology that helps address the issues and challenges of containerization and cloud computing, as it provides unified platforms for managing applications.

There is no one-size-fits-all approach to Kubernetes’ success. Instead, platform teams must strategize and create their own “golden path” or Internal Developer Platform (IDP) tailored to their unique needs and priorities. This involves addressing critical questions such as:

• Are you operating in highly regulated sectors like finance or healthcare, where security is paramount?
• Do you manage resource-intensive workloads, such as those of data scientists or machine learning applications, requiring optimal resource efficiency?
• Is high availability and reliability crucial for your applications, necessitating minimal downtime?

In this article, hard-won Kubernetes expertise will be shared, focusing on the core areas of security, efficiency, and reliability. The goal is to provide platform teams with actionable Kubernetes best practices for adoption and implementation, enabling long-term value realization across the entire organization.

Security Best Practices

As Kubernetes adoption increases, so does the interest of security teams, yet securing Kubernetes environments poses numerous challenges. Implementing security measures often involves coding and manual audits. Defining the security responsibilities between security and platform teams is crucial, as both play essential roles in enabling secure Kubernetes usage. Despite Kubernetes’ ability to balance agility and resilience, underutilization of governance and risk controls is common, leading to overlooked security vulnerabilities. Implementing robust security measures requires meticulous attention to detail, emphasizing the importance of providing an Internal Developer Platform (IDP) with integrated security features for effective collaboration across teams.

Kubernetes Security Challenges and Benefits

Kubernetes Security Challenges:

1. Neglect of Critical Deployment Configuration: Development teams new to Kubernetes may overlook essential deployment configurations, such as readiness and liveness probes, and resource requests and limits. Neglecting these pieces can lead to future complications.
2. Over-Permissioned Deployments: It’s not always apparent when a Kubernetes deployment is over-permissioned. Providing root access may seem like the easiest way to get something working, but it poses security risks that may not be immediately obvious.
3. Initial Insecurity: Organizations often initially operate in an insecure manner due to a lack of knowledge about Kubernetes security best practices. It’s crucial to tighten security postures to avoid learning security lessons the hard way.

Kubernetes Security Benefits:

1. Built-in Security Tooling: Kubernetes comes with robust built-in security features and has a thriving ecosystem of open-source and commercial solutions for hardening clusters.
2. Coherent Security Strategy: Kubernetes consolidates various pieces of computing infrastructure, making it easier for security teams to conceptualize and address potential attack vectors.
3. Limiting Attack Surface: The Kubernetes attack surface is substantially smaller than pre-Kubernetes infrastructure, reducing the number of potential attack vectors.
4. Blast Radius Limitation: While Kubernetes cannot secure application code, it can restrict the impact of an attack by implementing proper security controls. A well-configured Kubernetes deployment limits the spread of attacks within the cluster.

Best Practices For Cost Optimization

Container technology offers superior infrastructure utilization efficiency compared to traditional virtual machines. Kubernetes dynamically scales workloads using Horizontal Pod Autoscaler (HPA) for individual deployments and Cluster Autoscaler for the entire cluster. It’s crucial to set resource requests and limits sensibly to maximize infrastructure utilization while maintaining application performance.

Setting resource limits too low may cause application termination, while excessively high limits lead to resource wastage and increased costs. Despite the challenge of determining suitable values for each application, fine-tuning resource limits and requests for efficient workload execution is essential.

Setting accurate resource limits and requests is vital for optimizing application operation on Kubernetes clusters, ensuring efficient resource utilization and reliable performance.

Recommendations For Kubernetes Resources Management

1. Enhance Visibility: Investigate application resources and historical usage to uncover any hidden issues. Adjust settings to improve Kubernetes efficiency.
2. Monitor Kubernetes Costs: Assess individual applications to identify cost-saving opportunities without compromising performance.
3. Resource Optimization: Insights closely monitors CPU and memory usage, offering recommendations for resource limits and requests. Optimize CPU and memory utilization for your Kubernetes workloads.
4. Cost Allocation by Namespace or Label: Group and allocate cost estimates based on namespaces or labels, simplifying alignment with business context in reports.

Reliability Best Practices

As businesses scale, achieving reliability becomes increasingly challenging. Adopting a more direct and streamlined approach to cloud-native applications and infrastructure can address this challenge effectively. Containers provide abstraction and isolation for cloud-native applications and their dependencies, enabling scalability without the need to scale traditional application server virtual machines. Cloud-native methodologies offer opportunities to redefine how application components communicate and scale:

• APIs for Communication: Utilize APIs for communication instead of relying on a shared file system.
• Service Discovery: Implement service discovery mechanisms to route traffic to services as they scale.
• Containerization: Abstract application dependencies from the underlying operating system using containers.

Applications with more cloud-native characteristics are easier to containerize and manage in Kubernetes. Another strategy to ensure cluster reliability is transitioning to the use of Infrastructure as Code (IaC).

The Benefits of Infrastructure as Code (IaC)

Infrastructure as Code (IaC) involves managing IT infrastructure using configuration files. Some key advantages of IaC include:

• Reduced Human Error and Future Proofing: Automation and IaC minimize human error by generating predictable outcomes. Testing infrastructure upgrades and changes in new environments helps validate changes without impacting production.
• Repeatability and Consistency: Using code for infrastructure provisioning ensures consistency across environments and facilitates easy replication.
• Disaster Recovery: IaC enables quick recovery from disasters by allowing infrastructure to be recreated from code.
• Improved Auditability: Configuration files provide a clear audit trail of infrastructure changes, enhancing transparency and compliance efforts.

Kubernetes Reliability Best Practices

Simplifying Complexity

Avoid excessive complexity in Kubernetes environments. Prioritize simplicity with these approaches:

1. Service Delivery vs. Traffic Routing: Opt for dynamic service delivery over manual DNS entries or hardcoded hostnames for improved scalability.
2. Application Configuration: Utilize files or environment variables within containers for diverse configurations across environments.
3. Configuration Management Tools: Follow CI/CD best practices by building and deploying new container images, ensuring consistency and reducing configuration discrepancies.

High Availability Architecture and Fault Tolerance

Ensure redundancy for critical components and distribute applications across the Kubernetes cluster for high availability. Plan HA redundancy based on workload requirements to maintain cluster resilience.

Resource Limits and Autoscaling

Set appropriate resource limits to prevent resource contention issues and implement Auto Scaling mechanisms for dynamic workload adjustments, stabilizing the cluster.

Liveness and Readiness Probes

Implement self-healing mechanisms with liveness and readiness probes. These probes continually assess container health, facilitating automatic detection and resolution of issues to enhance cluster reliability.

Policy Enforcement Best Practices

As organizations expand Kubernetes adoption beyond pilot projects to multiple applications and teams, managing cluster configurations becomes increasingly challenging. With a self-service model in place, DevOps and infrastructure leaders face the task of overseeing numerous users across multiple clusters, each building and deploying applications.

The complexity escalates when workloads are inconsistently deployed or modified manually. Without proper guardrails in place, discrepancies in configurations across containers and clusters are inevitable. These inconsistencies stem from various sources, including copying YAML configurations from online examples, over-provisioning workloads to expedite deployment, or lacking processes to verify configurations.

Manually identifying and rectifying these misconfigurations is error-prone and burdensome for platform teams, often leading to code review overload. To maintain consistency and reliability in Kubernetes environments, organizations should implement robust policy enforcement practices.

Kubernetes Policy Enforcement Best Practices

When it comes to enforcing policies in Kubernetes, organizations have three primary options to consider:

Develop Internal Tools

While engineers often prefer developing custom solutions, organizations must weigh the investment of time, money, and resources required to develop and maintain in-house tooling. Leaders must determine if this approach aligns with their business priorities and if it allows teams to focus on core business challenges.

Deploy Open Source Solutions

Numerous open-source tools are available to assist with security, reliability, and efficiency configuration in Kubernetes. These tools include auditing solutions for container scanning and network monitoring. Tools like Polaris, Goldilocks, Nova, and Pluto, which audit Kubernetes clusters for security, efficiency, and reliability. Polaris, for instance, offers built-in checks for various security configurations, readiness probe configurations, and more. While opting for open-source tools, teams must consider the deployment and management overhead and assess if it aligns with their resource availability and business objectives.

Kubernetes Governance Software

Alternatively, organizations can invest in Kubernetes governance software like Fairwinds Insights. This solution enables platform teams to automatically enforce policies across the entire CI/CD pipeline, ensuring secure, scalable, and cost-efficient Kubernetes clusters. Fairwinds Insights provides a unified platform for managing policies, allowing teams to configure and deploy policies across multiple clusters seamlessly. This approach streamlines policy enforcement and enables proactive management of Kubernetes environments.

Conclusion

Throughout this article, we have explored key strategies and recommendations for optimizing Kubernetes operations. From implementing robust security measures to ensuring efficient resource utilization and enforcing policies effectively, each aspect contributes to the overall success of Kubernetes deployments.

It is crucial for organizations to continuously evaluate and refine their Kubernetes practices, considering the evolving landscape of cloud-native technologies and the unique requirements of their applications. By embracing a proactive approach to Kubernetes management and leveraging the insights provided by tools like Fairwinds Insights, organizations can navigate the complexities of Kubernetes with confidence.