Kubernetes Config and Efficiency: The Butterfly Effect

Ever heard of the butterfly effect? In chaos theory, it happens when a tiny change in one sensitive initial condition has a huge impact down the line. The butterfly effect is a great analogy for Kubernetes configuration and the way its overall success affects cloud efficiency. Although the yardstick varies according to size, organizations who make the best possible use of their cloud resources (at the lowest cost, with minimal waste and effort), are optimizing their cloud efficiency. Along with container security and reliability, maintaining robust cloud efficiency remains one of the most pressing concerns in Kubernetes and cloud-native technology today. 

When best practices around Kubernetes configuration—and misconfiguration, as the case may be—are not properly addressed, efficiency can be severely compromised along with performance and optimization of resources. This means practitioners need to perform a lot of different checks to ensure their workloads are running efficiently and cost-effectively. Building an efficient cloud environment means assessing all dimensions of the configuration posture to ensure Kubernetes clusters and containerized workloads are happy and secure. Because without configuration best practices in place, the “butterfly effect” will jeopardize cloud efficiency along with other markers like security, reliability and cost. 

Misconfigurations Mean Problems

Large organizations will find it is nearly impossible to manually check each Kubernetes configuration and assess its risk. Because Kubernetes default settings are naturally open, replacing them with best practices is mandatory. Helpful guidance and a useful framework for hardening your cloud environment can be found in various objective, consensus-driven security guidelines for Kubernetes software.  

Monitoring Kubernetes workloads for efficiency issues and keeping resources fine-tuned as workloads change over time requires an in-depth approach. Part of this solution comes from finding a SaaS orchestration platform with the ability to establish effective governance, streamline development and operations and provide a better (and more efficient) user experience. Misconfigurations in Kubernetes are far more common than not, which means a stable, reliable cluster can only be achieved through recognizing this pitfall and instituting best practices across the enterprise. 

Configuration Validation to the Rescue

Best practices around configuration demand validation technology. For smaller outfits with only a few Kubernetes clusters, manual configuration validation via code review is pretty manageable. However, scalability becomes a major challenge as bigger organizations with multiple development teams attempt deployment to multiple clusters. DevOps teams can quickly lose control and visibility into container security, along with myriad other issues. Finding automation and policies designed to enforce consistency while also providing the right guardrails across the organization is critical. 

Configuration validation and management (such as IaC scanning) affect cloud efficiency by providing the necessary visibility to proactively identify where money and other resources are being wasted. Misconfigurations of Kubernetes workloads often involve inefficient provisioning of compute resources, something that often leads to an oversized bill for cloud computing. To maximize CPU efficiency and memory utilization for a workload, teams need to set resource limits and requests properly. But here’s the catch—knowing the right limits to set for smooth application performance can be tricky, at best. This is where visibility comes in.

Gaining visibility into application resource use can help teams better understand how their application performs with different CPU and memory settings. These can then be adjusted to improve app performance or to increase the efficiency of Kubernetes compute resources, ultimately helping organizations save money in the cloud and capacity in their data centers.

Kubernetes Service Ownership Saves the Day

Kubernetes offers a framework in which distributed systems are built with microservices and containers to run applications reliably. This model means separate teams own different layers of the stack, a fundamental concept of Kubernetes service ownership. Developers are specifically responsible for getting their applications to Kubernetes with proper configurations. This pervasive DevSecOps-like model of service ownership frees operations teams from handling deployment configuration and allows them to focus on policy enforcement and actionable developer feedback.

Workload configuration, typically made using YAML files and Helm charts, affects the security and reliability of services as well as the efficiency of workloads in a cluster. There are numerous factors to consider when assembling a stable and reliable Kubernetes cluster, including the potential need for application changes and alterations to cluster configuration. These considerations include things like setting resource requests and limits, autoscaling pods with the right metrics and using liveness and readiness probes.

Solutions like IaC scanning can inspect YAML and Helm configurations when developers make a pull request. Traditional infrastructure as code scanning solutions will examine configuration for security violations, like privilege escalation, and other issues. This type of solution software goes further by also incorporating efficiency and reliability checks for platform engineering teams, who rely on them for running stable and scalable infrastructure. It’s also important to watch workloads in production to measure the actual CPU and memory usage, so you can provide feedback to the developer when they don’t match the settings. 

Robert Brennan

Robert Brennan is director of open source software at Fairwinds, a cloud-native infrastructure solution provider. He focuses on the development of open source tools that abstract the complexity from underlying infrastructure to enable an optimal experience for developers. Before Fairwinds, he worked as a software engineer at Google in AI and natural language processing. He is the co-founder of DataFire.io, an open source platform for building API’s and integrations, and LucyBot, developer of a suite of automated API documentation solutions deployed by Fortune 500 companies. He is a graduate of Columbia College and Columbia Engineering where he focused on machine learning.

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