The ScaleOps Blog

Kubernetes continues to evolve, offering features that enhance efficiency and adaptability for developers and operators. Among these are Resize CPU and Memory Resources assigned to Containers, introduced in Kubernetes version 1.27. This feature allows for adjusting the CPU and memory resources of running pods without restarting them, helping to minimize downtime and optimize resource usage. This blog post explores how this feature works, its practical applications, limitations, and cloud provider support. Understanding this functionality is vital for effectively managing containerized workloads and maintaining system reliability.

Kubernetes has become the de facto platform for building highly scalable, distributed, and fault-tolerant microservice-based applications. However, its massive ecosystem can overwhelm engineers and lead to bad cluster management practices, resulting in resource waste and unnecessary costs.

Managing Kubernetes costs can be challenging, especially with containers running across multiple clusters and usage constantly fluctuating. Without a clear strategy, unexpected expenses can quickly add up. This article explores the key principles of Kubernetes cost management, highlighting major cost factors, challenges, best practices, and tools to help you maintain efficiency and stay within budget.

Amazon recently introduced EKS Auto Mode, a feature designed to simplify Kubernetes cluster management. This new feature automates many operational tasks, such as managing cluster infrastructure, provisioning nodes, and optimizing costs. It offers a streamlined experience for developers, allowing them to focus on deploying and running applications without the complexities of cluster management.

In Kubernetes, the availability during planned and unplanned disruptions is a critical necessity for systems that require high uptime. Pod Disruption Budgets (PDBs) allow for the management of pod availability during disruptions. With PDBs, one can limit how many pods of an application could be disrupted within a window of time, hence keeping vital services running during node upgrades, scaling, or failure. In this article, we discuss the main components of PDBs, their creation, use, and benefits, along with the best practices for improving them for high availability at the very end.

When managing large-scale Kubernetes clusters, efficient resource utilization is key to maintaining application performance while controlling costs. But certain workloads, deemed “unevictable,” can hinder this balance. These pods—restricted by Pod Disruption Budgets (PDBs), safe-to-evict annotations, or their role in core Kubernetes operations—are anchored to nodes, preventing the autoscaler from adjusting resources effectively. The result? Underutilized nodes that drive up costs and compromise scalability. In this blog post, we dive into how unevictable workloads challenge Kubernetes autoscaling and how ScaleOps’ optimized pod placement capabilities bring new efficiency to clusters through intelligent automation.

The Kubernetes Vertical Pod Autoscaler (VPA) is a critical component for managing resource allocation in dynamic containerized environments. This guide explores the benefits, limitations, and best practices of Kubernetes VPA, while offering practical insights for advanced Kubernetes users.

The Kubernetes Horizontal Pod Autoscaler (HPA) adjusts the number of active pods in real time based on resource needs, ensuring efficient scaling for applications. This blog covers how HPA works, setup examples, best practices, and more, for a complete autoscaling strategy.