The ScaleOps Blog

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.

As Kubernetes becomes the backbone of modern application deployment, platform engineering teams face the challenge of managing its resource complexity. While Kubernetes provides the flexibility and scalability necessary to handle containerized workloads, ensuring efficient resource management is critical for both innovation and operational efficiency. Mismanagement can lead to performance bottlenecks, higher costs, and downtime. This is where intelligent automation platforms like ScaleOps step in, dramatically simplifying resource management.

Kubernetes is hailed for its scalability and flexibility, but managing its resources effectively can be a challenge that results in unnecessary costs. As cloud-based applications scale, organizations often overlook hidden inefficiencies that waste resources, leading to inflated bills. This blog post highlights best practices for optimizing Kubernetes resources to ensure cost efficiency.