Dynamic Scaling and Performance Optimization for Microservices using Kubernetes

Abstract

This study evaluates Kubernetes' role in managing microservices under high-load conditions, emphasizing the efficiency of Horizontal Pod Autoscaler (HPA), Cluster Autoscaler, and security mechanisms. The research demonstrates how Kubernetes enhances scalability, reduces failure risks, and ensures stable performance. Experimental results validate its effectiveness in optimizing CPU load and response time for fluctuating workloads. The problems faced by developers when implementing this software were also considered, namely: security settings, optimization of auto-scaling, and configuration scheme of network interaction between components. The experimental results presented in Tables 1–3 confirm the effectiveness of automatic microservices scaling in Kubernetes. Under loads of 10,000 and 100,000 connections, the average CPU load without scaling reached 606.34–555 ms, whereas with Horizontal Pod Autoscaler (HPA) enabled, this metric was reduced to 219–293 ms. Similarly, server response time in scenarios 1 and 2 decreased by more than half (from 43 to 12 ms and from 58 to 32 ms, respectively). These findings demonstrate that HPA and Cluster Autoscaler mechanisms, designed for dynamically adjusting the number of nodes based on the current load, optimize computational resources and enhance system responsiveness even under increasing traffic. The article targets developers and software architects optimizing microservice applications. In conclusion, recommendations are provided on leveraging Kubernetes to build a flexible, fault-tolerant microservice architecture capable of handling high loads. The article is aimed at developers and architects of software systems that optimize microservice applications. In conclusion, recommendations are given on using Kubernetes to create a flexible, fault-tolerant microservice structure ready for high loads.