PAHPA: Revolutionizing Kubernetes Autoscaling With Integrated Predictive Analytics and Real-Time Monitoring

Kubernetes provides powerful container orchestration features, like auto-scaling, which dynamically adjusts the scale of containerized applications. However, the default auto scaling mechanism in Kubernetes typically responds to workload changes only after they have occurred, which can result in resource mismatches. While there has been significant research into proactive auto-scaling approaches, most existing solutions struggle to adapt to the constantly evolving characteristics of prediction targets. This paper presents PAHPA, an intelligent, prediction-assisted approach aimed at enhancing Kubernetes' autoscaling capabilities, which integrates an prediction model, SLMD-LightGBM proposed in this paper, featuring a self updating mechanism for continuous prediction optimization, queueing theory analysis to improve resource allocation decisions, and a correction mechanism that refines prediction metrics using real-time data. A central insight of this paper is that while predictions are crucial, they should not be the sole basis for decisions and must be complemented by real-time monitoring data to ensure robust and adaptive autoscaling. Experimental results demonstrate that PAHPA significantly enhances system stability and reduces service latency, achieving a 9.5% lower Violation Rate (16.3%) than HPA (25.8%) and a 63% reduction in 99th percentile latency (880ms vs. 2400ms). This research highlights how combining predictive analytics with real-time monitoring can lead to more effective autoscaling strategies in cloud-native environments.