Research on Optimization of Kubernetes Elastic Scaling Based on Entropy Weight Utilization and Prediction Algorithm

This study proposes an improved elastic scaling strategy based on a composite algorithm that combines entropy weight utilization and a prediction model, to address the issues of single-metric evaluation, latency, and low resource utilization in Kubernetes's built-in elastic scaling strategy. The entropy weight utilization composite algorithm calculates the comprehensive load value of the Kubernetes cluster by focusing on the distribution differences (information entropy method) and overall trends (average utilization weight method) of resource utilization across different nodes, thereby solving the problem of single metric evaluation. Next, this study constructs a predictive model that combines Adaptive Variational Mode Decomposition (AVMD) and the Attention Mechanism-based enhanced Long Short-Term Memory (Attention Mechanism-based LSTM) network to solve the latency and low resource utilization issues by predicting load changes. This model enables the system to quickly respond, expand its capacity at the onset of high traffic, and rapidly scale down to release resources once traffic subsides. Experimental results show that the improved elastic scaling strategy reduces the response time by 52% during the early stage of burst traffic compared with the default Kubernetes scaling strategy, and it rapidly scales down after the traffic subsides to release resources, demonstrating high practical application value.