WPPSO: A Container Management Method Based on Workload Prediction and Particle Swarm Optimization for Serverless Computing

Serverless computing has emerged as a prominent research focus in cloud computing because it provides infrastructure-transparent development and elastic resource management. However, this computing paradigm still faces the inherent challenge of cold start. Existing approaches have two major limitations: insufficient workload prediction accuracy and inefficient allocation of reusable container replicas to incoming function requests. To address these challenges, we propose a container scheduling approach based on Workload Prediction and Particle Swarm Optimization (PSO), named WPPSO. WPPSO first leverages a code-pre-trained large language model (LLM) to extract intrinsic function features and then uses a spatio-temporal fusion-based temporal neural network (STF-TNN) to predict serverless workloads. It subsequently employs a greedy algorithm to construct a high-quality initial matching state and uses PSO to refine the container scheduling strategy. Finally, WPPSO introduces a hierarchical container recycling mechanism to reduce idle resource waste. Extensive experiments show that WPPSO reduces startup latency by up to 72.2% and memory footprint by 63.4% compared with the native Knative platform. Compared with RainbowCake, WPPSO achieves a 15.6% lower mean startup latency without statistical significance and a statistically significant 31% reduction in idle memory consumption.