A fusion deep Q-learning and particle swarm optimization algorithm for adaptive resource allocation in cloud computing circumstances

Effective resource allocation in cloud computing continues a critical challenge due to dynamic loads, stringent service-level expectations, and the need to balance execution time, energy, and cost. This study suggests a hybrid framework that integrates Deep Q-Learning (DQL) with Particle Swarm Optimization (PSO) to aid adaptive, multi-objective scheduling. DQL learns allocation strategies through interaction with the cloud environment, while PSO performs global search to refine action selection and accelerate convergence. Using Cloud Sim with real and synthetic workloads (Google Cluster, Planet Lab traces), the proposed method achieved a 35% reduction in average task execution time (from 245 s to 159 s) and a ~ 40% relative growth in resource utilization (from 60.1% to 84.6%), reduced SLA violations from 28 to 8, and lowered energy consumption to 6.3 kWh, outperforming standalone and hybrid models across 30 independent runs. Statistical tests (two-tailed t-test, α = 0.05) confirm significance. These results demonstrate that coupling reinforcement learning among swarm intelligence yields adaptive, high-quality decisions on behalf of real-time cloud resource scheduling.