2025/05/21 by Prasad, Raveena, Ankit Roy, Roy, Aarush +2 · 1 citation
Computer Science · #Cloud Computing and Resource Management #Distributed #FOS: Computer and information sciences #IoT and Edge/Fog Computing #Parallel #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2505.15171
openalex publication_date 2025/05/21 · openalex created_date 2025/10/19 · openalex updated_date 2026/07/28
Assigning tasks efficiently in cloud computing is a challenging problem and is considered an NP-hard problem. Many researchers have used metaheuristic algorithms to solve it, but these often struggle to handle dynamic workloads and explore all possible options effectively. Therefore, this paper presents a new hybrid method that combines two popular algorithms, Grey Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO). GWO offers strong global search capabilities (exploration), while PSO enhances local refinement (exploitation). The hybrid approach, called HybridPSOGWO, is compared with other existing methods like MPSOSA, RL-GWO, CCGP, and HybridPSOMinMin, using key performance indicators such as makespan, throughput, and load balancing. We tested our approach using both a simulation tool (CloudSim Plus) and real-world data. The results show that HybridPSOGWO outperforms other methods, with up to 15% improvement in makespan and 10% better throughput, while also distributing tasks more evenly across virtual machines. Our implementation achieves consistent convergence within a few iterations, highlighting its potential for efficient and adaptive cloud scheduling.