Enhancing Cloud Task Scheduling Using a Hybrid Particle Swarm and Grey Wolf Optimization Approach

TL;DR

This paper introduces a hybrid metaheuristic algorithm combining Grey Wolf Optimizer and Particle Swarm Optimization to improve task scheduling efficiency in cloud computing, outperforming existing methods in key performance metrics.

Contribution

The paper proposes a novel hybrid GWO-PSO algorithm for cloud task scheduling, enhancing global exploration and local exploitation capabilities.

Findings

Up to 15% improvement in makespan

10% better throughput

More balanced task distribution

Abstract

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.