Multi-objective task scheduling in large-scale distributed systems using a Lévy flight-based hybrid Bat-Whale optimization algorithm

Abstract

The rapid growth of cloud computing demands efficient task scheduling strategies capable of handling heterogeneous resources, dynamic workloads, and multiple conflicting objectives. Existing approaches often optimize a single criterion, limiting their effectiveness in large-scale distributed systems. This paper proposes hybrid Bat–Whale optimization algorithm (BWOA), a hybrid scheduling algorithm combining the Bat algorithm and Whale optimization algorithm, enhanced with Lévy flight-based exploration, adaptive crossover, and a smart local search mechanism. The framework balances global exploration and local exploitation while preserving population diversity and intensifying search around promising solutions. A problem-aware local search reallocates long-duration tasks to high performance virtual machines and selectively swaps tasks with poor response times. Experiments on a heterogeneous cloud environment with 300 tasks and 50 virtual machines, using min–max scaling for workload normalization, demonstrate that BWOA outperforms classical methods, including first come, first served (FCFS) and Min-Min scheduling algorithms, achieving superior makespan (≈32.77 s) while maintaining competitive utilization, throughput, and energy efficiency. These results highlight the effectiveness of hybrid metaheuristic approaches integrating multiple optimization strategies for multi-objective task scheduling in large scale cloud systems, providing a robust and scalable solution for both academic research and practical deployment.