An Improved Whale Optimization Algorithm for Task Scheduling in Cloud Computing Environments
Year: 2025
Type: conference-paper
Abstract: Task scheduling is a critical challenge in cloud computing environments, directly impacting system performance, resource utilization, and quality of service. Traditional scheduling algorithms often struggle to balance multiple optimization objectives while handling the dynamic and heterogeneous nature of cloud resources. This paper proposes an Improved Whale Optimization Algorithm (IWOA) for task scheduling in cloud computing environments. The standard Whale Optimization Algorithm (WOA) is enhanced with three key improvements: an adaptive weight adjustment strategy based on population diversity, a Lévy flight-based local search mechanism, and a hybrid mutation operation. These modifications significantly improve the algorithm's exploration-exploitation balance and help it escape local optima. Extensive simulations compare the proposed IWOA with the standard WOA, Genetic Algorithm (GA), and Min-Min algorithm across multiple performance metrics including makespan, resource utilization, and load balancing. Results demonstrate that IWOA outperforms the comparison algorithms, achieving a notable reduction in makespan and a significant improvement in resource utilization compared to the Min-Min algorithm. The proposed algorithm provides an effective solution for optimizing task scheduling in cloud environments, particularly for scenarios requiring balanced performance across multiple objectives.
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4416986844