A multi-objective approach to load balancing in cloud environments integrating ACO and WWO techniques

Effective load balancing and resource allocation are essential in dynamic cloud computing environments, where the demand for rapidity and continuous service is perpetually increasing. This paper introduces an innovative hybrid optimisation method that combines water wave optimization (WWO) and ant colony optimization (ACO) to tackle these challenges effectively. ACO is acknowledged for its proficiency in conducting local searches effectively, facilitating the swift discovery of high-quality solutions. In contrast, WWO specialises in global exploration, guaranteeing extensive coverage of the solution space. Collectively, these methods harness their distinct advantages to enhance various objectives: decreasing response times, maximising resource efficiency, and lowering operational expenses. We assessed the efficacy of our hybrid methodology by conducting extensive simulations using a cloud-sim simulator and a variety of workload trace files. We assessed our methods in comparison to well-established algorithms, such as WWO, genetic algorithm (GA), spider monkey optimization (SMO), and ACO. Key performance indicators, such as task scheduling duration, execution costs, energy consumption, and resource utilisation, were meticulously assessed. The findings demonstrate that the hybrid WWO-ACO approach enhances task scheduling efficiency by 11%, decreases operational expenses by 8%, and lowers energy usage by 12% relative to conventional methods. In addition, the algorithm consistently achieved an impressive equilibrium in resource allocation, with balance values ranging from 0.87 to 0.95. The results emphasise the hybrid WWO-ACO algorithm's substantial impact on improving system performance and customer satisfaction, thereby demonstrating a significant improvement in cloud computing optimisation techniques.