Unified load balancing strategies for enhanced cloud computing solutions

Abstract

Cloud computing offers scalable, on-demand resources that enable a variety of services and applications. Effective load balancing in cloud environments is essential for maintaining performance and Quality of Service (QoS). These environments present complex, dynamic conditions that make efficient load balancing challenging. Many existing algorithms focus on single-objective optimisation, such as minimising response time, which often results in trade-offs and inefficiencies when dealing with unpredictable workloads. This dissertation tackles these inefficiencies by introducing a unified, multi-objective load balancing strategy that combines Ant Colony Optimisation (ACO) and Genetic Algorithm (GA) techniques. The hybrid ACO-GA algorithm is implemented within the CloudAnalyst simulation environment, leveraging ACO’s rapid local search and GA’s global exploration capabilities to dynamically balance workloads across cloud resources. Extensive simulation experiments demonstrate that the proposed hybrid approach significantly improves key QoS metrics compared to both conventional and state-of-the-art load balancers. The ACO-GA consistently achieved substantially lower average response times and improved load distribution relative to traditional algorithms. For example, under light workloads it reduced mean response time by roughly 50% versus Round Robin and 40% under heavy loads. The hybrid method also outperformed modern heuristics, sustaining about 8–10% faster response than advanced metaheuristic policies while shortening data centre processing delays. These gains were accompanied by more efficient resource utilisation, as the algorithm prevented server overloading and underutilisation through balanced task allocation. Notably, performance improvements persisted across both low and high demand scenarios, highlighting the algorithm’s robust adaptability to dynamic cloud conditions. Overall, the results affirm that this unified ACO-GA strategy effectively addresses the limitations of single-objective approaches, offering a significant enhancement in cloud service performance, resource utilisation and QoS.