Maximizing Resource Utilization using Hybrid Cloud-based Task Allocation Algorithm

Cloud computing operates similarly to a utility, providing users with on-demand access to various hardware and software resources, billed according to usage. These resources are primarily virtualized, with virtual machines (VMs) serving as critical components. However, task allocation within VMs presents significant challenges, as uneven distribution can lead to underloading or overloading, causing system inefficiencies and potential failures. This study addresses these issues by proposing a novel hybrid task allocation algorithm that combines the strengths of the Artificial Bee Colony (ABC) algorithm with Particle Swarm Optimization (PSO). Our approach aims to enhance resource utilization and reduce the risks of VM overload or underload. We conduct a comprehensive evaluation of the proposed hybrid algorithm against traditional ABC and PSO algorithms, focusing on their effectiveness in managing diverse task loads. The results of our empirical analysis indicate that our hybrid approach outperforms the conventional algorithms, leading to better resource utilization and more accurate task allocation. These findings have significant implications for optimizing task allocation in cloud computing environments, and we suggest potential avenues for future research to further refine these strategies.