Enhancing Cloud Resource Allocation Efficiency with Harris Hawk Optimization Algorithm(HHOA)

The demand for efficient cloud resource allocation has surged with the growing reliance on cloud computing for a wide array of applications. Optimizing resource allocation in cloud environments is crucial for cost-effectiveness, performance, and energy conservation. In this context, bio-inspired optimization algorithms have gained prominence for their ability to address complex allocation problems. This paper investigates the application of the Harris Hawk Optimization algorithm (HHOA) to enhance cloud resource allocation efficiency. HHOA, inspired by the hunting behavior of Harris's hawks, is an efficient and versatile metaheuristic algorithm known for its ability to find optimal solutions in complex, dynamic environments. By harnessing the HHOA algorithm, this study aims to improve the allocation of resources such as virtual machines and storage within cloud infrastructures as demanded by the tasks. In our proposed approach we present a comprehensive examination of HHOA's suitability for cloud resource allocation and compares its performance with conventional optimization algorithms like Stimulated Annealing (SA), Genetic Algorithm (GA) and Tabu Search (TS). Through a series of experiments, we found that Harris Hawk Optimization Algorithm (HHOA) is proven to be very effective in improving operational efficiency by enhanced reliability, lowered latency and lowered average response time.