A Hybrid Eagle’s Web Swarm Optimization (EWSO) technique for effective cloud resource management

encompasses a range of tasks focused on the efficient allocation and administration of resources within a cloud computing environment, all in order to meet the requirements of cloud users. The key components of cloud computing resource management consist of resource allocation, task scheduling, load balancing, and data storage optimization. Cloud resource management poses several challenges, including the optimization of resource allocation, efficient task scheduling, and effective data storage management in distributed file systems like HDFS. These challenges require careful consideration and strategic approaches to ensure optimal utilization of resources, timely execution of tasks, and efficient handling of large-scale data. This paper introduces a novel approach for optimizing cloud resources using Eagle’s Web Swarm Optimization (EWSO). The EWSO technique integrates three distinct algorithms, namely the Black Widow optimizer, Golden Eagle optimizer, and Chaotic Leader & Salp optimizer, to address specific aspects of resource optimization in the cloud. By combining these algorithms, the proposed approach aims to enhance the efficiency and the reliability of resource allocation, task scheduling, and HDFS optimization in the cloud environment. To evaluate the effectiveness of the proposed EWSO technique, its efficiency is compared to several other bio-inspired optimization techniques commonly used in cloud resource management. These include WARMS, AARP, ILPEPS and BFDHP. Through this comparison, the performance, and capabilities of the EWSO technique can be assessed in the context of cloud resource management. The findings from experiments prove the effectiveness of the proposed EWSO technique compared to other bio-inspired mechanisms in terms of resource allocation, task scheduling, and HDFS optimization. These findings provide empirical evidence that the EWSO technique outperforms alternative approaches in managing and optimizing cloud resources, highlighting its potential for enhancing the overall effectiveness and productivity of cloud-based systems.