Optimization and selection of cloud data center resources using salp swarm optimization

The ever-changing nature of workloads and the need for effective resource allocation make cloud data center resource optimization an essential concern. Keeping energy usage low while maintaining Quality of Service (QoS) is a common challenge for existing optimization methods. The Salp Swarm Optimization (SSO) framework recommends the most effective resource allocation policy for resource distribution in cloud architecture. This is necessary due to the continuously fluctuating demands for resources and the complexities of the cloud environment. CPU use, power consumption, and infrastructure efficiency are considered during layer allocation. The framework's efficiency is evaluated using data obtained by PlanetLab Virtualized Research to determine how effective the resource allocation is. The utility of resource allocation is demonstrated by increasing PUE and CPU utilization. CloudSim oversees resource allocation, and when it does so, it does so while considering the energy consumption of the host. A high degree of data center efficiency ultimately leads to developing a green data center, which is achieved through cloud resource allocation in conjunction with SSO. The model performance is evaluated by simulating it on the cloud and comparing the results to several other performance factors, including make span, delay, and throughput. The proposed SSA model increases the sustainability ratio of ۹۲.۰۷%, resource optimization ratio of ۹۸.۵۴%, space optimization ratio of ۹۰.۱۵%, time efficiency ratio of ۹۲.۲۰%, and performance ratio of ۹۳.۲۹% compared to other existing models.

کلیدواژه ها:

optimization ، cloud ، Data Center ، Meta-Heuristic Approaches ، resources