A Two-Tier Energy-Aware Resource Allocation Framework in Cloud Computing Using the Spider Wasp Optimizer

Abstract

Cloud infrastructures are increasingly subject to performance and environmental requirements, particularly in large data centers with substantial energy expenditures. This paper addresses the issue of energy-aware quality of service (QoS) guaranteed resource allocation and presents an original two-tiered resource allocation scheme based on a Green Management Module (GMM) and a Spider Wasp Optimizer (SWO)-driven Cloud Management Module (CMM). The CMM pre-filters candidate resources based on the latest measurements beforehand, and the GMM selects the optimal resource, aided by the SWO, to inform the allocation decision. Experimental results show that the scheme reduces service response time and energy consumption by 25% and 26% compared to baseline schemes such as dynamic VM provisioning and VM allocation policies. The scheme presents an energy-aware and scalable design with tremendous prospects for optimizing resource use in existing clouds.