Hybrid Model for Optimal Container Resource Allocation in Cloud

Abstract

Most of the industries and fields reside on cloud computing based microservice owing to its capability with high-performance. The main constraint for cloud providers is the container resource allocation, as it impacts system performance and resource consumption directly. This paper presents a narrative hybrid approach, which hybrids the theory of particle swarm optimization (PSO) and grey wolf optimization (GWO), which is named as velocity updated GWO (VU-GWO) for optimal container resource allocation. Moreover, a new rescaled objective function is defined as the solution of optimized resource allocation. The considered rescaled objective function involves threshold distance, balanced cluster use, system failure, and total network distance. To the end, the presented scheme is evaluated over other classical schemes, and the betterment of the proposed model is proved.