A Swarm based Virtual Machine Deployment in Cloud Computing data centers

Le résumé fourni par la source

Cloud service providers offer computation as a service that are tailored to encounter the distinct needs and requirements of users, utilizing the Infrastructure as a Service (IaaS) business model. The utilization of virtualization technology in a cloud computing data center operates several Virtual machines (VMs) concurrently on a single Physical Machine (PM). The Virtual Machine Placement (VMP) problem refers to the task of assigning virtual machines to various physical computers. The method take part a crucial function in delineating the level of energy consumption and efficiency in resource use within the infrastructure of cloud data centers. Nevertheless, devising an effective resolution to this problem is a complex task, mostly owing to challenges arising from multi-dimensional resources, machine heterogeneity, and the vast scale of cloud data centers. The research work presents a novel meta-heuristic approach that aims to optimize power consumption and retrench resource dissipation to address the problems highlighted. The method under consideration, referred the meta-heuristic swarm-based approach called Discrete Gravitational Search Algorithm, aims to retrench the overall utilization of power by optimizing the use of physical machines. The above dilemma is accomplished by lowering the dimension of active machines and giving priority to those that are more power-efficient. Additionally, it mitigates resource inefficiency by optimizing and equitably distributing resource use among physical machines. The obtained findings are compared with the methodologies and characteristics already in use, demonstrating that the adopted model surpass other existing algorithms. On comparing the results with respect to VM placement an improvement of 10.3% on average when compared with existing algorithms and 0.8 milliseconds improvement is achieved by the proposed algorithm for identifying the sequence of VM’s for placing it in PM’s.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.