Energy-Efficient PSO and Latency Based Group Discovery Algorithm in Cloud Scheduling

Abstract

Cloud computing is a large model change of computing system. It provides high scalability and flexibility among an assortment of on-demand services. To imporve the performance of the multi-cloud environment in distributed application might require less energy efficiency and minimal inter-node latency correspondingly. The major problem is that the energy efficiency of the cloud computing data center is less if the number of server is low, else it increases. To overcome the energy efficiency and network latency problem a novel energy-efficient particle swarm optimization representation for multi-job scheduling and Latency representation for the grouping of nodes with respect to network latency is proposed. The scheduling procedure is through on the basis of network latency and energy efficiency. Scheduling schema is the main part of Cloud Scheduler component, which helps the scheduler in scheduling decision on the base of dissimilar criterion. It also works well with incomplete latency information and performs intelligent grouping on the basis of both network latency and energy efficiency. Design a realistic particle swarm optimization algorithm for the cloud servers and construct an overall energy competence based on the purpose of the servers and calculation of fitness value for each cloud servers. Also, in order to speed up the convergent speed and improve the probing aptitude of our algorithm, a local search operative is introduced. Finally, the experiment demonstrates that the proposed algorithm is effectual and well-organized.