Resource Scheduling Policy using Population based Metaheuristic Search along with Firefly Caching Policy in Cloud Environment

Abstract

Rapid development in the field of network technology, the cloud computing receives great attention among small scale industries to large industries because of its decentralized environment. The cloud environment comprised of distributed resources in a dynamic fashion, so this necessitates the need for developing optimal scheduling in cloud environment with the satisfaction of QoS necessitated by the cloud consumer with the maximum profit to cloud providers. But the presence of impreciseness while scheduling cloud resources is the challenging issue of traditional scheduling policies. The main objective of this paper is to treat the vagueness in scheduling of cloud resources by designing intuitionistic fuzzy with population-basedmetaheuristic search which works based on the inspiration of teaching and learning process. This proposed work represents the parameters involved in resource scheduling by the means of intuitionistic fuzzy representation with the aim of reducing the response and execution time with maximize throughput which favors the profit of cloud service providers. Once the job is passed to the local schedulers the optimization is also fine grained at the local host level by utilizing cache segmentation with the knowledge of firefly algorithm. The simulation results showed that this developed model provides potential resource scheduling in cloud computing by treating hesitancy in adversarial situation.