Improved Energy Efficient Job Scheduling in Cloud Computing
Volume: 4 Issue: 8
Year of Publication: 2017
Authors: Simranjeet Kaur, Dr. Rajan Manro, Er. Robin Khurana
Cloud computing has offered services related to utility aligned IT services. Reducing the schedule length is considered as one of the significant QoS need of the cloud provider for the satisfaction of budget constraints of an application. Task scheduling in a parallel environment is one of the NP problems, which deals with the optimal assignment of a task. To deal with the favorable assignment of some task, task scheduling is considered as one of the NP problem. In this research work the jobs are distributed in a centralized environment. In Centralized environment every job request is forwarded to a central server. The central server passed the jobs to sub servers that are present with in the area of request. This has been performed by using distance formula. Also to reduce the energy consumption by each sub-server is possible by using formation of feedback queue. Job scheduling has been optimized on the basis of priority by using genetic algorithm. Rules are set according to the priorities of the job then scheduling is done by using genetic algorithm. Fuzzy logic also used for classification of the jobs to decide which job has been allotted to the system. Metrics namely, SLR, CCR (Computation Cost Ratio) and Energy consumption are used for the evaluation of the proposed work. All the simulations will be carried out in CLOUDSIM environment.
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CLOUDSIM, Computation cost ratio (CCR), Genetic algorithm (GA), SLR, energy consumption, Fuzzy logic.