Open AccessEnergy Efficient Scheduling in Cloud Computing using Black Widow Optimization
Author(s) -
Manoj Kumar,
. Suman
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1950/1/012063
Subject(s) - cloud computing , cloudsim , computer science , energy consumption , distributed computing , scheduling (production processes) , quality of service , utility computing , computer network , mathematical optimization , operating system , cloud computing security , engineering , electrical engineering , mathematics
The delivery of services at IaaS, SaaS, and PaaS levels in Cloud Computing through internet proves to a promising technology. The increase in demand of services of cloud computing increased the creation of cloud datacenters in the world. This leads to increase in demand of energy consumption by datacenter. The integration of green computing with cloud tries to minimize the consumption of power, to minimize their energy costs, and increases their profit. Minimizing the energy consumption with promise of better Quality of Service (QoS) seems to be mutually explosive task for cloud service providers (CSP). To deal with these, a meta-heuristic technique called Energy Efficient Black Widow Optimization based Scheduling algorithm (EEBWOSA) is developed and analyzed in this paper. The special stage of cannibalism which excludes bad solutions will not be used for generating new solution which leads to early convergence. The performance of EEBWOSA is tested on workload taken from HPC2N dataset in CloudSim tool. It exhibits reduction in energy consumption by 25.69% and 13.52% as compared to GA and PSO.