Particle Swarm Optimization and Gravitational Emulation Based Hybrid Load Balancing Strategy in Cloud Computing |
Author(s): |
| RAKSHANDA , Department of Computer Science & Applications Kurukshetra University, Kurukshetra ; Dr. Kanwal Garg, Department of Computer Science & Applications Kurukshetra University, Kurukshetra; Vinod, Department of Computer Science & Applications Kurukshetra University, Kurukshetra |
Keywords: |
| Cloud Computing; Cloud Sim; Load Balancing; Genetic Algorithm; Particle Swarm Optimization |
Abstract |
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Cloud computing is a rising technology adopted by both industry and the academic world, providing an elastic way to store and recover the data files. Today’s data is increasing rapidly so load balancing is the essential parameter required for efficient operation of various components in a cloud computing to minimize system load and provide the resources at a rapid rate. The Genetic Algorithm (GA) along with its many versions has been popular mainly because of their intuitiveness, ease of implementation. PSO is a current heuristic search method whose mechanics is inspired by the swarming or common behavior of natural populations. GEL algorithm depends on gravitational attraction. The present research paper conducted an experiment to ensure that a hybrid combination of PSO-GEL has the better result than GA-GEL. |
Other Details |
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Paper ID: IJSRDV4I40737 Published in: Volume : 4, Issue : 4 Publication Date: 01/07/2016 Page(s): 983-986 |
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