원문정보
보안공학연구지원센터(IJGDC)
International Journal of Grid and Distributed Computing
Vol.8 No.4
2015.08
pp.145-152
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
Cloud computing has been emerged as a new service model in computing world and giving lot of interest to the researchers to find its benefits. Task scheduling is a major conflict in cloud environment and genetic algorithms are one of the optimization techniques to solve that problem. Virtual machine’s processing elements are important criteria to solve a scheduling problem. In proposed algorithm called upgraded Genetic algorithm, initial population is sorted according to the number of processing elements of each virtual machines. Proposed algorithm is compared with MGA in terms of cost and with MACO in terms of make span. Experiment results shows upgrade genetic algorithm gives better efficiency in term of cost and make span.
목차
Abstract
1. Introduction
2. Task Scheduling – Necessity of Cloud Computing
3. Related Work
4. Evolution of Basic Genetic Algorithm
4.1. Encoding and Initialization [9]
4.2. Fitness Function
4.3. Selection [10]
4.4. Crossover [11]
4.5. Mutation [12]
4.6. Replacement
5. Problem Formulation
6. Proposed Approach
7. Implementation and Results
8. Conclusion and Future Work
Acknowledgements
References
1. Introduction
2. Task Scheduling – Necessity of Cloud Computing
3. Related Work
4. Evolution of Basic Genetic Algorithm
4.1. Encoding and Initialization [9]
4.2. Fitness Function
4.3. Selection [10]
4.4. Crossover [11]
4.5. Mutation [12]
4.6. Replacement
5. Problem Formulation
6. Proposed Approach
7. Implementation and Results
8. Conclusion and Future Work
Acknowledgements
References
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