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Abstract
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Article Information:
Cost Optimization Using Hybrid Evolutionary Algorithm in Cloud Computing
B. Kavitha and P. Varalakshmi
Corresponding Author: B. Kavitha. MIT Campus
Submitted: July 24, 2014
Accepted: October 12, 2014
Published: July 10, 2015 |
Abstract:
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The main aim of this research is to design the hybrid evolutionary algorithm for minimizing multiple problems of dynamic resource allocation in cloud computing. The resource allocation is one of the big problems in the distributed systems when the client wants to decrease the cost for the resource allocation for their task. In order to assign the resource for the task, the client must consider the monetary cost and computational cost. Allocation of resources by considering those two costs is difficult. To solve this problem in this study, we make the main task of client into many subtasks and we allocate resources for each subtask instead of selecting the single resource for the main task. The allocation of resources for the each subtask is completed through our proposed hybrid optimization algorithm. Here, we hybrid the Binary Particle Swarm Optimization (BPSO) and Binary Cuckoo Search algorithm (BCSO) by considering monetary cost and computational cost which helps to minimize the cost of the client. Finally, the experimentation is carried out and our proposed hybrid algorithm is compared with BPSO and BCSO algorithms. Also we proved the efficiency of our proposed hybrid optimization algorithm.
Key words: Binary cuckoo search, binary particle swarm optimization, computational cost, levy flights, monetary cost, ,
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Cite this Reference:
B. Kavitha and P. Varalakshmi, . Cost Optimization Using Hybrid Evolutionary Algorithm in Cloud Computing. Research Journal of Applied Sciences, Engineering and Technology, (7): 758-769.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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