This paper is published in Volume-3, Issue-4, 2017
Area
Cloud Computing
Author
Chenni Kumaran .J
Co-authors
M. Aramudhan
Org/Univ
Manonmaniam Sundaranar University, Tirunelveli, India
Pub. Date
07 July, 2017
Paper ID
V3I4-1137
Publisher
Keywords
Cloud Computing, Resource Allocation, Enthalpy, Krill Herd, Task Measure, Scheduling, Queuing Theory, Cuckoo Search.

Citationsacebook

IEEE
Chenni Kumaran .J, M. Aramudhan. Resource Allocation Utilizing Enthalpy Based Krill Herd Optimization Algorithm in Cloud Computing, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Chenni Kumaran .J, M. Aramudhan (2017). Resource Allocation Utilizing Enthalpy Based Krill Herd Optimization Algorithm in Cloud Computing. International Journal of Advance Research, Ideas and Innovations in Technology, 3(4) www.IJARIIT.com.

MLA
Chenni Kumaran .J, M. Aramudhan. "Resource Allocation Utilizing Enthalpy Based Krill Herd Optimization Algorithm in Cloud Computing." International Journal of Advance Research, Ideas and Innovations in Technology 3.4 (2017). www.IJARIIT.com.

Abstract

Cloud computing has been a sort of Internet-related computing which proffers distributed computer processing resources , data computers and other devices upon requirement. The prior methodology of resource assignment in cloud computing is accomplished through Queuing Theory Based Cuckoo Search algorithm and it is having few limitations including failure in processing plus knapsack issue. Moreover scheduling energy consumption plus computational cost is high. Our proposed work of resource assignment through workflow scheduling is purely carried in two stages. First stage comprises of two phases, for every available task we measure task reward, delay, transmission probability, communication cost and reputation. According to the task measure value computed, we calculate the Enthalpy values. In the Second stage of our proposed work, we employ enthalpy based krill herd optimization algorithm for allocating resources that increase trade make span and for minimizing the resource usage. It also reduces computational complexity by enhancing the computing efficiency of processing elements. The implementation of our suggested algorithm reduces the knapsack issue of energy consumption, VM usage, PM usage, computational time, task migration and resource utilization which proffers to cost reduction.
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