TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Minimizing Energy Consumption in Scheduling of Dependent Tasks using Genetic Algorithm in Computational Grid |
| 영문제목(English Title) |
Minimizing Energy Consumption in Scheduling of Dependent Tasks using Genetic Algorithm in Computational Grid |
| 저자(Author) |
Omprakash Kaiwartya
Shiv Prakash
Abdul Hanan Abdullah
Ahmed Nazar Hassan
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| 원문수록처(Citation) |
VOL 09 NO. 08 PP. 2821 ~ 2839 (2015. 08) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Energy consumption by large computing systems has become an important research theme not only because the sources of energy are depleting fast but also due to the environmental concern. Computational grid is a huge distributed computing platform for the applications that require high end computing resources and consume enormous energy to facilitate execution of jobs. The organizations which are offering services for high end computation, are more cautious about energy consumption and taking utmost steps for saving energy. Therefore, this paper proposes a scheduling technique for Minimizing Energy consumption using Adapted Genetic Algorithm (MiE-AGA) for dependent tasks in Computational Grid (CG). In MiE-AGA, fitness function formulation for energy consumption has been mathematically formulated. An adapted genetic algorithm has been developed for minimizing energy consumption with appropriate modifications in each components of original genetic algorithm such as representation of chromosome, crossover, mutation and inversion operations. Pseudo code for MiE-AGA and its components has been developed with appropriate examples. MiE-AGA is simulated using Java based programs integrated with GridSim. Analysis of simulation results in terms of energy consumption, makespan and average utilization of resources clearly reveals that MiE-AGA effectively optimizes energy, makespan and average utilization of resources in CG. Comparative analysis of the optimization performance between MiE-AGA and the state-of-the-arts algorithms: EAMM, HEFT, Min-Min and Max-Min shows the effectiveness of the model.
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| 키워드(Keyword) |
Energy Consumption
Scheduling
Genetic Algorithm(GA)
Dynamic Voltage Frequency Scaling (DVFS)
Idle Time
Makespan
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| 파일첨부 |
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