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Efficient Scheduling of Scientific Workflows with Energy Reduction Using Novel Discrete Particle Swarm Optimization and Dynamic Voltage Scaling for Computational Grids.

Christobel M, Tamil Selvi S, Benedict S - ScientificWorldJournal (2015)

Bottom Line: Minimizing energy imposes benefits like reduction in power consumption, decrease in cooling rates of the computing processors, provision of a green environment, and so forth.This novel DPSO yields better schedule with minimum computation time compared to Earliest Deadline First (EDF) and First Come First Serve (FCFS) algorithms which comparably reduces energy.Other scheduling parameters, such as job completion ratio and lateness, are also calculated and compared with EDF and FCFS.

View Article: PubMed Central - PubMed

Affiliation: Ponjesly College of Engineering, Nagercoil, Tamil Nadu 629003, India.

ABSTRACT
One of the most significant and the topmost parameters in the real world computing environment is energy. Minimizing energy imposes benefits like reduction in power consumption, decrease in cooling rates of the computing processors, provision of a green environment, and so forth. In fact, computation time and energy are directly proportional to each other and the minimization of computation time may yield a cost effective energy consumption. Proficient scheduling of Bag-of-Tasks in the grid environment ravages in minimum computation time. In this paper, a novel discrete particle swarm optimization (DPSO) algorithm based on the particle's best position (pbDPSO) and global best position (gbDPSO) is adopted to find the global optimal solution for higher dimensions. This novel DPSO yields better schedule with minimum computation time compared to Earliest Deadline First (EDF) and First Come First Serve (FCFS) algorithms which comparably reduces energy. Other scheduling parameters, such as job completion ratio and lateness, are also calculated and compared with EDF and FCFS. An energy improvement of up to 28% was obtained when Makespan Conservative Energy Reduction (MCER) and Dynamic Voltage Scaling (DVS) were used in the proposed DPSO algorithm.

No MeSH data available.


Energy reduction of novel DPSO with DVS and MCER compared to EDF, FCFS, and DPSO algorithms.
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fig8: Energy reduction of novel DPSO with DVS and MCER compared to EDF, FCFS, and DPSO algorithms.

Mentions: Also it is obvious from Figure 7 that the makespan of the schedule is unaltered with a decrease in processor speed of certain tasks resulting in the reduction of energy consumption. Table 6 shows the energy consumed by the DVS enabled processors using novel DPSO algorithm. DVS enabled scheduling thus provides a significant energy reduction. The amount of energy reduction using novel DPSO with DVS and MCER is compared to EDF, FCFS, and DPSO using (13) and is shown in Figure 8.


Efficient Scheduling of Scientific Workflows with Energy Reduction Using Novel Discrete Particle Swarm Optimization and Dynamic Voltage Scaling for Computational Grids.

Christobel M, Tamil Selvi S, Benedict S - ScientificWorldJournal (2015)

Energy reduction of novel DPSO with DVS and MCER compared to EDF, FCFS, and DPSO algorithms.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4446568&req=5

fig8: Energy reduction of novel DPSO with DVS and MCER compared to EDF, FCFS, and DPSO algorithms.
Mentions: Also it is obvious from Figure 7 that the makespan of the schedule is unaltered with a decrease in processor speed of certain tasks resulting in the reduction of energy consumption. Table 6 shows the energy consumed by the DVS enabled processors using novel DPSO algorithm. DVS enabled scheduling thus provides a significant energy reduction. The amount of energy reduction using novel DPSO with DVS and MCER is compared to EDF, FCFS, and DPSO using (13) and is shown in Figure 8.

Bottom Line: Minimizing energy imposes benefits like reduction in power consumption, decrease in cooling rates of the computing processors, provision of a green environment, and so forth.This novel DPSO yields better schedule with minimum computation time compared to Earliest Deadline First (EDF) and First Come First Serve (FCFS) algorithms which comparably reduces energy.Other scheduling parameters, such as job completion ratio and lateness, are also calculated and compared with EDF and FCFS.

View Article: PubMed Central - PubMed

Affiliation: Ponjesly College of Engineering, Nagercoil, Tamil Nadu 629003, India.

ABSTRACT
One of the most significant and the topmost parameters in the real world computing environment is energy. Minimizing energy imposes benefits like reduction in power consumption, decrease in cooling rates of the computing processors, provision of a green environment, and so forth. In fact, computation time and energy are directly proportional to each other and the minimization of computation time may yield a cost effective energy consumption. Proficient scheduling of Bag-of-Tasks in the grid environment ravages in minimum computation time. In this paper, a novel discrete particle swarm optimization (DPSO) algorithm based on the particle's best position (pbDPSO) and global best position (gbDPSO) is adopted to find the global optimal solution for higher dimensions. This novel DPSO yields better schedule with minimum computation time compared to Earliest Deadline First (EDF) and First Come First Serve (FCFS) algorithms which comparably reduces energy. Other scheduling parameters, such as job completion ratio and lateness, are also calculated and compared with EDF and FCFS. An energy improvement of up to 28% was obtained when Makespan Conservative Energy Reduction (MCER) and Dynamic Voltage Scaling (DVS) were used in the proposed DPSO algorithm.

No MeSH data available.