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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.


Scheduling of workflows for Example 1.
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fig3: Scheduling of workflows for Example 1.

Mentions: Consider that we have number of processors equal to three and number of jobs equal to five with the computation and communication time given for the workflow graph shown in Figure 2(a). The diagonal value in Figure 2(b) corresponds to the computation time of each node and the upper triangle gives the communication cost of the corresponding node with other nodes in the workflow graph. For a single workflow, the scheduling of jobs onto the processor is shown in Figure 3. Here P1, P2, and P3 are computing processors and ct00 and ct11 are the computation time of job node J0 and job node J1, respectively, with ct13 being the communication time between J1 and J3. The computation matrix is given below:(2)ct5,5=2120003030004100002100001.Grid computing applications like SETI@home, drug discovery, high energy physics, and so forth require hundreds or thousands of jobs to be done on the computing processors, so we are in need of a scheduling policy to increase the performance of the system based on users' or customers' request. The first and the foremost parameter to be optimized is the makespan, which is the application completion time, to be minimized. The scheduling problem is defined with certain assumptions.


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)

Scheduling of workflows for Example 1.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig3: Scheduling of workflows for Example 1.
Mentions: Consider that we have number of processors equal to three and number of jobs equal to five with the computation and communication time given for the workflow graph shown in Figure 2(a). The diagonal value in Figure 2(b) corresponds to the computation time of each node and the upper triangle gives the communication cost of the corresponding node with other nodes in the workflow graph. For a single workflow, the scheduling of jobs onto the processor is shown in Figure 3. Here P1, P2, and P3 are computing processors and ct00 and ct11 are the computation time of job node J0 and job node J1, respectively, with ct13 being the communication time between J1 and J3. The computation matrix is given below:(2)ct5,5=2120003030004100002100001.Grid computing applications like SETI@home, drug discovery, high energy physics, and so forth require hundreds or thousands of jobs to be done on the computing processors, so we are in need of a scheduling policy to increase the performance of the system based on users' or customers' request. The first and the foremost parameter to be optimized is the makespan, which is the application completion time, to be minimized. The scheduling problem is defined with certain assumptions.

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.