Multi-objective Optimization of Grid Computing for  Performance, Energy and Cost

Fanfakhri, Ahmed Badri Muslim; Al-Sultan, Ali Yakoob; Alwan, Esraa H.

doi:10.24017/science.2017.3.31

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…Authors introduced a prediction framework to execute parallel programs using a small training set. In [11,12,13,14,15,16], researchers have been proposed analytical execution time prediction models for NAS MPI programs over heterogenous cluster and grid. The remainder of the paper is organized as follows: Section 2 describes the execution time of MPI applications over grid.…”

Section: -Intoductionmentioning

confidence: 99%

Predicting the Performance of MPI Applications over Different Grid Architectures

Fanfakh¹

2019

JUBPAS

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Nowadays, the high speed and accurate optimization algorithms are required. In most of the cases, researchers need a method to predict some criteria with acceptable accuracy to use it after in their algorithms. However, in the field of parallel computing the execution time can be considered the most important criteria. Consequently, this paper presents new execution time prediction model for message passing interface applications execute over numerous grid scenarios. The model has ability to predict the execution time of the message passing applications running over any grid configuration in term of different number of nodes and their computing powers. The experiments are evaluated over SimGrid simulator to simulate the grid configuration scenarios. The results of comparing the real and the predicted execution time show a good accuracy. The average error ratio between the real and the predicted execution time for three benchmarks are 4.36%, 5.79% and 6.81%.

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Section: -Intoductionmentioning

confidence: 99%

Predicting the Performance of MPI Applications over Different Grid Architectures

Fanfakh¹

2019

JUBPAS

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“…The results show that the performance of the system is improved compared with previous model. Fanfakhri [4] proposed new multi-objective optimization algorithm to optimize the execution time, the energy consumption and the cost of booked resources in the grid system. The proposed algorithm used new optimization function to select the best frequencies that can improve the performance of the optimized metrics.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Job Ranking Backfilling Based on Linear and Logarithmic Ranking Equations

Al-Najjar

Alhady

Saleh³

2019

IJIE

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Grid system is used by many researchers' and scholars all over the world to solve the complicated and complex problems in different sciences. Job ranking backfilling is the most used model by many researchers in grid system to improve the performance of job scheduling algorithm. The model aims on serving the smallest job in the queue. As a second improvement of job backfilling, researchers proposed job ranking backfilling that serve job based on ranking equation. This paper proposes an enhance job ranking algorithm based on using linear and logarithmic ranking equations. Both proposed ranking equations used curve estimation model to predict on the variables' coefficients. By simulation and after different tests, the average results of job ranking backfilling with linear ranking equation outperform conventional job ranking backfilling with improvement equal 3.2% and 56.53% in total execution time and average waiting time, respectively. In addition, job ranking backfilling with logarithmic ranking equation shows average improvement equal 1.78% and 46.62% in total execution time and average waiting time, respectively. The results indicate that the proposed ranking equations would improve conventional job ranking backfilling in high and low demand grid system under different conditions.

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Multi-objective Optimization of Grid Computing for Performance, Energy and Cost

Cited by 2 publications

References 12 publications

Predicting the Performance of MPI Applications over Different Grid Architectures

Predicting the Performance of MPI Applications over Different Grid Architectures

Enhanced Job Ranking Backfilling Based on Linear and Logarithmic Ranking Equations

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