Load Balancing in Computational Grid Using Genetic Algorithm

Prakash, Shiv; Vidyarthi, Deo Prakash

doi:10.5923/j.ac.20110101.02

Cited by 24 publications

(17 citation statements)

References 17 publications

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“…makespan, reliability, availability, throughput, energy consumption for job scheduling in computational grid [6,7,14,[16][17][18]. Most of these models did scheduling with a single objective.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The distributed nature of grid resources and the dynamic nature of the grid workload make the makespan of the job unpredictable. For makespan minimization, it is warranted to effectively deal with the load balancing as well [6]. Further, the heterogeneous hardware components of the grid are more prone to failure and therefore reliability of such systems decreases with the increase in the number of geographically distributed resources [7].…”

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confidence: 99%

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An energy-efficient reliable grid scheduling model using NSGA-II

Kaushik

Vidyarthi

2015

Engineering with Computers

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parameters by effectively managing the available grid resources. In general, it is unusual to assume a single criterion for the decision making in a grid system which is governed by various related factors. The scheduling problem in the grid system may have several conflicting objectives, e.g., makespan, reliability, energy consumption, cost, security, etc., which need to be optimized simultaneously [1][2][3][4].The outcome values of different objectives, viz. execution time of any job, the energy consumption by the job and the reliability offered by the system to the job may vary from one allocation to another [5]. The distributed nature of grid resources and the dynamic nature of the grid workload make the makespan of the job unpredictable. For makespan minimization, it is warranted to effectively deal with the load balancing as well [6]. Further, the heterogeneous hardware components of the grid are more prone to failure and therefore reliability of such systems decreases with the increase in the number of geographically distributed resources [7]. The heterogeneity of the system demands varying energy consumption based on their hardware profile [8]. It necessitates choosing those nodes/machines that are able to execute the job quickly, reliably and with least energy consumption.Since these objectives are conflicting in nature, any approach adopted to optimize one objective may not result in the overall optimized solution with respect to other objectives. All the conflicting objectives need to be addressed simultaneously and the best way for this is to have the trade-off solutions with all the objectives [9, 10]. A good solution corresponding to all the objectives is desired. This solution may not offer the best of all the objectives, but certainly better when seen as a trade-off among the objectives. This approach is referred as multiobjective optimization technique [5]. As such, job scheduling in the grid is an NP-hard problem and traditional Abstract Job scheduling in computational grid is a complex problem and various heuristics and meta-heuristics have been proposed for the same. These approaches usually optimize specific characteristic parameters while allocating the jobs on the grid resources. Many a times, it is desired to optimize multiple parameters during job scheduling. Nondominated sorting genetic algorithm (NSGA-II) has been observed to be the best meta-heuristic to solve such multiobjective optimization problem. The proposed work applies NSGA-II for job scheduling in computational grid with three conflicting objectives: maximizing reliability of the system for job allocation, minimizing energy consumption and balancing the load on the system. Performance study of the proposed model is done by simulating it on some real data. The result indicates that the proposed model performs well with multiple objectives.

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Section: Literature Reviewmentioning

confidence: 99%

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An energy-efficient reliable grid scheduling model using NSGA-II

Kaushik

Vidyarthi

2015

Engineering with Computers

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“…Taking the effect of IPC into consideration, [9] uses GA for solving the problem of independent task scheduling in computational grid (CG). In [10], in place of a single, more than one task has been considered for allocation with load consideration using GA focusing on minimization of turnaround time in distributed computing systems. In [10], Load balancing, which is also to be taken care of and is an NP Hard problem, has been widely discussed in [26].…”

Section: Related Workmentioning

confidence: 99%

“…In [10], in place of a single, more than one task has been considered for allocation with load consideration using GA focusing on minimization of turnaround time in distributed computing systems. In [10], Load balancing, which is also to be taken care of and is an NP Hard problem, has been widely discussed in [26]. Load balancing on the grid nodes that uses GA has been elaborated in [11].…”

Section: Related Workmentioning

confidence: 99%

Fault-Tolerance Aware Multi Objective Scheduling Algorithm for Task Scheduling in Computational Grid

Sahu¹,

Singh²,

Prakash³

2015

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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“…In [28], the two types of GA to improve the performance of the scheduling algorithm are presented and minimize the total execution time and meet load balancing. In [29], the balance is the net charge on the computational grid using genetic algorithms regardless of makespan or fees for network resources represented. In [30], the different load balancing strategy based on a tree representation of a network is studied.…”

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confidence: 99%

PSO for multi-objective problems: Criteria for leader selection and uniformity distribution

Motameni

2016

JAIDM

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This paper proposes a method to solve multi-objective problems using improved Particle Swarm Optimization. We propose leader particles which guide other particles inside the problem domain. Two techniques are suggested for selection and deletion of such particles to improve the optimal solutions. The first one is based on the mean of the m optimal particles and the second one is based on appointing a leader particle for any n founded particles. We used an intensity criterion to delete the particles in both techniques. The proposed techniques were evaluated based on three standard tests in multi-objective evolutionary optimization problems. The evaluation criterion in this paper is the number of particles in the optimal-Pareto set, error, and uniformity. The results show that the proposed method searches more number of optimal particles with higher intensity and less error in comparison with basic MOPSO and SIGMA and CMPSO and NSGA-II and microGA and PAES and can be used as proper techniques to solve multi-objective optimization problems.

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Load Balancing in Computational Grid Using Genetic Algorithm

Cited by 24 publications

References 17 publications

An energy-efficient reliable grid scheduling model using NSGA-II

An energy-efficient reliable grid scheduling model using NSGA-II

Fault-Tolerance Aware Multi Objective Scheduling Algorithm for Task Scheduling in Computational Grid

PSO for multi-objective problems: Criteria for leader selection and uniformity distribution

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