Green Energy Model for Grid Resource Allocation

Kaushik, Achal; Vidyarthi, Deo Prakash

doi:10.4018/ijghpc.2014040104

Cited by 2 publications

(8 citation statements)

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“…Similarly, nodes put in sleep mode may increase the reliability, but the links put in the sleep mode reduces the reliability of the system for the job under execution and thus warrants a trade-off between energy saving, reliability and average load overhead [15,28].…”

Section: The Proposed Modelmentioning

confidence: 99%

“…Two threshold vales are used for the purpose: lower and upper. If the load on a node goes below the lower threshold, that node may be put to sleep mode and if the load of a node goes above the upper threshold, some node needs to be awakened [14,15,18].…”

Section: The Proposed Modelmentioning

confidence: 99%

“…The load redistribution requires the identification of the appropriate nodes which may be given extra load affecting minimally other system characteristics. The possibility of turning the sleep mode of the resources to on/off is based on threshold satisfying some given constraints using dynamic allocation (DA) method [14,15]. Two threshold vales are used for the purpose: lower and upper.…”

Section: The Proposed Modelmentioning

confidence: 99%

“…In the model, multiple jobs are considered with each job, having set of modules and arbitrary precedence amongst them [17]. The system in consideration is based on a multi-entry point decentralized system wherein a user submits the jobs to any of the active nodes of the grid via a cluster agent in response to which the model explores the appropriate clusters using load balancing allocation (LBA) method with matching specialization [14,15]. For minimal energy consumption, sleep mode of all the computing nodes and the corresponding links that are not in use or in scarce use are considered [29].…”

Section: The Proposed Modelmentioning

confidence: 99%

“…In the second part, an energy saving mechanism is applied in which all the nodes of the chosen cluster with load less than a dynamically evaluated threshold switch to the sleep mode. The concept of energy saving is carried forward from one of our previous work [14,15]. The loads of the sleeping nodes are redistributed among other active nodes while maintaining the completion time constraint.…”

Section: Introductionmentioning

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: The Proposed Modelmentioning

confidence: 99%

Section: The Proposed Modelmentioning

confidence: 99%

Section: The Proposed Modelmentioning

confidence: 99%