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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The computational grid helps in faster execution of compute intensive jobs. Many characteristic parameters are intended to be optimized while making resource allocation for job execution in computational grid. Most often, the green energy aspect, in which one tries for better energy utilization, is ignored while allocating the grid resources to the jobs. The conventional systems, which propose energy efficient scheduling strategies, ignore other Quality of Service parameters while scheduling the jobs. The proposed work tries to optimize the energy in resource allocation to make it a green energy model. It explores how effectively the jobs submitted to the grid can be executed for optimal energy uses making no compromise on other desired related characteristic parameters. A graph theoretic model has been developed for this purpose. The performance study of the proposed green energy model has been experimentally evaluated by simulation. The result reveals the benefits and gives an insight for an energy efficient resource allocation.

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