A Multiobjective Resources Scheduling Approach Based on Genetic Algorithms in Grid Environment

Ye, Guangchang; Rao, Ruonan; Li, Minglu

doi:10.1109/gccw.2006.9

Cited by 34 publications

(14 citation statements)

References 3 publications

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“…Later these Metaheuristics were compared against a MOEA algorithm, optimizing the makespan and flowtime objective functions. In [8], the authors propose an algorithm called Multi-Objective Resource Scheduling Approach-MORSA, which is a combination between NPGA and NSGA Algorithms. They combine the sorting algorithm of non-dominated solutions with the process of Niche Sharing to ensure diversity.…”

Section: Related Workmentioning

confidence: 99%

Multi-Objective Ant Colony Optimization for Task Scheduling in Grid Computing

Nitu

Garg

2014

Advances in Intelligent Systems and Computing

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Resource Management in Grid computing system is a fundamental issue in achieving high performance due to the distributed and heterogeneous nature of the resources. The efficiency and effectiveness of Grid resource management greatly depend on the scheduling algorithm. In this paper, the problem of scheduling is represented by a weighted directed acyclic graph (DAG). Ant Colony Optimization is used for scheduling tasks on resources in Grid which simultaneously pay attention to two objectives of makespan (schedule length) and the failure probability (reliability). These objectives are conflicting and it is not possible to minimize both objectives at the same time. With the help of concept of non-dominance, we are able to choose a trade-off between makespan minimization and reliability maximization. For evaluating the algorithm, ACO is compared with NSGA-II. The metrics for evaluating the convergence and diversity of the obtained non-dominated solutions by the two algorithms are reported. The results of simulation using JAVA programming language manifest that proposed approach can be used more efficiently for allocating the tasks as compared to NSGA-II.

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Section: Related Workmentioning

confidence: 99%

Multi-Objective Ant Colony Optimization for Task Scheduling in Grid Computing

Nitu

Garg

2014

Advances in Intelligent Systems and Computing

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“…Most of the other notable list heuristics consider different user-and systemcentric objectives like execution time (makespan) [23][24][25], resource utilization [26], reliability [27,28] and total economic cost [29,30] as independent scheduling criteria. Some isolated approaches try to optimize across multiple criteria [31][32][33].…”

Section: Related Workmentioning

confidence: 99%

Energy-Aware Workflow Scheduling in Grid Under QoS Constraints

Garg

Singh

2015

Arab J Sci Eng

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In this study, we have considered the problem of scheduling precedence-constraint parallel applications (workflows) in heterogeneous grid-computing environment. Recently many heuristics have been devoted to grid scheduling typically restricted to optimizing the execution time (makespan) only without paying much concentration on energy consumption. Reducing energy consumption can bring various advantages like reducing operating costs, environmental perspective and increase in system reliability. This paper aims to develop energy-aware task scheduling algorithm in grid based on the dynamic voltage and frequency scaling (DVFS) technique. The user negotiates with the service provider on their quality of service (QoS) requirements along with green computing specifications to reach the service level agreement. With the use of DVFS, the algorithm minimizes the energy consumption of task execution while satisfying the QoS constraints (deadline). The proposed static scheduling algorithm works in three phases: deadline distribution, tasks ordering and then assigning the best services to tasks along with selecting the appropriate voltage levels while meeting its sub-deadline. The simulation results using randomly generated task graphs and task graphs corresponding to real-world problems exhibit that the proposed algorithm achieves energy efficiency and reduces energy consumption up to 68 % with the increase in 30 % of the execution time.B Ritu Garg

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“…Additionally, there exist several works using multi-objective optimization algorithms for the problem of mapping tasks on Grids, either considering dependencies between tasks [28], [29], deadlines [30], or static allocation of independent tasks [31], [32], [33], as the problem considered in this paper. However, these papers are considering objectives such as the resource utilization, the completion time of the resources, or the total execution time, but none is considering the robustness of the system as we do in this work.…”

Section: Related Workmentioning

confidence: 99%

Multi-objective robust static mapping of independent tasks on grids

Dorronsoro

Bouvry

Cañero

et al. 2010

IEEE Congress on Evolutionary Computation

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We study the problem of efficiently allocating incoming independent tasks onto the resources of a Grid system. Typically, it is assumed that the estimated time to compute each task on every machine is known. We are making the same assumption in this work, but we allow the existence of inaccuracies in these values. Our schedule will be robust versus such inaccuracies, ensuring that even when the estimated time to compute all the tasks is increased by a given percentage, the makespan of the schedule (i.e., the time when the last machine finishes its tasks) will not grow behind that percentage. We propose a new multi-objective definition of the problem, optimizing at the same time the makespan of the schedule and its robustness. Four well-known multi-objective evolutionary algorithms are used to find competitive results to the new problem. Finally, a new population initialization method for scheduling problems is proposed, leading to more efficient and accurate algorithms.

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A Multiobjective Resources Scheduling Approach Based on Genetic Algorithms in Grid Environment

Cited by 34 publications

References 3 publications

Multi-Objective Ant Colony Optimization for Task Scheduling in Grid Computing

Multi-Objective Ant Colony Optimization for Task Scheduling in Grid Computing

Energy-Aware Workflow Scheduling in Grid Under QoS Constraints

Multi-objective robust static mapping of independent tasks on grids

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