An Improved Scheduling Algorithm for Grid Tasks

Li, Wenzheng; Zhang, Wenyue

doi:10.1109/iuce.2009.35

Cited by 7 publications

(6 citation statements)

References 5 publications

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“…The goal of Grid scheduling is to make an optimal mapping of tasks to computational resources and utilize them efficiently [7] with the objective(s) of minimizing makespan, flowtime, cost and maximizing load balancing across resources, resource utilization, throughput etc. [8,9,10]. Even if, the detailed information concerning to the status of jobs and resources is well-known then also optimal scheduling has been shown to be NP-complete [8,11,12,13,14,15].…”

Section: Grid Schedulingmentioning

confidence: 99%

A Survey on Scheduling Heuristics in Grid Computing Environment

Mishra¹,

Patel²,

Rout³

et al. 2014

IJMECS

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Job scheduling is one of the thrust research area in the discipline of Grid computing. Scheduling in the Grid environment is not only complicated but also known to be NP-Complete problem and that is all due to its unique characteristics. Thus, there are limited opportunities to find an optimal solution. In recent past, many eminent researchers reported a variety of Scheduling Heuristics that can have a substantial impact on the performance of the Grid systems. Unfortunately, it gives rise to difficulty in evaluating and keeping track of those solutions. Therefore, the motivation of this comprehensive study is to present firstly, an in-depth review of the topic under discussion mostly in the perspective of Grid Scheduling environment, and secondly, a proposal for a new state-of-the-art classification of the existing Scheduling Heuristics. All these Heuristics in each category have been further studied based on significant parameters frequently used in Scheduling Heuristics. The final part of this study includes a fair assessment of those mostly used dominating parameters. This report deals with the key concepts behind existing Scheduling Heuristics including Objectives, Types of Job Scheduling, Functionality of Grid, Nature of Grid, and the importance of the proposed classification.

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Section: Grid Schedulingmentioning

confidence: 99%

A Survey on Scheduling Heuristics in Grid Computing Environment

Mishra¹,

Patel²,

Rout³

et al. 2014

IJMECS

View full text Add to dashboard Cite

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“…The job with maximum expected completion time is allocated to the machine which has M CT for that job. This improves the makespan and balances the load to some extent and performs best for jobs with longer execution time [2].…”

Section: Max-minmentioning

confidence: 99%

“…The node with largest cost function is deleted at times in order to reduce the height of the tree. A cost function f (n) is assigned to each node which is calculated on the makespan of its best partial solution [2]. The makespan is calculated as the sum of maximum of machine availability times and lower bound estimate of the difference between the makespan of node's partial solution and makespan of best solution.…”

Section: A*mentioning

confidence: 99%

Prioritized User Demand Approach for Scheduling Meta Tasks on Heterogeneous Grid Environment

Suresh¹,

Balasubramani²,

Keerthika³

2011

IJCA

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Due to the rapid evolution of grid computing, which deals with the effective utilization of the globally distributed comp uter resources to solve massive problems, grid scheduling is the major focus. Efficient scheduling algorithms are the need of the hour to achieve efficient utilization of the unused CPU cycles distributed geographically in various locations. The existing job scheduling algorithms in grid computing had mainly concentrated on the system performance rather than the user satisfaction. In this paper we have presented a new prioritized user demand algorithm that mainly focuses on better meeting the deadlines of the statically available jobs as expected by the users. This algorithm also concentrates on the better utilization of the available heterogeneous resources. The performance analysis shows that the prioritized user demand algorithm performs better than the other heuristic scheduling algorithms in terms of makespan and resource utilization rate.

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“…Max-min algorithm is similar to min-min algorithm. It finds the job with maximum expected completion time from set of unmapped jobs and allocates it to machine which has minimum completion time for the job [11].…”

Section: Introductionmentioning

confidence: 99%

User Demand Aware Grid Scheduling Model with Hierarchical Load Balancing

Suresh¹,

Balasubramanie²

2013

Mathematical Problems in Engineering

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Grid computing is a collection of computational and data resources, providing the means to support both computational intensive applications and data intensive applications. In order to improve the overall performance and efficient utilization of the resources, an efficient load balanced scheduling algorithm has to be implemented. The scheduling approach also needs to consider user demand to improve user satisfaction. This paper proposes a dynamic hierarchical load balancing approach which considers load of each resource and performs load balancing. It minimizes the response time of the jobs and improves the utilization of the resources in grid environment. By considering the user demand of the jobs, the scheduling algorithm also improves the user satisfaction. The experimental results show the improvement of the proposed load balancing method.

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An Improved Scheduling Algorithm for Grid Tasks

Cited by 7 publications

References 5 publications

A Survey on Scheduling Heuristics in Grid Computing Environment

A Survey on Scheduling Heuristics in Grid Computing Environment

Prioritized User Demand Approach for Scheduling Meta Tasks on Heterogeneous Grid Environment

User Demand Aware Grid Scheduling Model with Hierarchical Load Balancing

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