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Abhinav Srivastava, Rituraj Rathore & Raksha Sharma

achieve high performance, it is preferable to schedule light weight jobs in groups instead of light weight jobs. This paper mainly focuses on light weight job scheduling, grouping policies and allocation of these groups to resources in dynamic environment.

RELATED WORK Scheduling algorithm is the heart of the grid computing concept. Various scheduling algorithm have been proposed since 1990, lots of successful attempt have been made on individual job scheduling as well as grouping based coarse-grained job scheduling algorithm. Some of the representative research works on job scheduling in Grid computing have been studied. A Dynamic Job Grouping-Based Scheduling for deploying applications (DJGBSDA), group jobs according to MIPS of the resource. Scheduling framework for Bandwidth-Aware Job Grouping-Based strategy that groups the jobs according to MIPS and Bandwidth of the resource [5]. A Bandwidth-Aware Job Grouping-Based scheduling strategy that schedules the jobs according to MIPS and bandwidth of the resource and the model sends grouped jobs to the resource whose network bandwidth has highest communication or transmission rate[6]. Grouping based fine-grained job scheduling algorithm presents job scheduling algorithm that schedules the groups of jobs to resources according to MIPS and Bandwidth of resources [7]. Grouping-Based Job Scheduling Model (GBJS) added constraints on memory size in addition to MIPS and Bandwidth [8]. Allthese algorithms attempted to utilize resources efficiently. But a very few of them focused their attention on the degree of compaction for optimal grid utilization, which measures the percentage of time in which the resources are been used respect to the overall execution time [9]. Thus, the higher the compaction degree is, the more exploited and less idle the resources are. In this paper we have proposed High Compaction based Coarse-Grained Scheduling: - A coarsegrained algorithm which is well focused to the compaction degree of the resource keeping in mind the various constraints including processing capability (in MIPS), bandwidth (in Mb/s), and memory-size (in Mb) of the available resources.

PROPOSED MODEL This study presents the high compaction based coarse grained job scheduling techniques in grid computing. This scheduling techniques maximizes the utilization of grid resource to the optimized level, reduces the processing time of the jobs and network delay on the grid. This model converts jobs into group of jobs i.e. coarse grained jobs. Grouping Strategy Grouping strategy is based on processing capability (in MIPS), bandwidth (in Mb/s), and memory-size (in Mb) of the available resources. Jobs are grouped according to the capability of the selected resource. Therefore, the following conditions must be satisfied: Groupedjob_MI≤Resource_MIPS* Granularity size …….

(1)

Groupedjob_MS ≤ Resource_MS ……

(2)

Groupedjob_MS ≤ Resource_baud_rate * Tcomm

(3)

Where, MI (Million Instruction) is job’s required computational power, MIPS (Million Instruction Per Second) is processing capability of the resource and Granularity_size (time in seconds) for the job grouping activity, Groupedjob_MS is required Memory Size of group job, Resource_MS is the amount of Memory available at resource, Baud_rate is the bandwidth capacity of resource, and Tcomm is the job’s communication time [4].

34 high compaction full  

Grid computing is the federation of computer resources from multiple administrative domains to reach a common goal. The Main focus is on Uni...

34 high compaction full  

Grid computing is the federation of computer resources from multiple administrative domains to reach a common goal. The Main focus is on Uni...

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