A Novel Binary Search Based Method to Find Minimal Number of Cores Required to Obtain Minimal Makespan, Given Makespan and Given Utilization of Cores for a DAG Application

Uma, B.; Venugopal, C. R.

doi:10.1115/1.859940.paper30

Cited by 2 publications

(2 citation statements)

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“…A brute force method can be used to find this, by making several calls to scheduling method and recording the makespan for each case. But an efficient binary search based method [24] is used in this work and its time complexity is O(log n) against O(n) of the brute force method.…”

Section: Maximum Number Of Processor For a Dagmentioning

confidence: 99%

“…An efficient binary search based method [24] with time complexity of O(log(n)) is used to find the maximum number of processors a DAG can utilize. The decrease in makespan and increase in computing area (decrease in average processor utilization) for every added processor is used to fix the optimal number of processors for a DAG.…”

Section: Optimal and Maximum Number Of Processors For A Dagmentioning

confidence: 99%

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Multiple DAG Applications Scheduling on a Cluster of Processors

Boregowda¹,

Chakravarthy²

2014

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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Section: Maximum Number Of Processor For a Dagmentioning

confidence: 99%

Section: Optimal and Maximum Number Of Processors For A Dagmentioning

confidence: 99%

Multiple DAG Applications Scheduling on a Cluster of Processors

Boregowda¹,

Chakravarthy²

2014

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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Sharing of Cluster Resources among Multiple Workflow Applications

Boregowda¹,

Chakravarthy²

2014

IJCSIT

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Many computational solutions can be expressed as workflows. A Cluster of processors is a shared resource among several users and hence the need for a scheduler which deals with multi-user jobs presented as workflows. The scheduler must find the number of processors to be allotted for each workflow and schedule tasks on allotted processors. In this work, a new method to find optimal and maximum number of processors that can be allotted for a workflow is proposed. Regression analysis is used to find the best possible way to share available processors, among suitable number of submitted workflows. An instance of a scheduler is created for each workflow, which schedules tasks on the allotted processors. Towards this end, a new framework to receive online submission of workflows, to allot processors to each workflow and schedule tasks, is proposed and experimented using a discrete-event based simulator. This space-sharing of processors among multiple workflows shows better performance than the other methods found in literature. Because of space-sharing, an instance of a scheduler must be used for each workflow within the allotted processors. Since the number of processors for each workflow is known only during runtime, a static schedule can not be used. Hence a hybrid scheduler which tries to combine the advantages of static and dynamic scheduler is proposed. Thus the proposed framework is a promising solution to multiple workflows scheduling on cluster.

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A Novel Binary Search Based Method to Find Minimal Number of Cores Required to Obtain Minimal Makespan, Given Makespan and Given Utilization of Cores for a DAG Application

Cited by 2 publications

References 0 publications

Multiple DAG Applications Scheduling on a Cluster of Processors

Multiple DAG Applications Scheduling on a Cluster of Processors

Sharing of Cluster Resources among Multiple Workflow Applications

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