In this paper, we propose a multi-criteria job scheduler for scheduling a continuous stream of batch jobs on largescale computing farms, called Convergent Scheduling 2.0 (CS 2.0), which is an enhancement of the scheduler described in. CS 2.0 exploits a set of heuristics that drive the scheduler in taking decisions. Each heuristics manages a specific constraint, and contributes to compute the measurement of the matching degree between a job and a machine. Scheduling choices are taken both to meet the QoS requested by the submitted jobs and to optimize the exploitation of hardware and software resources. In order to validate CS 2.0, we compared it versus two common job scheduling algorithms: Easy and Flexible backfilling. CS 2.0 demonstrated to be able to compute good assignments that allow a better exploitation of resources with respect to the other algorithms.

A Multi-criteria Job Scheduling Framework for Large Computing Farms

Dazzi P;
2010

Abstract

In this paper, we propose a multi-criteria job scheduler for scheduling a continuous stream of batch jobs on largescale computing farms, called Convergent Scheduling 2.0 (CS 2.0), which is an enhancement of the scheduler described in. CS 2.0 exploits a set of heuristics that drive the scheduler in taking decisions. Each heuristics manages a specific constraint, and contributes to compute the measurement of the matching degree between a job and a machine. Scheduling choices are taken both to meet the QoS requested by the submitted jobs and to optimize the exploitation of hardware and software resources. In order to validate CS 2.0, we compared it versus two common job scheduling algorithms: Easy and Flexible backfilling. CS 2.0 demonstrated to be able to compute good assignments that allow a better exploitation of resources with respect to the other algorithms.
978-076954108-2
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11568/1143820
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