A job scheduling framework for large computing farms

Authors:
Gabriele Capannini;Ranieri Baraglia;Diego Puppin;Laura Ricci;Marco Pasquali
Affiliations:
Information Science and Technologies Institute, Pisa, Italy;Information Science and Technologies Institute, Pisa, Italy;Information Science and Technologies Institute, Pisa, Italy;Largo B. Pontecorvo, Pisa, Italy;Information Science and Technologies Institute, Pisa, Italy
Venue:
Proceedings of the 2007 ACM/IEEE conference on Supercomputing
Year:
2007

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Abstract

In this paper, we propose a new method, called Convergent Scheduling, for scheduling a continuous stream of batch jobs on the machines of large-scale computing farms. This method exploits a set of heuristics that guide the scheduler in making decisions. Each heuristics manages a specific problem constraint, and contributes to carry out a value that measures the degree of matching between a job and a machine. Scheduling choices are taken to meet the QoS requested by the submitted jobs, and optimizing the usage of hardware and software resources. We compared it with some of the most common job scheduling algorithms, i.e. Backfilling, and Earliest Deadline First. Convergent Scheduling is able to compute good assignments, while being a simple and modular algorithm.