Istituto di Scienza e Tecnologie dell'Informazione     
Capannini G., Baraglia R., Puppin D., Ricci L., Pasquali M. A job scheduling framework for large computing farms. In: SC'07 ACM/IEEE Computer Society International Conference for High Performance Computing, Networking, Storage, and Analysis (Reno, NV, USA, November 13-17 2007). Proceedings, pp. 1 - 10. IEEE Press, 2007.
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.
DOI: 10.1145/1362622.1362695
Subject Job scheduling
Nonnumerical Algorithms and Problems-Sequencing

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