A priority-based multilevel scheduler for batch jobs on grids

During the last decades, the architectures devoted to the high performance computing changed their shape and structure several times. From the early SMP machines to the Clusters of computers, through the Computational Grids to nowadays Computing Clouds. If on one side this evolution brought to computing platform able to provide several Petaflops, on the other side the increased amount of computing power, obtained through the usage of thousands computer in parallel makes more and more complex an effective and efficient management of resources. In this paper, we propose a multicriteria job scheduler for scheduling a continuous stream of batch jobs on large-scale computing farms, called Convergent Scheduling 2.0 (CS 2.0), which is an enhancement of the scheduler described in [3]. 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.