Practical Scheduling of Bag-of-Tasks Applications on Grids with Dynamic Resilience
IEEE Transactions on Computers
Applying reinforcement learning to scheduling strategies in an actual grid environment
International Journal of High Performance Systems Architecture
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Over the past decade the computational grid has emerged as an attractive platform to tackle various large-scale problems, especially in science and engineering. One primary issue associated with the efficient and effective utilization of heterogeneous resources in a grid is scheduling. Grid scheduling involves a number of challenging issues mainly due to the dynamic nature of the grid. In this paper, we propose a novel scheduling algorithm, called the Multiple Queues with Duplication (MQD) algorithm for bag-of-tasks applications in grid environments. The proposed algorithm makes scheduling decisions implicitly taking the recent workload pattern of resources into account. In addition, it adopts a duplication scheme in order to achieve better resource utilization and to lead to better schedules. In our evaluation study a number of intensive experiments with various simulation settings have been conducted. Based on the experimental results, MQD confidently demonstrated its practicability and competitiveness with four previously proposed algorithms.