Applying reinforcement learning to scheduling strategies in an actual grid environment
International Journal of High Performance Systems Architecture
GRM: a reliable and fault tolerant data replication middleware for grid environment
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
GreenSlot: scheduling energy consumption in green datacenters
Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis
An Adaptive Scheduler Framework for Complex Workflow Jobs on Grid Systems
International Journal of Distributed Systems and Technologies
Enabling Interoperability among Grid Meta-Schedulers
Journal of Grid Computing
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Grid computing has emerged as a way to share geographically and organizationally distributed resources that may belong to different institutions or administrative domains. In this context, the scheduling and resource management is usually performed by a grid resource broker. The scheduling task consists of distributing the jobs among the different centers resources and the need to coordinate the grid with the underlying scheduling levels which have already been identified. However, there is still a lack of policies for this approach. In this paper we describe and evaluate our coordinated grid scheduling strategy. We take as a reference the FCFS job scheduling policy and the matchmaking approach for the resource selection. We also present a new job scheduling policy based on backfilling (JR-backfilling) that aims to improve the workloads execution performance, avoiding starvation and the SLOW-coordinated resource selection policy that considers the average bounded slowdown of the resources as the main parameter to perform the resource selection. From our evaluation, based on trace-driven simulations of real grid systems, we state that our proposed coordinated strategy can substantially improve the workloads execution performance as well as the resource utilization.