PFRF: An adaptive data replication algorithm based on star-topology data grids
Future Generation Computer Systems
Knowledge discovery for scheduling in computational grids
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Improving scheduling performance using a q-learning-based leasing policy for clouds
Euro-Par'12 Proceedings of the 18th international conference on Parallel Processing
Journal of Network and Computer Applications
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In this paper, we address the problem of finding well-performing workload exchange policies for decentralized Computational Grids using an Evolutionary Fuzzy System. To this end, we establish a noninvasive collaboration model on the Grid layer which requires minimal information about the participating High Performance and High Throughput Computing (HPC/HTC) centers and which leaves the local resource managers completely untouched. In this environment of fully autonomous sites, independent users are assumed to submit their jobs to the Grid middleware layer of their local site, which in turn decides on the delegation and execution either on the local system or on remote sites in a situation-dependent, adaptive way. We find for different scenarios that the exchange policies show good performance characteristics not only with respect to traditional metrics such as average weighted response time and utilization, but also in terms of robustness and stability in changing environments.