Improving scheduling performance using a q-learning-based leasing policy for clouds
Euro-Par'12 Proceedings of the 18th international conference on Parallel Processing
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We utilize a competitive coevolutionary algorithm (CA) in order to optimize the parameter set of a Fuzzy-System for job negotiation between Community-Grids. In a Community-Grid, users are submitting jobs to their local High Performance Computing (HPC) sites over time. Now, we assume that Community-Grids are interconnected such that the exchange of jobs becomes possible: Each Community strives for minimizing the response time for their own members by trying to distribute workload to other communities in the Grid environment. For negotiation purpose, a Fuzzy-System is used to steer each site’s decisions whether to distribute or accept workload in a beneficial, yet egoistic direction. In such a system, it is essential that communities can only benefit if the workload is equitably (not necessarily equally) portioned among all participants. That is, if one community egoistically refuses to execute foreign jobs regularly, other HPC sites suffer from overloading. This, on the long run, deteriorates the opportunity to utilize them for job delegation. Thus, the egoistic community will degrade its own average performance. This scenario is particularly suited for the application of a competitive CA: the Fuzzy-Systems of the participating communities are modeled as species, which evolve in different populations while having to compete within the commonly shared ecosystem. Using real workload traces and Grid setups, we show that the opportunistic cooperation leads to significant improvements for both each community and the overall system.