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Discovering performance bounds for grid scheduling by using evolutionary multiobjective optimization

In this paper, we introduce a methodology for the approximation of optimal solutions for a resource allocation problem in the domain of Grid scheduling on High Performance Computing systems. In detail, we review a real-world scenario with decentralized, equitable, and autonomously acting suppliers of compute power who wish to collaborate in the provision of their resources. We exemplarily apply NSGA-II in order to explore the bounds of maximum achievable benefit. To this end, appropriate encoding schemes and variation operators are developed while the performance is evaluated. The simulations are based upon recordings from real-world Massively Parallel Processing systems that span a period of eleven months and comprise approximately 100,000 jobs. By means of the obtained Pareto front we are able to identify bounds for the maximum benefit of Grid computing in a popular scenario. For the first time, this enables Grid scheduling researchers to rank their developed real-world strategies.