Cooperative power-aware scheduling in grid computing environments
Journal of Parallel and Distributed Computing
Game-theoretic static load balancing for distributed systems
Journal of Parallel and Distributed Computing
Energy- and performance-aware scheduling of tasks on parallel and distributed systems
ACM Journal on Emerging Technologies in Computing Systems (JETC)
A Game-Theoretic Analysis of Grid Job Scheduling
Journal of Grid Computing
Information Sciences: an International Journal
Computers and Operations Research
Hopfield neural network for simultaneous job scheduling and data replication in grids
Future Generation Computer Systems
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A grid differs from traditional high performance computing systems in the heterogeneity of the computing nodes as well as the communication links that connect the different nodes together. In grids there exist users and service providers. The service providers provide the service for jobs that the users generate. Typically the amount of jobs generated by all the users are more than any single provider can handle alone with any acceptable quality of service (QoS). As such, the service providers need to cooperate and allocate jobs among them so that each is providing an acceptable QoS to their customers. QoS is of particular concerns to service providers as it directly affects customers' satisfaction and loyalty. In this paper, we propose a game theoretic solution to the QoS sensitive, grid job allocation problem. We model the QoS based, grid job allocation problem as a cooperative game and present the structure of the Nash Bargaining Solution. The proposed algorithm is fair to all users and represents a Pareto optimal solution to the QoS objective. One advantage of our scheme is the relatively low overhead and robust performance against inaccuracies in performance prediction information.