A Game-Theoretic Approach to Multiobjective Job Scheduling in Cloud Computing Systems

This paper presents a distributed and security-driven solution to multiobjective job scheduling problem in the Cloud Computing infrastructures. The goal of this scheme is allocating a limited quantity of resources to a specific number of jobs minimizing their execution failure probability and job completion time. As this problem is NP-hard in the strong sense, a meta-heuristic NSGA-II is proposed to solve it. To select the best strategy from the resulting Pareto frontier we develop decision-making mechanisms based on the game-theoretic model of Spatial Prisoner´s Dilemma and realized by independent, selfish brokering agents. Their behavior is conditioned by objectives of the various entities involved in the scheduling process and driven towards a Nash equilibrium solution by the employed social welfare criteria. The performance of the applied scheduler is verified by a number of numerical experiments. The related results show the effectiveness of the proposed solution for medium and large-sized scheduling problems.