A Decentralized Multi-agent Approach to Job Scheduling in Cloud Environment

Paper proposes a novel solution to a job scheduling problem in the Cloud Computing systems. The goal of this scheme is allocating a limited quantity of resources to a specific number of jobs minimizing their execution failure probability and completion time. It employs the Pareto dominance concept implemented at the client level. To select the best scheduling strategies from the Pareto frontier and construct a global scheduling solution we develop decision-making mechanisms based on the game-theoretic model of Spatial Prisoner's Dilemma and realized by selfish agents operating in the two-dimensional Cellular Automata space. 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 related results show the effectiveness and scalability of this scheme in the presence of a large number of jobs and resources involved in the scheduling process.