A novel multi-agent reinforcement learning approach for job scheduling in Grid computing

Abstract

Grid computing utilizes distributed heterogeneous resources to support large-scale or complicated computing tasks, and an appropriate resource scheduling algorithm is fundamentally important for the success of Grid applications. Due to the complex and dynamic properties of Grid environments, traditional model-based methods may result in poor scheduling performance in practice. Scalability and adaptability are among the key objectives of Grid job scheduling. In this paper, a novel multi-agent reinforcement learning method, called ordinal sharing learning (OSL) method, is proposed for job scheduling problems, especially, for realizing load balancing in Grids. The approach circumvents the scalability problem by using an ordinal distributed learning strategy, and realizes multi-agent coordination based on an information-sharing mechanism with limited communication. Simulation results show that the OSL method can achieve the goal of load balancing effectively, and its performance is even comparable to some centralized scheduling algorithm in most cases. The convergence property and adaptability of the proposed method are also illustrated.