A novel multi-agent reinforcement learning approach for job scheduling in Grid computing
Future Generation Computer Systems
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A fundamental problem in large scale Grids is the need for efficient and scalable techniques for resource discovery and scheduling. In traditional resource scheduling systems a single scheduler handles information about all computing resources and schedules jobs. This centralized approach has a serious scalability problem, since it introduces a bottleneck, as well as a single point of failure. Some decentralized scheduling systems have been proposed to improve scalability. However, the main contributions in this area are generally carried out under the assumption of several coordinated schedulers. Nevertheless this approach leads to high communication costs. Such costs are mainly caused by the strong dependency on negotiation among scheduler-to-scheduler and scheduler-to-resource communication. Current approaches to decentralized resource management - in particularly approaches based on Random Early Detection (RED) - are non-coordinated since these schedulers make scheduling related decisions in an independent way. This paper introduces a collaborative model of decentralized scheduling that improves resource scheduling based on RED strategies via gossiping. With this approach, schedulers can receive information from other schedulers without creating a high communication overhead and continue scheduling jobs in an independent way. The simulation results shows that our proposal is scalable and it handles large resources efficiently on large scale Grids.