  One of the main challenges in Grid systems is designing an adaptive,
scalable, and model-independent method for job scheduling to achieve a
desirable degree of load balancing and system efficiency. Centralized job
scheduling methods have some drawbacks, such as single point of failure and
lack of scalability. Moreover, decentralized methods require a coordination
mechanism with limited communications. In this paper, we propose a multi-agent
approach to job scheduling in Grid, named Centralized Learning Distributed
Scheduling (CLDS), by utilizing the reinforcement learning framework. The CLDS
is a model free approach that uses the information of jobs and their completion
time to estimate the efficiency of resources. In this method, there are a
learner agent and several scheduler agents that perform the task of learning
and job scheduling with the use of a coordination strategy that maintains the
communication cost at a limited level. We evaluated the efficiency of the CLDS
method by designing and performing a set of experiments on a simulated Grid
system under different system scales and loads. The results show that the CLDS
can effectively balance the load of system even in large scale and heavy loaded
Grids, while maintains its adaptive performance and scalability.

Distributed, Parallel, and Cluster Computing;Artificial Intelligence