In network computing systems, load balancing is the problem of job distribution among multiple processors. In the area of job distribution, Round Robin method and random job division are the simplest. The demerit of these approaches is to be unable to adapt to the change of network environment. Therefore, a job scheduling mechanism is required to make reasonable job assignment for efficient use of the network. This mechanism should be able to not only adapt dynamically to the destination but also allocate jobs even without advance prediction of the changes of traffic, individual computer process speed, or the length of waiting queue.
We propose learning automaton based job scheduling method for the load distribution problem in parallel distributed computing systems. This method can dynamically respond to job addresses and make proper job assignment. We show the effectiveness of our method by computer simulation.