Dynamic job scheduling in distributed computing system using stochastic learning automata

In a Distributed Computing System (DCS) jobs can arrive randomly at each node, which can change the status of the node constantly. Therefore, jobs in DCS should be scheduled dynamically to meet the constraints of the system and to improve the system performance. For job scheduling, accurate global information is impossible. However, an estimation can be made to schedule job to achieve near-optimal solution of the problem of job scheduling. For reliability, a scheduler should be placed on each node in the system. This study is focuses on dynamic job scheduling in DCS using network of stochastic learning automata (SLA). SLA is used as a decision maker in job scheduling. First, an abstract model of DCS is presented, then the algorithm is formulated for dynamic job scheduling. A mathematical proof of correctness is conducted for the validation of the algorithm