Genetic algorithm for network-aware job scheduling in grid environment

In this paper we present the implementation of Genetic Algorithm based grid scheduler that minimizes the jobs finalization time. For data and compute intensive jobs, storage and computing resources need to communicate with each other over the networks, so the network should be used in an efficient way. The proposed scheduling algorithm not only takes processing power of resources into account but also network characteristics when making scheduling decisions. We have implemented and tested the proposed scheduling algorithm in GridSim. Results show that the jobs finalization time and makespan are reduced when network characteristics are also considered in scheduling decisions.