A Complex Network-Based Approach for Job Scheduling in Grid Environments

Many optimization techniques have been adopted for efficient job scheduling in grid computing, such as: genetic algorithms, simulated annealing and stochastic methods. Such techniques present common problems related to the use of inaccurate and out-of-date information, which degrade the global system performance. Besides that, they also do not properly model a grid environment. In order to adequately model a real grid environments and approach the scheduling using updated information, this paper uses complex network models and the simulated annealing optimization technique. The complex network concepts are used to better model the grid and extract environment characteristics, such as the degree distribution, the geodesic path, latency. The complex network vertices represent grid process elements, which are generalized as computers. The random and scale free models were implemented in a simulator. These models, associated with Dijkstra algorithm, helps the simulated annealing technique to find out efficient allocation solutions, which minimize the application response time.