On-line routing of virtual circuits with applications to load balancing and machine scheduling
Journal of the ACM (JACM)
On power-law relationships of the Internet topology
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
IEEE Transactions on Parallel and Distributed Systems
Packing Schemes for Gang Scheduling
IPPS '96 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Nexus: Small Worlds and the Groundbreaking Theory of Networks
Nexus: Small Worlds and the Groundbreaking Theory of Networks
A Task Duplication Based Scheduling Algorithm with Optimality Condition in Heterogeneous Systems
ICPPW '02 Proceedings of the 2002 International Conference on Parallel Processing Workshops
A Task Duplication Based Scheduling Algorithm for Heterogeneous Systems
IPDPS '00 Proceedings of the 14th International Symposium on Parallel and Distributed Processing
A Unified Scheduling Algorithm for Grid Applications
HPCS '06 Proceedings of the 20th International Symposium on High-Performance Computing in an Advanced Collaborative Environment
IMSCCS '06 Proceedings of the First International Multi-Symposiums on Computer and Computational Sciences - Volume 1 (IMSCCS'06) - Volume 01
A Simulation Model for Grid Scheduling Analysis and Optimization
PARELEC '06 Proceedings of the international symposium on Parallel Computing in Electrical Engineering
A novel approach for distributed application scheduling based on prediction of communication events
Future Generation Computer Systems
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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 Such techniques present common problems related to the use of inaccurate and out-of-date information, which degrade the global system performance.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.