Numerical Recipes in C: The Art of Scientific Computing
Numerical Recipes in C: The Art of Scientific Computing
Handbook of Graphs and Networks: From the Genome to the Internet
Handbook of Graphs and Networks: From the Genome to the Internet
A Resource Management Architecture for Metacomputing Systems
IPPS/SPDP '98 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
The Anatomy of the Grid: Enabling Scalable Virtual Organizations
Euro-Par '01 Proceedings of the 7th International Euro-Par Conference Manchester on Parallel Processing
Grid Information Services for Distributed Resource Sharing
HPDC '01 Proceedings of the 10th IEEE International Symposium on High Performance Distributed Computing
An Online Credential Repository for the Grid: MyProxy
HPDC '01 Proceedings of the 10th IEEE International Symposium on High Performance Distributed Computing
Self-Adaptive and Self-Optimising Resource Monitoring for Dynamic Grid Environments
DEXA '04 Proceedings of the Database and Expert Systems Applications, 15th International Workshop
Self-adaptive applications on the grid
Proceedings of the 12th ACM SIGPLAN symposium on Principles and practice of parallel programming
Characterization of a computational grid as a complex system
GMAC '09 Proceedings of the 6th international conference industry session on Grids meets autonomic computing
TPNC'12 Proceedings of the First international conference on Theory and Practice of Natural Computing
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Grid computing has become in recent years an important alternative to traditional parallel computing by providing computational capabilities at large scales. On the other hand, complex networks have shown to develop nontrivial topological features which can be adapted to the optimization of a wide range of problems. The present study uses this approach for the reduction of applications execution time in a Grid infrastructure. In this work, we focused on those characterized by parametric sweeps. The way to achieve this task is by applying the preferential attachment technique with a small modification: new nodes are added to the evolving graph with a probability proportional not only to the target node degree but also to the efficiency of resources involved. As a result, the application becomes self-adaptive to the infrastructure by dynamically obtaining resource rankings and classifying them according to their efficiency.