| Publisher | The University of Warwick | ||
|---|---|---|---|
| Format | 353.7KB PDF, requires Acrobat Rdr 5 | Date added | 23 Mar 2004 |
| Topics | Load Balancing, High Performance Computing | ||
| Downloads | 55 | ||
Workload and resource management are essential functionalities in the software infrastructure for grid computing. The management and scheduling of dynamic grid resources in a scalable way requires new technologies to implement a next generation intelligent grid environment. This work demonstrates that AI technologies can be utilised to achieve effective workload and resource management. A combination of intelligent agents and multi-agent approaches is applied for both local grid resource scheduling and global grid load balancing. Each agent is a representative of a local grid resource and utilises predictive application performance data and iterative heuristic algorithms to engineer local load balancing across multiple hosts. At a higher level of the global grid, agents cooperate with each other to balance workload using a peer-to-peer service advertisement and discovery mechanism.
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