In a computational grid, jobs must adapt to the dynamically changing heterogeneous environment with an objective of maintaining the quality of service. In order to enable adaptive execution of multiple jobs running co...In a computational grid, jobs must adapt to the dynamically changing heterogeneous environment with an objective of maintaining the quality of service. In order to enable adaptive execution of multiple jobs running concurrently in a computational grid, we propose an integrated performance-based resource management framework that is supported by a multi-agent system （MAS）. The multi-agent system initially allocates the jobs onto different resource providers based on a resource selection algorithm. Later, during runtime, if performance of any job degrades or quality of service cannot be maintained for some reason （resource failure or overloading）, the multi-agent system assists the job to adapt to the system. This paper focuses on a part of our framework in which adaptive execution facility is supported. Adaptive execution facility is availed by reallocation and local tuning of jobs. Mobile, as well as static agents are employed for this purpose. The paper provides a summary of the design and implementation and demonstrates the efficiency of the framework by conducting experiments on a local grid test bed.展开更多
基金support from the project entitled"Developing Multi-Agent System for Performance-Based Resource Brokering and Management in Computational Grid Environment"funded by Department of Science and Technology,Government of India under the SERC scheme
文摘In a computational grid, jobs must adapt to the dynamically changing heterogeneous environment with an objective of maintaining the quality of service. In order to enable adaptive execution of multiple jobs running concurrently in a computational grid, we propose an integrated performance-based resource management framework that is supported by a multi-agent system （MAS）. The multi-agent system initially allocates the jobs onto different resource providers based on a resource selection algorithm. Later, during runtime, if performance of any job degrades or quality of service cannot be maintained for some reason （resource failure or overloading）, the multi-agent system assists the job to adapt to the system. This paper focuses on a part of our framework in which adaptive execution facility is supported. Adaptive execution facility is availed by reallocation and local tuning of jobs. Mobile, as well as static agents are employed for this purpose. The paper provides a summary of the design and implementation and demonstrates the efficiency of the framework by conducting experiments on a local grid test bed.
