Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Future Generation Computer Systems - Special issue on metacomputing
The Vision of Autonomic Computing
Computer
Adaptive Computing on the Grid Using AppLeS
IEEE Transactions on Parallel and Distributed Systems
GridG: generating realistic computational grids
ACM SIGMETRICS Performance Evaluation Review
Grid Information Services for Distributed Resource Sharing
HPDC '01 Proceedings of the 10th IEEE International Symposium on High Performance Distributed Computing
A decoupled scheduling approach for Grid application development environments
Journal of Parallel and Distributed Computing - Special issue on computational grids
The dawning of the autonomic computing era
IBM Systems Journal
Using Divisible Load Theory to Dimension Optical Transport Networks for Grid Excess Load Handling
ICAS-ICNS '05 Proceedings of the Joint International Conference on Autonomic and Autonomous Systems and International Conference on Networking and Services
A resource manager for optimal resource selection and fault tolerance service in Grids
CCGRID '04 Proceedings of the 2004 IEEE International Symposium on Cluster Computing and the Grid
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In this paper, a distributed and scalable Grid service management architecture is presented. The proposed architecture is capable of monitoring task submission behaviour and deriving Grid service class characteristics, for use in performing automated computational, storage and network resource-to-service partitioning. This partitioning of Grid resources amongst service classes (each service class is assigned exclusive usage of a distinct subset of the available Grid resources), along with the dynamic deployment of Grid management components dedicated and tuned to the requirements of a particular service class introduces the concept of Virtual Private Grids. We present two distinct algorithmic approaches for the resource partitioning problem, the first based on Divisible Load Theory (DLT) and the second built on Genetic Algorithms (GA). The advantages and drawbacks of each approach are discussed and their performance is evaluated on a sample Grid topology using NSGrid, an ns-2 based Grid simulator. Results show that the use of this Service Management Architecture in combination with the proposed algorithms improves computational and network resource efficiency, simplifies schedule making decisions, reduces the overall complexity of managing the Grid system, and at the same time improves Grid QoS support (with regard to job response times) by automatically assigning Grid resources to the different service classes prior to scheduling.