Deterministic Learning Automata Solutions to the Equipartitioning Problem
IEEE Transactions on Computers
Merging the CCA Component Model with the OGSI Framework
CCGRID '03 Proceedings of the 3st International Symposium on Cluster Computing and the Grid
Supporting timeliness and accuracy in distributed real-time content-based video analysis
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
A Fixed-Structure Learning Automaton Solution to the Stochastic Static Mapping Problem
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 18 - Volume 19
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As advancements in grid computing continue to support a variety of parallel and distributed systems, the issue of large-scale application scheduling is becoming a key concern. In particular, a class of quality-sensitive applications that requires to fulfil some quality requirements in order to satisfy the user needs. There is a growing need to support these applications as they perform unacceptably if platform resources are scarce or if the deployment is not carefully configured and tuned for the anticipated load. In this paper, we present the notion of application service planning where the application is a composition of service components representing their corresponding functionality, and is configured on the grid environment such that all services involved in the composition get sufficient resources to allow them to deliver at least the minimum required quality to be useful for the application. Addressing such a service planning problem, we summarise a quality deviation model and a learning automaton based solution techniques as a flavour of our research.