Effective fault tolerance for agent-based cluster computing
Journal of Systems and Software
Self-Adaptability and Man-in-the-Loop: A Dilemma in Autonomic Computing Systems
DEXA '04 Proceedings of the Database and Expert Systems Applications, 15th International Workshop
Usable Autonomic Computing Systems: The Administrator's Perspective
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
Dynamic security reconfiguration for the semantic web
Engineering Applications of Artificial Intelligence
Resolving scheduling issues of the london underground using a multi-agent system
HoloMAS'05 Proceedings of the Second international conference on Holonic and Multi-Agent Systems for Manufacturing
Finding, expressing and managing parallelism in programs executed on clusters of workstations
Computer Communications
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Non-dedicated loosely coupled systems are popular platforms for cluster-and grid-based parallel processing, fundamentally because they have good cost-performance ratios and are scalable. However, these platforms represent highly dynamic environments in which performance and efficiency can be seriously impacted by changes in environmental conditions. This is especially significant where the run-time configuration has been determined statically, either at compilation time or at the start of execution. This paper introduces the concept of agile parallel processing in which the application manages several aspects of its own run-time behaviour, including deployment granularity. This approach reduces the emphasis on the preconfiguration of components, and relies instead on inbuilt learning and discovery capabilities. To facilitate investigation into the extent to which a self-managing approach can be beneficial to parallel processing, an experimental framework has been developed. The framework provides a range of services such as dynamic worker discovery and performance calibration, and policy-controlled facilities such as resource management and adaptation to suit environmental conditions. The framework integrates these services with the parallel application code. The operation and performance of policy-based dynamic deployment scheduling in dynamic environments is analysed in detail.