Flexible self-adjustment of task deployment in dynamic environments
Multiagent and Grid Systems
Policy-driven self-management for an automotive middleware
HotAC II Hot Topics in Autonomic Computing on Hot Topics in Autonomic Computing
Towards Scheduling Virtual Machines Based On Direct User Input
VTDC '06 Proceedings of the 2nd International Workshop on Virtualization Technology in Distributed Computing
A versatile policy toolkit supporting run-time policy reconfiguration
Cluster Computing
Policy-based autonomic computing with integral support for self-stabilisation
International Journal of Autonomic Computing
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Policies are being increasingly used as a means of implementing autonomic computing features in IT systems. Since these policies consume system resources, the performance of a policy-based system may be hampered if resource intensive policies are scheduled at the same time when other policies and applications are being executed. However, with applications being accessed by users globally across time zones, and fast changing business needs, it is increasingly difficult to identify and maintain suitable schedules for these policies. In this paper we propose a framework to address the above aspect of the policy-based autonomic computing -- the problem of determining when to schedule a given policy such that its impact on system performance is minimized, and then giving appropriate feedback. This feedback is aimed at assisting the policy maker in defining or redefining the policy schedule so that it can be executed more effi- ciently. In this framework, we make use of the underlying log data (i.e. resource utilization data) of a managed resource in order to determine an appropriate policy schedule. We demonstrate the efficacy of our approach using DB2 as a managed resource and policies for data management.