When Virtual Is Better Than Real
HOTOS '01 Proceedings of the Eighth Workshop on Hot Topics in Operating Systems
PKUAS: An Architecture-Based Reflective Component Operating Platform
FTDCS '04 Proceedings of the 10th IEEE International Workshop on Future Trends of Distributed Computing Systems
Managing Web server performance with AutoTune agents
IBM Systems Journal
Towards Autonomic Computing Middleware via Reflection
COMPSAC '04 Proceedings of the 28th Annual International Computer Software and Applications Conference - Volume 01
Self-adapting service level in Java enterprise edition
Proceedings of the 10th ACM/IFIP/USENIX International Conference on Middleware
Integrating Resource Consumption and Allocation for Infrastructure Resources on-Demand
CLOUD '10 Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing
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An autonomic middleware performs adaptive computations on the fly that bring benefits to the system while consuming additional resources such as CPU and memory. These computations can sometimes interfere with normal business functions of the system due to resource competition, especially when under heavy load. In this paper, we propose an approach to tuning the computation levels and thus controlling the resource costs of the adaptive computations in an autonomic middleware. The tuning (i.e., upgrading or degrading) of the computation levels is performed automatically based on the varying workloads, and the features and gains of the adaptive computations. The essence of our approach is to enable a flexible tradeoff between business functions and adaptive computations by executing the latter dynamically when resources are limited and competed. We present tuning policies and mechanisms to suit different adaptive computations, and implement an automatic tuning framework to investigate our approach. The experiment on the framework indicates that it is effective and efficient to improve the performance of the middleware system.