Concepts and experiments in computational reflection
OOPSLA '87 Conference proceedings on Object-oriented programming systems, languages and applications
The art of metaobject protocol
The art of metaobject protocol
Monitoring, security, and dynamic configuration with the dynamicTAO reflective ORB
IFIP/ACM International Conference on Distributed systems platforms
Composing crosscutting concerns using composition filters
Communications of the ACM
Preserving QoS of e-commerce sites through self-tuning: a performance model approach
Proceedings of the 3rd ACM conference on Electronic Commerce
Supporting Unanticipated Dynamic Adaptation of Application Behaviour
ECOOP '02 Proceedings of the 16th European Conference on Object-Oriented Programming
Efficient Evaluation of Alternatives for Assembly of Services
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 15 - Volume 16
Spidernet: a quality-aware service composition middleware
Spidernet: a quality-aware service composition middleware
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High dynamic computing environments make QoS guarantee more important for component-based distributed system. Software system should possess self-tuning capacity for reacting to external environment variation. This paper firstly analyzes a model of component resource requirement and dependence relations among them those affect the quality of service. Then we propose an adaptive middleware framework that automatically tune server configuration parameters and react to workload changes to preserve the quality of service the application requires. The key of this framework is adopting backtracking algorithms, and it guides to search for the best combination of configuration parameters to satisfy application requirement and QoS requirement. Finally, it is prototyped on tomcat application server and validated using an information query system by comparing the performance requirement satisfaction with and without this framework. The results show that the application performance can be improved through this adaptive framework's regulation when the workload increases.