Artificial Neural Networks: Theory and Applications
Artificial Neural Networks: Theory and Applications
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A middleware for autonomic QoS management based on learning
SEM '05 Proceedings of the 5th international workshop on Software engineering and middleware
A distributed architecture meta-model for self-managed middleware
Proceedings of the 5th workshop on Adaptive and reflective middleware (ARM '06)
A Reinforcement Learning Approach to Online Web Systems Auto-configuration
ICDCS '09 Proceedings of the 2009 29th IEEE International Conference on Distributed Computing Systems
Evaluating Transport Protocols for Real-Time Event Stream Processing Middleware and Applications
OTM '09 Proceedings of the Confederated International Conferences, CoopIS, DOA, IS, and ODBASE 2009 on On the Move to Meaningful Internet Systems: Part I
An aspect-oriented approach for developing self-adaptive fractal components
SC'06 Proceedings of the 5th international conference on Software Composition
Adapting distributed real-time and embedded pub/sub middleware for cloud computing environments
Proceedings of the ACM/IFIP/USENIX 11th International Conference on Middleware
Timely Autonomic Adaptation of Publish/Subscribe Middleware in Dynamic Environments
International Journal of Adaptive, Resilient and Autonomic Systems
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Quality of Service (QoS)-enabled publish/subscribe (pub/- sub) middleware provides much needed infrastructure for data dissemination in distributed real-time and embedded (DRE) systems. It is hard, however, to quantify the performance of mechanisms that support multiple interrelated QoS concerns, e.g., reliability, latency, and jitter. Moreover, once an appropriate mechanism is selected, it is hard to maintain QoS properties as the operating environment fluctuates since the chosen mechanism might no longer provide the needed QoS. For DRE systems operating in such environments, adjustments to mechanisms supporting QoS must be both timely and resilient to unforeseen environments. This paper describes our work to (1) define composite metrics to evaluate multiple interrelated QoS concerns and (2) analyze various adjustment techniques ( i.e., policy-based approaches, machine learning techniques) used for the QoS mechanisms of a DRE system in a dynamic environment. Our results show that (1) composite metrics quantify the support that mechanisms provide for multiple QoS concerns to ease mechanism evaluation and creation of related composite metrics and (2) neural network machine learning techniques provide the constant-time complexity needed for DRE pub/- sub systems to determine adjustments and the robustness to handle unknown environments.