Joe Hoffert
ABSTRACT
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.
- }}J. Hoffert, A. Gokhale, and D. Schmidt, "Evaluating Transport Protocols for Real-time Event Stream Processing Middleware and Applications," in Proceedings of the 11th International Symposium on Distributed Objects, Middleware, and Applications (DOA '09), Vilamoura, Algarve-Portugal, Nov. 2009. Google Scholar
Digital Library
- }}M. Chandy, O. Etzion, R. von Ammon, and P. Niblett, "07191 summary --- event processing," in Event Processing, ser. Dagstuhl Seminar Proceedings, M. Chandy, O. Etzion, and R. von Ammon, Eds., no. 07191. Dagstuhl, Germany: Internationales Begegnungs- und Forschungszentrum für Informatik (IBFI), Schloss Dagstuhl, Germany, 2007.Google Scholar
- }}R. Sutton and A. Barto, Reinforcement Learning: An Introduction. The MIT Press, March 1998. Google ScholarDigital Library
- }}X. Bu, J. Rao, and C.-Z. Xu, "A reinforcement learning approach to online web systems auto-configuration," in ICDCS '09: Proceedings of the 2009 29th IEEE International Conference on Distributed Computing Systems. Washington, DC, USA: IEEE Computer Society, 2009, pp. 2--11. Google ScholarDigital Library
- }}T. M. Mitchell, Machine Learning. NewYork: McGraw-Hill, 1997. Google ScholarDigital Library
- }}D. W. Patterson, Artificial Neural Networks: Theory and Applications. Upper Saddle River, NJ, USA: Prentice Hall PTR, 1998. Google ScholarDigital Library
- }}S. E. Institute, "Ultra-Large-Scale Systems: Software Challenge of the Future," Carnegie Mellon University, Pittsburgh, PA, USA, Tech. Rep., June 2006.Google Scholar
- }}P. Grace, G. Coulson, G. S. Blair, and B. Porter, "A distributed architecture meta-model for self-managed middleware," in Proceedings of the 5th workshop on Adaptive and reflective middleware (ARM '06). New York, NY, USA: ACM, 2006, p. 3. Google ScholarDigital Library
- }}P. Vienne and J.-L. Sourrouille, "A middleware for autonomic qos management based on learning," in SEM '05: Proceedings of the 5th international workshop on Software engineering and middleware. New York, NY, USA: ACM, 2005, pp. 1--8. Google ScholarDigital Library
- }}P.-C. David and T. Ledoux, Software Composition. Berlin / Heidelberg: Springer LNCS, 2006, ch. An Aspect-Oriented Approach for Developing Self-Adaptive Fractal Components, pp. 82--97. Google ScholarDigital Library
Index Terms
- Adapting and evaluating distributed real-time and embedded systems in dynamic environments
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