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The current and important question for Internet is how to assure the quality of service. Several protocols have been proposed to support different classes of network traffic. The open research problem is how to divide available bandwidth among those traffic classes to support their Quality of Service requirements. A major challenge in this area is developing algorithms that can handle situations in which we do not know the traffic intensities in all traffic classes in advance or those intensities are changing with time. In this paper we formulate the problem and next propose the reinforcement learning algorithm to solve it. The reinforcement function proposed is evaluated and compared to other methods.
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Tom
Strony
53--65
Opis fizyczny
Bibliogr. 12 poz., wykr.
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autor
autor
- Wrocław University of Technology, Institute of Informatics, Wyb. Wyspiańskiego 27, 50-370 Wrocław, Poland, Halina.Kwasnicka@pwr.wroc.pl
Bibliografia
- [1] Anker T., Cohen R., Dolev D., Singer Y., Prfq: Probabilistic fair queuing, Technical Report CS-2000-30, Institute of Computer Science, The Hebrew University of Jerusalem, Israel, 2000.
- [2] Babuska R., Busoniu L., De Schutter B., Reinforcement learning for multi-agent systems, Technical Report 06-041, Delft Center for Systems and Control, Delft University of Technology, July 2006; paper for a keynote presentation at the 11th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2006), Prague, Czech Republic, September 2006.
- [3] Blake S., Black D., Carlson M., Davies E., Wang Z., Weiss W., An architecture for differentiated service, RFC Editor, USA, 1998.
- [4] Chang H.S., Fu M.C., Hu J., Marcus S.I., Simulation-based Algorithms for Markov Decision Processes (Communications and Control Engineering), Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2007.
- [5] Cisco IOS Documentation. Quality of service solution guide, implementing diffserv for end-to-end quality of service, Release 12.2.2002, pp. 371-392.
- [6] Ferra H.L., Lau, K. Leckie C., Tang A., Applying reinforcement learning to packet scheduling in routers, In IAAI, 2003, pp. 79-84.
- [7] Hall J., Mars P., Satisfying QoS with a learning based scheduling algorithm, 6th International Workshop on Quality of Service, 2000, pp. 171-176.
- [8] Kaelbling L.P., Littman M.L., Moore A.P., Reinforcement learning: A survey, Journal of Artificial Intelligence Research, 4, 1996, pp. 237-285.
- [9] Shenker S., Partridge C., Guerin R., Specification of guaranteed quality of service, RFC 2212, 1997.
- [10] Sutton R.S., Barto A.G., Reinforcement Learning: An Introduction, MIT Press, Cambridge, MA, 1998.
- [11] Wang H., Shen C., Shin K., Adaptive-weighted packet scheduling for premium service, Proceedings of IEEE International Conference on Communications, 2001.
- [12] Zhang H., Service disciplines for guaranteed performance service in packet-switching networks, Proc. IEEE, October 1995, pp. 1374-1396.
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Bibliografia
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bwmeta1.element.baztech-article-BAT5-0042-0017