Enhancing revenue maximization with adaptive WRR

In the future converged wireless and wired networks will be required to provide support to a number of different types of traffic, each with its own particular characteristics and quality of service parameters including e.g. guaranteed bandwidth, jitter, and latency. The customers of different classes pay different prices to the service provider, who must share resources in a plausible way. Differentiation can be implemented by using a multiqueue system, where each queue corresponds to one service class. In this paper, an adaptive weighted round robin (WRR) based algorithm for traffic allocation is presented and studied in the single node case. The weights in the adaptive gradient type WRR algorithm are updated using revenue as a target function. Due to the adaptive nature of the algorithm, it can operate in the nonstationary environments. In addition, it is nonparametric and deterministic in the sense that any assumptions about call density functions or duration distributions are not made.

[1]  Jian Zhang,et al.  Revenue-maximization-based adaptive WFQ , 2002, SPIE/OSA/IEEE Asia Communications and Photonics.

[2]  Abhay Parekh,et al.  A generalized processor sharing approach to flow control in integrated services networks-the single node case , 1992, [Proceedings] IEEE INFOCOM '92: The Conference on Computer Communications.

[3]  Van Jacobson,et al.  Link-sharing and resource management models for packet networks , 1995, TNET.

[4]  Hui Zhang,et al.  WF/sup 2/Q: worst-case fair weighted fair queueing , 1996, Proceedings of IEEE INFOCOM '96. Conference on Computer Communications.

[5]  R. Shreedhar,et al.  Efficient Fair Queuing Using Deficit Round - , 1997 .

[6]  Ion Stoica,et al.  Providing guaranteed services without per flow management , 1999, SIGCOMM '99.

[7]  P. V. Ushakumari,et al.  On the queueing system , 1998 .

[8]  C. Dovrolis,et al.  Proportional differentiated services, part II: loss rate differentiation and packet dropping , 2000, 2000 Eighth International Workshop on Quality of Service. IWQoS 2000 (Cat. No.00EX400).

[9]  S. Jamaloddin Golestani,et al.  A self-clocked fair queueing scheme for broadband applications , 1994, Proceedings of INFOCOM '94 Conference on Computer Communications.

[10]  Timo Hämäläinen,et al.  QoS aware adaptive pricing for network services , 2001, GLOBECOM'01. IEEE Global Telecommunications Conference (Cat. No.01CH37270).

[11]  Timo Hämäläinen,et al.  Optimal link allocation and revenue maximization , 2002, Journal of Communications and Networks.

[12]  Yau-Hwang Kuo,et al.  An adaptive approach to weighted fair queue with QoS enhanced on IP network , 2001, Proceedings of IEEE Region 10 International Conference on Electrical and Electronic Technology. TENCON 2001 (Cat. No.01CH37239).

[13]  Scott Shenker,et al.  Core-stateless fair queueing: achieving approximately fair bandwidth allocations in high speed networks , 1998, SIGCOMM '98.

[14]  Parameswaran Ramanathan,et al.  Proportional differentiated services: delay differentiation and packet scheduling , 2002, TNET.

[15]  Abhay Parekh,et al.  A generalized processor sharing approach to flow control in integrated services networks: the single-node case , 1993, TNET.