Adaptive scheduling method for maximizing revenue in flat pricing scenario

Abstract End-to-end quality of service is critical to the success of current and future networked applications. Applications, such as real-time actions and transactions, should be given priority over less critical ones (such as web surfing). Furthermore, many multimedia applications require delay or delay variation guarantees for acceptable performance. Weighted fairness is also important both among customers or aggregates (depending on the tariff or subscription), and also within an aggregate (for example, to prevent starvation among sessions or service categories). This paper presents an adaptive scheduling algorithm for the traffic allocation. We use flat pricing scenario in our model, and the weights of the queues are updated using revenue as a target function. Due to the closed form 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 connection density functions or duration distributions are not made.

[1]  Scott Shenker,et al.  Integrated Services in the Internet Architecture : an Overview Status of this Memo , 1994 .

[2]  H. Mendelson,et al.  User delay costs and internal pricing for a service facility , 1990 .

[3]  Robin Mason,et al.  Internet service classes under competition , 2000, IEEE Journal on Selected Areas in Communications.

[4]  L. McKnight,et al.  Internet economics , 1997 .

[5]  Frank Kelly,et al.  Charging and rate control for elastic traffic , 1997, Eur. Trans. Telecommun..

[6]  Ioannis Ch. Paschalidis.  Class-specific quality of service guarantees in multimedia communication networks , 1999, Autom..

[7]  Frank Kelly,et al.  On tariffs, policing and admission control for multiservice networks , 1993, Oper. Res. Lett..

[8]  F. Kelly,et al.  Stochastic networks : theory and applications , 1996 .

[9]  Frank Kelly,et al.  Rate control for communication networks: shadow prices, proportional fairness and stability , 1998, J. Oper. Res. Soc..

[10]  J. Tsitsiklis,et al.  On the large deviations behavior of acyclic networks of $G/G/1$ queues , 1998 .

[11]  David L. Black,et al.  An Architecture for Differentiated Service , 1998 .

[12]  Ketil Danielsen,et al.  User Control Modes and IP Allocation , 1995 .

[13]  Timo Hämäläinen,et al.  Adaptive weighted fair scheduling method for channel allocation , 2003, IEEE International Conference on Communications, 2003. ICC '03..

[14]  Andrew M. Odlyzko,et al.  Paris metro pricing for the internet , 1999, EC '99.

[15]  John N. Tsitsiklis,et al.  Congestion-dependent pricing of network services , 2000, TNET.

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

[17]  Timo Hämäläinen,et al.  Enhancing revenue maximization with adaptive WRR , 2003, Proceedings of the Eighth IEEE Symposium on Computers and Communications. ISCC 2003.

[18]  Andrew Whinston,et al.  A Stochastic Equilibrium Model of Internet Pricing , 1997 .

[19]  Hui Zhang,et al.  Service disciplines for guaranteed performance service in packet-switching networks , 1995, Proc. IEEE.

[20]  Timo Hämäläinen,et al.  QoS- and revenue aware adaptive scheduling algorithm , 2004, Journal of Communications and Networks.

[21]  Jon M. Peha,et al.  Cost-based scheduling and dropping algorithms to support integrated services , 1996, IEEE Trans. Commun..

[22]  Richard J. Gibbens,et al.  Resource pricing and the evolution of congestion control , 1999, at - Automatisierungstechnik.

[23]  Frank Kelly,et al.  Notes on effective bandwidths , 1994 .

[24]  Deborah Estrin,et al.  Pricing in computer networks: motivation, formulation, and example , 1993, TNET.

[25]  Chita R. Das,et al.  Providing fairness in DiffServ architecture , 2002, Global Telecommunications Conference, 2002. GLOBECOM '02. IEEE.