Pricing-based strategies for autonomic control of web servers for time-varying request arrivals

This paper considers a web service that receives requests from customers at various rates at different times. The objective is to build an autonomic system that is tuned to different settings based on the varying conditions, both internally and externally. The authors have developed revenue-based pricing as well as admission control strategies, taking into account quality of service issues such as slow down and fairness aspects. Three heuristics are developed in this paper to address the pricing and admission control problem. The three heuristics are: (1) static pricing combined with queue-length-threshold-based admission control; (2) dynamic optimal pricing with no admission control; and (3) static pricing with nonnegative-profit-based admission control. These three strategies are benchmarked against a fourth strategy (called-do nothing) with no pricing and no admission control. The paper evaluates and compares their performance, implementability and computational complexity. The conclusion is that the web server revenue can be significantly increased by appropriately turning away customers via pricing or admission control mechanisms, and this can be done autonomically in the web server.

[1]  Prashant J. Shenoy,et al.  Resource overbooking and application profiling in shared hosting platforms , 2002, OSDI '02.

[2]  Prashant J. Shenoy,et al.  Dynamic resource allocation for shared data centers using online measurements , 2003, IWQoS'03.

[3]  Chenyang Lu,et al.  Modeling and performance control of Internet servers , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[4]  S. Clearwater.  Market-based control: a paradigm for distributed resource allocation , 1996 .

[5]  Rajkumar Buyya,et al.  Economic-based Distributed Resource Management and Scheduling for Grid Computing , 2002, ArXiv.

[6]  Gianfranco Ciardo,et al.  Profit-driven service differentiation in transient environments , 2003, 11th IEEE/ACM International Symposium on Modeling, Analysis and Simulation of Computer Telecommunications Systems, 2003. MASCOTS 2003..

[7]  Yong Liu,et al.  Pricing in multiservice loss networks: static pricing, asymptotic optimality, and demand substitution effects , 2002, TNET.

[8]  Adam Wierman,et al.  Classifying scheduling policies with respect to unfairness in an M/GI/1 , 2003, SIGMETRICS '03.

[9]  Jr. Shaler Stidham.  Pricing and capacity decisions for a service facility: stability and multiple local optima , 1992 .

[10]  Eric Anderson,et al.  Proceedings of the Fast 2002 Conference on File and Storage Technologies Hippodrome: Running Circles around Storage Administration , 2022 .

[11]  David E. Culler,et al.  User-Centric Performance Analysis of Market-Based Cluster Batch Schedulers , 2002, 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID'02).

[12]  LiuYong,et al.  Pricing in multiservice loss networks , 2002 .

[13]  Sang Hyuk Son,et al.  A feedback control approach for guaranteeing relative delays in Web servers , 2001, Proceedings Seventh IEEE Real-Time Technology and Applications Symposium.

[14]  Sridhar Seshadri,et al.  Performance Analysis for E-Business: Impact of Long Range Dependence , 2002, Electron. Commer. Res..

[15]  Mor Harchol-Balter,et al.  Web servers under overload: How scheduling can help , 2006, TOIT.

[16]  Tao Yang,et al.  Proceedings of the 5th Symposium on Operating Systems Design and Implementation Integrated Resource Management for Cluster-based Internet Services , 2022 .

[17]  StidhamShaler.  Pricing and Capacity Decisions for a Service Facility , 1992 .

[18]  Prashant J. Shenoy,et al.  A practical learning-based approach for dynamic storage bandwidth allocation , 2003, IWQoS'03.

[19]  Bernardo A. Huberman,et al.  The ecology of computation , 1988, Digest of Papers. COMPCON Spring 89. Thirty-Fourth IEEE Computer Society International Conference: Intellectual Leverage.