Modeling and Control Design for Performance Management of Web Servers Via an LPV Approach

This paper presents a control-theoretic approach to the performance management of Internet Web servers to meet service-level agreements. In particular, a CPU frequency management problem is studied to provide response time guarantees with minimal energy cost. It is argued that linear time-invariant modeling and control may not be sufficient for the system to adapt to dynamically varying load conditions. Instead, a Linear-parameter-varying (LPV) approach is presented in this paper, where workload arrival and service parameters are chosen as scheduling parameters to characterize time-varying operating conditions. Modeling the performance management of a Web server as an LPV system has been extensively discussed in this paper; we have derived first-principles models based on analyzing transient and steady-state queueing dynamics as well as empirical models using system identification algorithms. LPV-Hinfin controllers are then designed for the derived LPV system models. Using real Web server workloads, the performance of LPV control compares favorably to various linear control designs and a design based on the conventional queueing theory. The proposed LPV modeling and control framework can be generalized to incorporate more sophisticated workload models and more complicated server environments. In addition, due to the LPV nature of Web systems with respect to load conditions, the proposed approach can be applied to a variety of resource management problems and used for middleware designs

[1]  Joseph L. Hellerstein,et al.  Using Control Theory to Achieve Service Level Objectives In Performance Management , 2001, 2001 IEEE/IFIP International Symposium on Integrated Network Management Proceedings. Integrated Network Management VII. Integrated Management Strategies for the New Millennium (Cat. No.01EX470).

[2]  Eitan Altman,et al.  Congestion control as a stochastic control problem with action delays , 1999, Autom..

[3]  Lui Sha,et al.  Queueing model based network server performance control , 2002, 23rd IEEE Real-Time Systems Symposium, 2002. RTSS 2002..

[4]  John A. Buzacott,et al.  Stochastic models of manufacturing systems , 1993 .

[5]  Yixin Diao,et al.  Using MIMO feedback control to enforce policies for interrelated metrics with application to the Apache Web server , 2002, NOMS 2002. IEEE/IFIP Network Operations and Management Symposium. ' Management Solutions for the New Communications World'(Cat. No.02CH37327).

[6]  Asser N. Tantawi,et al.  Performance management for cluster based Web services , 2003 .

[7]  K. Poolla,et al.  Identification of linear parameter-varying systems via LFTs , 1996, Proceedings of 35th IEEE Conference on Decision and Control.

[8]  Marco Lovera,et al.  Identification of Nonlinear Parametrically Varying Models Using Separable Least Squares , 2003 .

[9]  Yixin Diao,et al.  Comparative studies of load balancing with control and optimization techniques , 2005, Proceedings of the 2005, American Control Conference, 2005..

[10]  Amin Vahdat,et al.  Managing energy and server resources in hosting centers , 2001, SOSP.

[11]  Bassam Bamieh,et al.  Identification of linear parameter varying models , 2002 .

[12]  E. N. Elnozahy,et al.  Energy-Efficient Server Clusters , 2002, PACS.

[13]  A. Robertsson,et al.  Design and evaluation of load control in Web server systems , 2004, Proceedings of the 2004 American Control Conference.

[14]  Chenyang Lu,et al.  An adaptive control framework for QoS guarantees and its application to differentiated caching , 2002, IEEE 2002 Tenth IEEE International Workshop on Quality of Service (Cat. No.02EX564).

[15]  Ronghua Zhang,et al.  Practical application of control theory to Web services , 2004, Proceedings of the 2004 American Control Conference.

[16]  Chenyang Lu,et al.  Proceedings of the Fast 2002 Conference on File and Storage Technologies Aqueduct: Online Data Migration with Performance Guarantees , 2022 .

[17]  P. Apkarian,et al.  Advanced gain-scheduling techniques for uncertain systems , 1997, Proceedings of the 1997 American Control Conference (Cat. No.97CH36041).

[18]  S. Prajna,et al.  A new solution approach to polynomial LPV system analysis and synthesis , 2004, Proceedings of the 2004 American Control Conference.

[19]  Chenyang Lu,et al.  Feedback performance control in software services , 2003 .

[20]  Yixin Diao,et al.  A first-principles approach to constructing transfer functions for admission control in computing systems , 2002, Proceedings of the 41st IEEE Conference on Decision and Control, 2002..

[21]  Yixin Diao,et al.  Optimizing Quality of Service Using Fuzzy Control , 2002, DSOM.

[22]  Hong Chen,et al.  Performance evaluation of scheduling control of queueing networks: Fluid model heuristics , 1995, Queueing Syst. Theory Appl..

[23]  M. Lovera,et al.  Identification of non-linear parametrically varying models using separable least squares , 2004 .

[24]  M. Sznaier,et al.  An LMI approach to control oriented identification of LPV systems , 2000, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

[25]  Anand Sivasubramaniam,et al.  Managing server energy and operational costs in hosting centers , 2005, SIGMETRICS '05.

[26]  Sang Hyuk Son,et al.  Feedback Control Real-Time Scheduling: Framework, Modeling, and Algorithms* , 2001, Real-Time Systems.