Adaptive entitlement control of resource containers on shared servers

In this paper, we describe the design of online feedback control algorithms to dynamically adjust entitlement values for a resource container on a server shared by multiple applications. The goal is to determine the minimum level of entitlement for the container such that its hosted application achieves desired performance levels. Classic control theory is used for both model identification and controller design. Specific implementation issues that affect the closed-loop system performance are discussed. A self-tuning adaptive controller is also presented to handle limited variations in the workload. The controllers were implemented and evaluated on a testbed using the HP-UX PRM as the resource container and the Apache Web server as the hosted application in the container. In all experiments, our controller was able to quickly converge to the proper level of CPU entitlement for the Web server to track its performance target. By using our entitlement control system, shared servers can potentially reach much higher resource utilization while meeting service level objectives for the hosted applications under changing operating conditions.

[1]  Michael Murphy,et al.  Meeting performance goals with the HP-UX workload manager , 2000, WIESS'00.

[2]  Gene F. Franklin,et al.  Digital control of dynamic systems , 1980 .

[3]  Sharad Singhal,et al.  Web2K: Bringing QoS to Web Servers , 2000 .

[4]  Stefan Savage,et al.  Processor capacity reserves: operating system support for multimedia applications , 1994, 1994 Proceedings of IEEE International Conference on Multimedia Computing and Systems.

[5]  P. Marti,et al.  On real-time control tasks schedulability , 2001, 2001 European Control Conference (ECC).

[6]  Nagarajan Kandasamy,et al.  Self-optimization in computer systems via on-line control: application to power management , 2004 .

[7]  Karl Johan Åström,et al.  Adaptive Control , 1989, Embedded Digital Control with Microcontrollers.

[8]  David Mosberger,et al.  httperf—a tool for measuring web server performance , 1998, PERV.

[9]  Yixin Diao,et al.  Using fuzzy control to maximize profits in service level management , 2002, IBM Syst. J..

[10]  Tarek F. Abdelzaher,et al.  Differentiated caching services; a control-theoretical approach , 2001, Proceedings 21st International Conference on Distributed Computing Systems.

[11]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[12]  Xiaoyun Zhu,et al.  Triage: performance isolation and differentiation for storage systems , 2004, Twelfth IEEE International Workshop on Quality of Service, 2004. IWQOS 2004..

[13]  Michael B. Jones,et al.  CPU reservations and time constraints: efficient, predictable scheduling of independent activities , 1997, SOSP.

[14]  Joseph L. Hellerstein,et al.  Using Control Theory to Achieve Service Level Objectives In Performance Management , 2002, Real-Time Systems.

[15]  Yixin Diao,et al.  Feedback Control of Computing Systems , 2004 .

[16]  Xiaoyun Zhu,et al.  Statistical service assurances for applications in utility grid environments , 2004, Perform. Evaluation.

[17]  Klara Nahrstedt,et al.  A control-based middleware framework for quality-of-service adaptations , 1999, IEEE J. Sel. Areas Commun..

[18]  Max Donath,et al.  American Control Conference , 1993 .

[19]  Yixin Diao,et al.  Managing Web server performance with AutoTune agents , 2003 .

[20]  Peter Druschel,et al.  Resource containers: a new facility for resource management in server systems , 1999, OSDI '99.

[21]  Chenyang Lu,et al.  End-to-end utilization control in distributed real-time systems , 2004, 24th International Conference on Distributed Computing Systems, 2004. Proceedings..

[22]  K. Shin,et al.  Performance Guarantees for Web Server End-Systems: A Control-Theoretical Approach , 2002, IEEE Trans. Parallel Distributed Syst..

[23]  Chenyang Lu,et al.  ControlWare: a middleware architecture for feedback control of software performance , 2002, Proceedings 22nd International Conference on Distributed Computing Systems.

[24]  Erich M. Nahum,et al.  Yaksha: a self-tuning controller for managing the performance of 3-tiered Web sites , 2004, Twelfth IEEE International Workshop on Quality of Service, 2004. IWQOS 2004..

[25]  Andrew Warfield,et al.  Xen and the art of virtualization , 2003, SOSP '03.

[26]  S. Parekh,et al.  MIMO control of an Apache web server: modeling and controller design , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[27]  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.

[28]  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).

[29]  Joseph L. Hellerstein,et al.  Feedback control of a Lotus Notes server: modeling and control design , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[30]  Shuichi Oikawa,et al.  Resource kernels: a resource-centric approach to real-time and multimedia systems , 2001, Electronic Imaging.