Autocorrelation-driven load control in distributed systems

In this paper, we propose a new approach for the development of load control policies in autonomic multitier systems. We control system load in a completely new way compared to existing policies: we leverage on the autocorrelation of service times and show that autocorrelation can be used to forecast future service requirements of requests and adaptively control system load. To the best of our knowledge, this is the first direct application of autocorrelation of service times to autonomic load control. We propose ALoC and D ALoC, two autocorrelation-driven policies that drop a percentage of the load in order to meet pre-defined quality-of-service levels in a distributed system. Both policies are easy to implement and rely on minimal assumptions. In particular, D ALoC is a fully no-knowledge measurement-based policy that self-adjusts its load control parameters based only on policy targets and on statistical information of requests served in the past. We illustrate the effectiveness of these new policies in a distributed multi-server setting via detailed trace driven simulations. We show that if these policies are employed in the server with a temporal dependent service process, then end-to-end response time, across all servers, reduces up to 80% by only dropping at most 13% of the incoming requests. Using real traces, we also show that, in the constrained case of being able to drop only from a portion of the incoming workload, our policy still improves request response time by up to 30%.

[1]  Xiannong Meng.  Book Review: Computer Networks and Systems: Queueing Theory and Performance Evaluation by Thomas G. Robertazzi (Springer-Verlag, 1990) , 1992, PERV.

[2]  Jan Beran,et al.  Statistics for long-memory processes , 1994 .

[3]  Evgenia Smirni,et al.  Burstiness in Multi-tier Applications: Symptoms, Causes, and New Models , 2008, Middleware.

[4]  Michael Muskulus,et al.  Analysis and modeling of job arrivals in a production grid , 2007, PERV.

[5]  Cathy H. Xia,et al.  Load shedding and distributed resource control of stream processing networks , 2007, Perform. Evaluation.

[6]  Ludmila Cherkasova,et al.  Session-Based Admission Control: A Mechanism for Peak Load Management of Commercial Web Sites , 2002, IEEE Trans. Computers.

[7]  Bianca Schroeder,et al.  Understanding disk failure rates: What does an MTTF of 1,000,000 hours mean to you? , 2007, TOS.

[8]  David Finkel.  Brief review: Computer Networks & Systems: Queueing Theory and Performance Evaluation by Thomas Robertazzi (Springer-Verlag, 1990) , 1991, PERV.

[9]  Sally Floyd,et al.  Wide-Area Traffic: The Failure of Poisson Modeling , 1994, SIGCOMM.

[10]  Ningfang Mi.  Performance impacts of autocorrelated flows in multi-tiered systems , 2007, PERV.

[11]  Virgílio A. F. Almeida,et al.  Capacity Planning for Web Performance: Metrics, Models, and Methods , 1998 .

[12]  Walter Willinger,et al.  Experimental queueing analysis with long-range dependent packet traffic , 1996, TNET.

[13]  Eric A. Brewer,et al.  Self-similarity in file systems , 1998, SIGMETRICS '98/PERFORMANCE '98.

[14]  David E. Culler,et al.  SEDA: an architecture for well-conditioned, scalable internet services , 2001, SOSP.

[15]  Sally Floyd,et al.  Wide area traffic: the failure of Poisson modeling , 1995, TNET.

[16]  Ward Whitt,et al.  The Influence of Service-Time Variability in a Closed Network of Queues , 1986, Perform. Evaluation.

[17]  Amarnath Mukherjee,et al.  On resource management and QoS guarantees for long range dependent traffic , 1995, Proceedings of INFOCOM'95.

[18]  Attahiru Sule Alfa,et al.  On approximating higher order MAPs with MAPs of order two , 1999, Queueing Syst. Theory Appl..

[19]  B. Welford.  Note on a Method for Calculating Corrected Sums of Squares and Products , 1962 .

[20]  Qi Zhang,et al.  Performance impacts of autocorrelated flows in multi-tiered systems , 2007, Perform. Evaluation.

[21]  Alma Riska,et al.  Long-Range Dependence at the Disk Drive Level , 2006, Third International Conference on the Quantitative Evaluation of Systems - (QEST'06).

[22]  Asser N. Tantawi,et al.  An analytical model for multi-tier internet services and its applications , 2005, SIGMETRICS '05.

[23]  ThummlerAxel,et al.  A Novel Approach for Phase-Type Fitting with the EM Algorithm , 2006 .

[24]  Harry G. Perros,et al.  Equivalencies Between Open and Closed Queueing Networks with Finite Buffers , 1989, Perform. Evaluation.

[25]  Peter Buchholz,et al.  A Novel Approach for Phase-Type Fitting with the EM Algorithm , 2006, IEEE Transactions on Dependable and Secure Computing.