Abstract
As the complexity of IT systems increases, performance management and capacity planning become the largest and most difficult expenses to control. New methodologies and modeling techniques that explain large-system behavior and help predict their future performance are now needed to effectively tackle the emerging performance issues. With the multi-tier architecture paradigm becoming an industry standard for developing scalable client-server applications, it is important to design effective and accurate performance prediction models of multi-tier applications under an enterprise production environment and a real workload mix. To accurately answer performance questions for an existing production system with a real workload mix, we design and implement a new capacity planning and anomaly detection tool, called R-Capriccio, that is based on the following three components: i) a Workload Profiler that exploits locality in existing enterprise web workloads and extracts a small set of most popular, core client transactions responsible for the majority of client requests in the system; ii) a Regression-based Solver that is used for deriving the CPU demand of each core transaction on a given hardware; and iii) an Analytical Model that is based on a network of queues that models a multi-tier system. To validate R-Capriccio, we conduct a detailed case study using the access logs from two heterogeneous production servers that represent customized client accesses to a popular and actively used HP Open View Service Desk application.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Ari, B., Giivenir, H.A.: Clustered Linear Regression. Knowledge-Based Systems 15(3) (2002)
Capacity Planning for WebLogic Portal, http://edocs.bea.com/wlp/docs81/capacityplanning/capacityplanning.html
The Workload for the SPECweb96 Benchmark, http://www.specbench.org/osg/web96/workload.html
TPC-W Benchmark, http://www.tpc.org
Arlitt, M., Williamson, C.: Web Server Workload Characterization: The Search for Invariants. In: Proc. of the ACM SIGMETRICS 1996 Conference, Philadelphia, PA (May 1996)
Almeida, V., Bestavros, A., Crovella, M., de Oliveira, A.: Characterizing Reference Locality in the WWW. Technical Report, Boston University, TR-96-11 (1996)
Arlitt, M., Krishnamurthy, D., Rolia, J.: Characterizing the Scalability of a Large Web-based Shopping System. J. ACM Transactions on Internet Technology 1(1) (August 2001)
Menasce, D., Almeida, V., Dowdy, L.: Capacity Planning and Performance Modeling: from mainframes to client-server systems. Prentice Hall, Englewood Cliffs (1994)
Kachigan, T.M.: A Multi-Dimensional Approach to Capacity Planning. In: Proc. of CMG Conference 1980, Boston, MA (1980)
Cherkasova, L., Tang, W.: Sizing the Streaming Media Cluster Solution for a Given Workload. In: CCGrid 2004. Proc. of the 4th IEEE/ACM, Chicago, USA (2004)
Rolia, J., Cherkasova, L., Arlitt, M., Andrzejak, A.: A Capacity Management Service for Resource Pools. In: Proc. of the Fifth Int. Workshop on Software and Performance (2005)
Chase, J.S., Anderson, D., Thakar, P., Vahdat, A., Doyle, R.: Managing Energy and Server Resources in Hosting Centers. In: SOSP. Proc. of the 18th ACM Symposium on Operating System Principles (2001)
Sarris, D., Hofer, J.: Capacity Planning for e-Commerce Systems With Benchmark Factory, www.dlt.com/quest/
Klerk, L., Bender, J.: Capacity Planning. Microsoft TechNet (2000), http://www.microsoft.com/technet/archive/itsolutions/ecommerce
PHP HyperText preprocessor, www.php.net
Villela, D., Pradhan, P., Rubenstein, D.: Provisioning Servers in the Application Tier for E-Commerce Systems. In: Proc. of IWQoS 2004, Montreal, Canada (2004)
Urgaonkar, B., Shenoy, P., Chandra, A., Goyal, P.: Dynamic Provisioning of Multi-tier Internet Applications. In: ICAC 2005. In Proc. of the 2nd IEEE International Conference on Autonomic Computing, Seattle (June 2005)
Urgaonkar, B., Pacifici, G., Shenoy, P., Spreitzer, M., Tantawi, A.: An Analytical Model for Multi-tier Internet Services and its Applications. In: Proc. of the ACM SIGMETRICS’2005, Banff, Canada (June 2005)
Ranjan, S., Rolia, J., Fu, H., Knightly, E.: QoS-Driven Server Migration for Internet Data Centers. In: Proc. of IWQoS 2002, Miami, FL (May 2002)
Rolia, J., Vetland, V.: Correlating Resource Demand Information with ARM Data for Application Services. In: Proc. of the ACM Workshop on Software and Performance (1998)
Kelly, T.: Detecting Performance Anomalies in Global Applications. In: WORLDS 2005. Second Workshop on Real, Large Distributed Systems (2005)
Jain, R.: The Art of Computer Systems Performance Analysis: Techniques for experimental Design, Measurement, Simulation, and Modeling. Wiley-Interscience, NY (1991)
Stewart, C., Kelly, T., Zhang, A.: Exploiting Nonstationarity for Performance Prediction. In: Proc. of EuroSys 2007, Lisbon, Portugal (March 2007)
TPC-W Benchmark, http://www.tpc.org
Zhang, Q., Cherkasova, L., Smirni, E.: A Regression-Based Analytic Model for Dynamic Resource Provisioning of Multi-Tier Applications. In: ICAC 2007. Proc. of the Fourth International Conference on Autonomic Computing, Jacksonville, FL, p. 27 (2007)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 IFIP International Federation for Information Processing
About this paper
Cite this paper
Zhang, Q., Cherkasova, L., Mathews, G., Greene, W., Smirni, E. (2007). R-Capriccio: A Capacity Planning and Anomaly Detection Tool for Enterprise Services with Live Workloads. In: Cerqueira, R., Campbell, R.H. (eds) Middleware 2007. Middleware 2007. Lecture Notes in Computer Science, vol 4834. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76778-7_13
Download citation
DOI: https://doi.org/10.1007/978-3-540-76778-7_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76777-0
Online ISBN: 978-3-540-76778-7
eBook Packages: Computer ScienceComputer Science (R0)