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Exploiting Service Usage Information for Optimizing Server Resource Management

Published:01 July 2011Publication History
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Abstract

It is often difficult to tune the performance of modern component-based Internet services because: (1) component middleware are complex software systems that expose several independently tuned server resource management mechanisms; (2) session-oriented client behavior with complex data access patterns makes it hard to predict what impact tuning these mechanisms has on application behavior; and (3) component-based Internet services themselves exhibit complex structural organization with requests of different types having widely ranging execution complexity. In this article we show that exposing and using detailed information about how clients use Internet services enables mechanisms that achieve two interconnected goals: (1) providing improved QoS to the service clients, and (2) optimizing server resource utilization. To differentiate among levels of service usage (service access) information, we introduce the notion of the service access attribute and identify four related groups of service access attributes, encompassing different aspects of service usage information, ranging from the high-level structure of client web sessions to low-level fine-grained information about utilization of server resources by different requests. To show how the identified service usage information can be collected, we implement a request profiling infrastructure in the JBoss Java application server. In the context of four representative service management problems, we show how collected service usage information is used to improve service performance, optimize server resource utilization, or to achieve other problem-specific service management goals.

References

  1. Akkerman, A., Totok, A., and Karamcheti, V. 2005. Infrastructure for automatic dynamic deployment of J2EE applications in distributed environments. In Proceedings of the 3rd International Working Conference on Component Deployment (CD’05). Lecture Notes in Computer Science, vol. 3798, Springer, Berlin. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Akula, V. and Menascé, D. 2007. Two-level workload characterization of online auctions. Electron. Commerce Res. Appl. 6, 2, 192--208. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Barnes, D. and Mookerjee, V. 2009. Customer delay in e-commerce sites: Design and strategic implications. In Business Computing, Handbooks in Information Systems, vol. 3, G. Adomavicius and A. Gupta Eds., Emerald Group Publishing, Bradford, England, 74--85.Google ScholarGoogle Scholar
  4. Cecchet, E., Marguerite, J., and Zwaenepoel, W. 2002. Performance and scalability of EJB applications. ACM SIGPLAN Not. 37, 11, ACM, New York. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Chen, M., Kiciman, E., Fratkin, E., Brewer, E., and Fox, A. 2002. Pinpoint: Problem determination in large, dynamic, Internet services. In Proceedings of the International Conference on Dependable Systems and Networks (DSN’02). IEEE Computer Society, Los Alamitos, CA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Chen, X., Mohapatra, P., and Chen, H. 2001. An admission control scheme for predictable server response time for web accesses. In Proceedings of the International World WideWeb Conference (WWW’01). ACM, New York. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Cherkasova, L. and Phaal, P. 2002. Session-based admission control: A mechanism for peak load management of commercial web sites. IEEE Trans. Computers 51, 6, 669--685. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. EJB. 2011. Enterprise JavaBeans Technology. http://www.oracle.com/technetwork/java/index-jsp-140203.html.Google ScholarGoogle Scholar
  9. Elnikety, S., Nahum, E., Tracey, J., and Zwaenepoel, W. 2004. A method for transparent admission control and request scheduling in dynamic e-commerce web sites. In Proceedings of the International World Wide Web Conference (WWW’04). ACM, New York. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Fleury, M. and Reverbel, F. 2003. The JBoss extensible server. In Proceedings of the 4th ACM/IFIP/USENIX International Middleware Conference. Lecture Notes in Computer Science, vol. 2672, Springer, Berlin. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Gao, L., Dahlin, M., Nayate, A., Zheng, J., and Iyengar, A. 2003. Application specific data replication for edge services. In Proceedings of the International World Wide Web Conference (WWW’03). ACM, New York. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Gao, L., Dahlin, M., Zheng, J., Alvisi, L., and Iyengar, A. 2005. Dual-quorum replication for edge services. In Proceedings of the 6th ACM/IFIP/USENIX International Middleware Conference. Lecture Notes in Computer Science, vol. 3790, Springer, Berlin. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Garcia-Molina, H. 1983. Using semantic knowledge for transaction processing in a distributed database. ACM Trans. Datab. Syst. 8, 2, 186--213. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. GVU WWW User Surveys. 2001. Georgia Institute of Technology. Graphics, Visualization and Usability (GVU) Research Center. http://www.gvu.gatech.edu/user_surveys/.Google ScholarGoogle Scholar
  15. Java EE. 2011. Java Platform Enterprise. http://www.oracle.com/technetwork/java/javaee/.Google ScholarGoogle Scholar
  16. Java EE Web. 2011. Java EE web application technologies. http://www.oracle.com/technetwork/java/javaee/tech/webapps-138511.html.Google ScholarGoogle Scholar
  17. Java Pet Store. 2006. Sample Java EE application. http://java.sun.com/developer/releases/petstore/.Google ScholarGoogle Scholar
  18. JBoss. 2011. JBoss Java application server. http://www.jboss.org.Google ScholarGoogle Scholar
  19. JDBC. 2011. Java database connectivity technology. http://www.oracle.com/technetwork/java/javase/tech/index-jsp-136101.html.Google ScholarGoogle Scholar
  20. Jetty. 2011. HTTP server and servlet container. http://jetty.codehaus.org/jetty/.Google ScholarGoogle Scholar
  21. Kleinrock, L. 1975. Queueing Systems. Wiley, Hoboken, NJ.Google ScholarGoogle Scholar
  22. Llambiri, D., Totok, A., and Karamcheti, V. 2003. Efficiently distributing component-based applications across wide-area environments. In Proceedings of the 23rd International Conference on Distributed Computing Systems (ICDCS’03). IEEE, Los Alamitos, CA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Marinescu, F. 2002. EJB Design Patterns: Advanced Patterns, Processes, and Idioms. Wiley, Hoboken, NJ. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Marrs, T. and Davis, S. 2005. JBoss at Work: A Practical Guide. O’Reilly Media, Sebastopol, CA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Menascé, D., Almeida, V., Fonseca, R., and Mendes, M. 1999. A methodology for workload characterization of e-commerce sites. In Proceedings of the 1st ACM Conference on Electronic Commerce (EC’99). ACM, New York. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Menascé, D., Almeida, V., Riedi, R., Ribeiro, F., Fonseca, R., and Meira, W. 2000. In search of invariants for e-business workloads. In Proceedings of the 2nd ACM Conference on Electronic Commerce (EC’00). ACM, New York. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Moskalyuk, A. 2006. IT Facts: e-commerce research blog on ZDNet.com, Nov. 2006. http://blogs.zdnet.com/ITFacts/?p=12030.Google ScholarGoogle Scholar
  28. MySQL. 2011. MySQL Database. http://www.mysql.com/.Google ScholarGoogle Scholar
  29. Pecaut, D., Silverstein, M., and Stanger, P. 2000. Winning the online consumer: Insights into online consumer behavior, Boston Consulting Group. http://www.bcg.com.Google ScholarGoogle Scholar
  30. Roussas, G. 1997. A Course in Mathematical Statistics. Academic Press, Amsterdam.Google ScholarGoogle Scholar
  31. Selvridge, P., Chaparro, B., and Bender, G. 2001. The world wide wait: Effects of delays on user performance. Int. J. Industrial Ergonomics 29, 1, 15--20.Google ScholarGoogle ScholarCross RefCross Ref
  32. Shi, W., Wright, R., Collins, E., and Karamcheti, V. 2002. Workload characterization of a personalized Web site -- and its implications for dynamic content caching. In Proceedings of the 7th International Workshop on Web Caching and Content Distribution (WCW’02). IWCW, Boulder, CO.Google ScholarGoogle Scholar
  33. Singh, I., Stearns, B., Johnson, M., and The Enterprise Team. 2002. Designing Enterprise Applications with the J2EE Platform. Addison-Wesley, London. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Tedeschi, B. 2005. Glitches in booking first class online. The New York Times (4/10/05), Travel Section, 6.Google ScholarGoogle Scholar
  35. Totok, A. and Karamcheti, V. 2007. Modeling of concurrent web sessions with bounded inconsistency in shared data. J. Parall. Distrib. Comput. 67, 7, 830--847. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Totok, A. and Karamcheti, V. 2010a. Optimizing utilization of resource pools in web application servers. Concurrency Comput: Pract. Exper. 22, 18, 2421--2444. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Totok, A. and Karamcheti, V. 2010b. RDRP: Reward-driven request prioritization for e-commerce web sites. Electron. Commerce Res. Appl. 9, 6, 549--561. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. TPC-W. 2005. Transaction Processing Performance Council. Transactional web e-commerce benchmark. http://www.tpc.org/tpcw/.Google ScholarGoogle Scholar
  39. TPC-W-NYU. 2006. A Java EE implementation of the TPC-W benchmark. http://www.cs.nyu.edu/totok/professional/software/tpcw/tpcw.html.Google ScholarGoogle Scholar
  40. VanBoskirk, S., Li, C., and Parr, J. 2001. Keeping customers loyal. Forrester Research, May. http://www.forrester.com.Google ScholarGoogle Scholar
  41. Wang, M., Chan, N., Papadimitriou, S., Faloutsos, C., and Madhyastha, T. 2002. Data mining meets performance evaluation: Fast algorithms for modeling bursty traffic. In Proceedings of the 18th International Conference on Data Engineering (ICDE’02). IEEE, Los Alamitos, CA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Wong, M. H. and Agrawal, D. 1992. Tolerating bounded inconsistency for increasing concurrency in database systems. In Proceedings of the 11th Symposium on Principles of Database Systems (PODS’92). ACM, New York. Google ScholarGoogle ScholarDigital LibraryDigital Library

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