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.
- 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 Scholar
Digital Library
- Akula, V. and Menascé, D. 2007. Two-level workload characterization of online auctions. Electron. Commerce Res. Appl. 6, 2, 192--208. Google Scholar
Digital Library
- 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 Scholar
- Cecchet, E., Marguerite, J., and Zwaenepoel, W. 2002. Performance and scalability of EJB applications. ACM SIGPLAN Not. 37, 11, ACM, New York. Google Scholar
Digital Library
- 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 Scholar
Digital Library
- 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 Scholar
Digital Library
- 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 Scholar
Digital Library
- EJB. 2011. Enterprise JavaBeans Technology. http://www.oracle.com/technetwork/java/index-jsp-140203.html.Google Scholar
- 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 Scholar
Digital Library
- 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 Scholar
Digital Library
- 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 Scholar
Digital Library
- 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 Scholar
Digital Library
- Garcia-Molina, H. 1983. Using semantic knowledge for transaction processing in a distributed database. ACM Trans. Datab. Syst. 8, 2, 186--213. Google Scholar
Digital Library
- GVU WWW User Surveys. 2001. Georgia Institute of Technology. Graphics, Visualization and Usability (GVU) Research Center. http://www.gvu.gatech.edu/user_surveys/.Google Scholar
- Java EE. 2011. Java Platform Enterprise. http://www.oracle.com/technetwork/java/javaee/.Google Scholar
- Java EE Web. 2011. Java EE web application technologies. http://www.oracle.com/technetwork/java/javaee/tech/webapps-138511.html.Google Scholar
- Java Pet Store. 2006. Sample Java EE application. http://java.sun.com/developer/releases/petstore/.Google Scholar
- JBoss. 2011. JBoss Java application server. http://www.jboss.org.Google Scholar
- JDBC. 2011. Java database connectivity technology. http://www.oracle.com/technetwork/java/javase/tech/index-jsp-136101.html.Google Scholar
- Jetty. 2011. HTTP server and servlet container. http://jetty.codehaus.org/jetty/.Google Scholar
- Kleinrock, L. 1975. Queueing Systems. Wiley, Hoboken, NJ.Google Scholar
- 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 Scholar
Digital Library
- Marinescu, F. 2002. EJB Design Patterns: Advanced Patterns, Processes, and Idioms. Wiley, Hoboken, NJ. Google Scholar
Digital Library
- Marrs, T. and Davis, S. 2005. JBoss at Work: A Practical Guide. O’Reilly Media, Sebastopol, CA. Google Scholar
Digital Library
- 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 Scholar
Digital Library
- 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 Scholar
Digital Library
- Moskalyuk, A. 2006. IT Facts: e-commerce research blog on ZDNet.com, Nov. 2006. http://blogs.zdnet.com/ITFacts/?p=12030.Google Scholar
- MySQL. 2011. MySQL Database. http://www.mysql.com/.Google Scholar
- 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 Scholar
- Roussas, G. 1997. A Course in Mathematical Statistics. Academic Press, Amsterdam.Google Scholar
- 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 Scholar
Cross Ref
- 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 Scholar
- Singh, I., Stearns, B., Johnson, M., and The Enterprise Team. 2002. Designing Enterprise Applications with the J2EE Platform. Addison-Wesley, London. Google Scholar
Digital Library
- Tedeschi, B. 2005. Glitches in booking first class online. The New York Times (4/10/05), Travel Section, 6.Google Scholar
- 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 Scholar
Digital Library
- Totok, A. and Karamcheti, V. 2010a. Optimizing utilization of resource pools in web application servers. Concurrency Comput: Pract. Exper. 22, 18, 2421--2444. Google Scholar
Digital Library
- 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 Scholar
Digital Library
- TPC-W. 2005. Transaction Processing Performance Council. Transactional web e-commerce benchmark. http://www.tpc.org/tpcw/.Google Scholar
- 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 Scholar
- VanBoskirk, S., Li, C., and Parr, J. 2001. Keeping customers loyal. Forrester Research, May. http://www.forrester.com.Google Scholar
- 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 Scholar
Digital Library
- 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 Scholar
Digital Library
Index Terms
Exploiting Service Usage Information for Optimizing Server Resource Management
Recommendations
Towards optimizing the non-functional service matchmaking time
WWW '12 Companion: Proceedings of the 21st International Conference on World Wide WebThe Internet is moving fast to a new era where million of services and things will be available. In this way, as there will be many functionally-equivalent services for a specific user task, the service non-functional aspect should be considered for ...
QoS-aware selection of web service composition based on harmony search algorithm
ICACT'10: Proceedings of the 12th international conference on Advanced communication technologyDesigning of the composite services with desired quality is an interesting challenge of the web service environments. In a QoS-aware web service composition, appropriate services with acceptable quality are selected among several function-equivalent ...






Comments