摘要(英) |
As enterprises world-wide race to embrace real 吃time management to improve
productivity, customer services, and flexibility. Many resources have been invested in enterprise systems (ESs). All modern ESs adopt a n-tier client-server architecture that includes several application servers to host users and applications. As in any other multi-server environment, the load distributions and user
distributions in particular, become a critical issue in tuning system performance.
In ESs, each application is evoked by a user who logs on an application server
and stays connected to the server for an entire working session, which can last
for days. Therefore, admitting a user into an application server affects not only
current but also future performance of the server.
Distributions in application servers and web servers are different in granularity. In the former scenario, a user represented by a set of transactions is the atomic element while in the latter scenario, single request is the atomic element and different requests issued by the same user can be directed to different web
servers. To the best of our knowledge, no research has been devoted in the user
distribution to application servers in n-tier architecture.
The paper proposes two methods to distribute users evoking similar transactions to the same servers. One is threshold of application reusibility and the other is limited buÞer sizes in each servers. Based on user profiles, the algorithms return suggestions of user distributions, the number of servers needed,
and the similarity of user requests in each server. The paper also discusses how
to apply the knowledge of existing user patterns to distribute new users, who do not have enough entries in the proßle and have no distribution suggestion,in the run time. The algorithms are also applied on a set of real data which are
derived from the access log of an enterprise ERP system to evaluate the quality of the suggested distributions. |
參考文獻 |
[1] SAP AG. System R/3 Technicale Consultant Training 1 - adminis-
tration, chapter R/3 WorkLoad Distribution. SAP AG, 1998.
[2] SAP AG. System R/3 Technicale Consultant Training 3 - Perf. Tun-
ing, chapter R/3 Memory Management. SAP AG, 1998.
[3] Woo Hyun Ahn, Woo Jin Kim, and Daeyson Park. Content-aware coop-
erative caching for cluster-based. The Journal of system and software,
69(1):75-86, 2004.
[4] R. Argawal and R. Srikant. Fast algorithms for mining associations rules.
In Proceedings of International Conference in Very Large Data Bases,
pages 487Û499, 1994.
[5] H. Bryhni, E. Klovning, and O. Kure. A comparison of load balancing
techniques for scalable web servers. IEEE Network, 14:58-64, 2000.
[6] V. Cardellini, M. Colajanni, and P.S. Yu. Dynamic load balancing on web-
server systems. IEEE Internet Computing, 3:28-39, 1999.
[7] Yen-Liang Chen, Ping-Yu Hsu, and Chun-Ching Ling. Mining quantitative
assocation rules in bag databases. Journal of Information Management,
7:215-229, 2001.
[8] Gianfranco Ciardo, Alma Riska, and Evgenia Smirni. Equiload:a load bal-
ancing policy for cluster web servers. Performance Evaluation, 46:101-
124, 2001.
[9] B.A. Davey and H.A. Priestley. Introduction to Lattice and Order. Cam-
bridge Mathematical Textbooks, 1990.
[10] P. Dreyfus. The second wave: netscape usability on the services based
internet. IEEE Internet Computing, 2(2):36-40, 1998.
[11] R. O. Duda and P. E. Hard. Pattern Classißcation and Scene Analysis.
Wiley-Interscience Publication, 1973.
[12] S. Guha, R. Rastogi, and K. Shim. Rock: A robust clustering algorithm
for categorical attributes. Information Systems, 25(5):345Û366, 2000.
[13] J. Han and M. Kamber. Data Mining: Concepts and Techniques, chap-
ter Mining association rules in large databases. Morgan Kaufmann Pub-
lisher, 2001.
[14] J. Han and M. Kamber. Data Mining: Concepts and Techniques, chap-
ter Clustersing. Morgan Kaufmann Publisher, 2001.
[15] J.A. Hernæandes. The SAP R/3 Handbook, chapter Distributing R/3 Sys-
tems. McGraw-Hill, 2 edition, 2000.
[16] J. Pei J. Han and Y. Yin. Mining frequent patterns without candidate gen-
eration. In Proceedings of ACM-SIGMOD International Conference
on Management of Data, pages 1-12, 2000.
[17] A.K. Jain and R.C. Dubes. Algorithms for Clustering Data. Prentice
Hall, 1988.
[18] P. Mohapatra and H. Chen. A framework for managing qos and improving
performance of dynamic web content. In Proceedings of Global Telecom-
munications Conference, volume 4, pages 2460-2464, 2001.
[19] S. Nadimpalli and S. Majumdar. Techniques for achieving high performance
web servers. In Proceedings of International Conference on Parallel
Processing, pages 233-241, 2000.
[20] B. C-P. Ng and C-L. Wang. Document distribution algorithm for load
balancing on an extensible web server architecture. In Proceedings of
International symposium on cluster computing and the Grid, pages
140-147, 2001.
[21] Victor Safronov and Manish Parashar. Optimizing web servers using page
rank prefetching for clustered accesses. Information Sciences, 150:165-
176, 2003.
[22] Zhiguang Shan, Chuang Lin, and Dan Marineslu. Modeling and perfor-
mance analysis of qos-aware load balancing of web-server cluster. Com-
puter Networks, 40(2):235-244, 2002.
[23] Sun Micorsystems Inc. Software Development for the web enabled en-
terprise: beneßt of the solaris operating environment, 1999.
[24] J. Zhang, T. Hamalainen, J. Joutsensalo, and K. Kaario. Qos-aware load
balancing algorithm for globally distributed web systems. In Proceedings
of international conferences on Info-tech and Info-net, volume 2, pages
60-65, 2001. |