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Admission Control for WebServices in Enterprise Systems Using Expert Systems

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6987))

Abstract

Presently, in order to make connections among applications in heterogeneous environments, webservices are utilized. QoS is one of the nonfunctional topics of webservices being the subject matter of a lot of research works. Considering the multiplicity of systems which are connected together, and on the other hand, notifying the shared resources, a system may face congestion problem if there is no admission control applied. In this article to solve the problem, we will propose a mechanism covering admission control for webservices in enterprise systems. In this mechanism, for each webservice, the rate of resource consumption and the time taken for its execution are evaluated. Then, by receiving requests, functionality of webservice is investigated and stored as learning data. In case the webservice cannot respond in the allocated time, the system will sense the probability of congestion. This mechanism uses Naïve Bayesian method to predict next status of system. Admission control mechanism, considering the present status of the system and learning data, will distinguish what kind of response should be given to the requester of the service in order to protect the system from congestion. Consequently, by using this method, we can tackle with congestion problem when we use webservices in enterprise systems.

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References

  1. Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)

    MATH  Google Scholar 

  2. Menasce, D.: QoS issues in Web services. IEEE Internet Computing 6(6), 72–75 (2002)

    Article  Google Scholar 

  3. Chitra, S., Vidhya, A., Aghila, G.: Web service selection based on ranking of QoS using Naïve Bayes through ontology mapping. In: 2010 International Conference on Computer and Communication Technology (ICCCT), pp. 782–787 (2010)

    Google Scholar 

  4. Xiaodong, F., Ping, Z., Zhenhong, S., Ying, J.: Web service selection with uncertain QoS information. In: Control Conference, pp. 271–275 (2008)

    Google Scholar 

  5. Sha, L., Shaozhong, G., Xin, C., Mingjing, L.: A QoS Based Web Service Selection Model. In: International Forum on Information Technology and Applications, IFITA 2009, vol. 3, pp. 353–356 (2009)

    Google Scholar 

  6. Liu, B., Shi, Y., Wang, H.: QoS oriented web service composition and optimization in SOA. In: 2009 Joint Conferences on Pervasive Computing (JCPC), pp. 605–610 (2009)

    Google Scholar 

  7. Dong, W., Jiao, L.: QoS-Aware Web Service Composition Based on SLA. In: Fourth International Conference on Natural Computation, ICNC 2008, vol. 5, pp. 247–251 (2008)

    Google Scholar 

  8. Yu, T., Lin, K.: A broker-based framework for QoS-aware Web service composition. In: IEEE International Conference on e-Technology and e-Commerce and e-Service, pp. 22–29 (2005)

    Google Scholar 

  9. Bashar, A., Parr, G., McClean, S., Scotney, B., Nauck, D.: Machine learning based Call Admission Control approaches: A comparative study. In: 2010 International Conference on Network and Service Management (CNSM), pp. 431–434 (2010)

    Google Scholar 

  10. Nguyen, T., Armitage, G.: A survey of techniques for internet traffic classification using machine learning. IEEE Communications Surveys & Tutorials 10(4), 56–76, Fourth Quarter (2008)

    Article  Google Scholar 

  11. W3C Working Group, QoS for Web Services: Requirements and Possible Approaches, Note 25 (2003), http://www.w3c.or.kr/kr-office/TR/2003/ws-qos/

  12. Wikipedia, Naive Bayes classifier, http://en.wikipedia.org/wiki/Naive_Bayes_classifier

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© 2011 Springer-Verlag Berlin Heidelberg

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Salimi, A.J., Isazadeh, A., Karimpour, J. (2011). Admission Control for WebServices in Enterprise Systems Using Expert Systems. In: Gong, Z., Luo, X., Chen, J., Lei, J., Wang, F.L. (eds) Web Information Systems and Mining. WISM 2011. Lecture Notes in Computer Science, vol 6987. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23971-7_26

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  • DOI: https://doi.org/10.1007/978-3-642-23971-7_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23970-0

  • Online ISBN: 978-3-642-23971-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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