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QoS collection for web services based on WS-monitor model

Published: 23 October 2013 Publication History

Abstract

QoS information is often used as the basic evidence for Web services optimization. In order to collect QoS information of Web services, we propose a lightweight Server-Monitor framework. This proposal contains a WS-Monitor Model related to metamodel of QoS index information and an extending WSDL algorithm that adds QoS collect strategy into WSDL. Experimental results show that Server-Monitor obtains QoS information effectively and causes tiny negative effect on Web service.

References

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Cited By

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  • (2016)An Intelligent QoS Identification for Untrustworthy Web Services via Two-Phase Neural Networks2016 IEEE International Conference on Web Services (ICWS)10.1109/ICWS.2016.26(139-146)Online publication date: Jun-2016

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      cover image ACM Other conferences
      Internetware '13: Proceedings of the 5th Asia-Pacific Symposium on Internetware
      October 2013
      211 pages
      ISBN:9781450323697
      DOI:10.1145/2532443
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      • NJU: Nanjing University
      • CCF: China Computer Federation
      • Chinese Academy of Sciences

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 23 October 2013

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      Author Tags

      1. QoS collection
      2. WS-monitor model
      3. web service

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      Internetware '13
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      • NJU
      • CCF

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      Internetware '13 Paper Acceptance Rate 15 of 50 submissions, 30%;
      Overall Acceptance Rate 55 of 111 submissions, 50%

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      • (2016)An Intelligent QoS Identification for Untrustworthy Web Services via Two-Phase Neural Networks2016 IEEE International Conference on Web Services (ICWS)10.1109/ICWS.2016.26(139-146)Online publication date: Jun-2016

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