期刊文献+

Web服务运行时的监控和性能数据动态采集 被引量:3

Runtime monitoring and dynamic performance attributes data acquisition of Web services
下载PDF
导出
摘要 为保证Web服务业务流程及时、准确地获得所调用服务的性能状况,提出了一种轻量级的Web服务运行时的监控和性能数据动态采集框架。该框架采用面向方面的编程,将方面代码植入业务流程执行语言的Web服务消息调用处,以收集服务实例执行状态、时间、事件信息,据此进一步计算出服务性能指标。监控独立于流程本身的运行,实现了服务调用逻辑与监控逻辑的分离。通过实验验证了该方法的可行性,表明监控代价是可以接受的。 To guarantee Web services process obtaining performance status of called service timely and accurately,a lightweight Web services runtime monitoring and dynamic performance attributes data acquisition framework in Business Process Execution Language(BPEL) was presented.This framework adopted an aspect-oriented programming,and aspect-oriented code was implanted in BPFL Web Service message.Execution status,time,event information were collected,then service performance was calculated.Monitoring was operated independently from process,so that service call logic and monitoring logic was separated.Examples were used to suggest that this approach was feasible,and the monitoring cost was affordable.
出处 《计算机集成制造系统》 EI CSCD 北大核心 2011年第8期1654-1659,共6页 Computer Integrated Manufacturing Systems
基金 国家自然科学基金青年科学基金资助项目(60903091) 上海市自然科学基金资助项目(10ZR1408200)~~
关键词 业务流程执行语言 运行时监控 性能 面向方面编程 WEB服务 business process execution language runtime monitoring performance aspect-oriented programming Web services
  • 相关文献

参考文献14

  • 1BEA, IBM, Microsoft, et al. Business process execution lan- guage for Web services version1. 1[EB/OL]. (2007-02-08) [2011-03-05]. http://www. ibm. com/developer works/li- brary/ws-bpel.
  • 2BARESI L, GUINEA S, PISTORE M, et al. Dynamo+as trojan integrated approach for BPEL monitoring [C]//Pro ceedings of IEEE International Conference on Web Services. Washington, D. C. ,USA:IEEE,2009:230-237.
  • 3BARESI L, GUINEA S, NANO O, et al. Comprehensive mo nitoring of BPEL processes [J]. IEEE Internet Computing, 2010,14(3) : 50-57.
  • 4BARBON F, TRAVERSO P, PISTORE M, et al. Run time monitoring of instances and classes of Web service eomposi- tions[C]//Proceedings of IEEE International Conference on Web Services. Washington, D. C. , USA : IEEE, 2006 : 63-71.
  • 5RAN S. A model for Web services discovery with QoS[J]. ACM SIGecom Exchanges,2003,4(1):1-10.
  • 6RAJENDRAN T, BALASUBRAMANIE P. An efficient fra mework for agent-based quality driven Web services discovery [C]//Proceedings of International Conference on Intelligent Agent & Multi Agent Systems. Washington, D. C., USA: IEEE,2009: 1-10.
  • 7CHEN Hong'an, YU Tao, LIN K J. QCWS:an implementa- tion of QoS-capable multimedia Web services[C]//Proceedings of International Symposium on Multimedia Software Engineer- ing. Washington, D.C. ,USA,IEEE,2003:38 45.
  • 8WICKRAMAGE N, WEERAWARANA S. A benchmark for Web service frameworks [C]//Proceedings of IEEE Interna tional Conference on Service Computing. Washington, D. C. , USA: IEEE, 2005:233-240.
  • 9刘譞哲,黄罡,梅宏.用户驱动的服务聚合方法及其支撑框架[J].软件学报,2007,18(8):1883-1895. 被引量:28
  • 10胡建强,邹鹏,王怀民,周斌.Web服务描述语言QWSDL和服务匹配模型研究[J].计算机学报,2005,28(4):505-513. 被引量:108

二级参考文献18

共引文献133

同被引文献35

  • 1凌晓东.SOA综述[J].计算机应用与软件,2007,24(10):122-124. 被引量:113
  • 2Baresi L, Guinea S, Pistore M, et al. Dynamo + astro. An integrated approach for BPEL monitoring [ C ]// IEEE In- ternational Conference on Web Services. 2009:230-237.
  • 3Baresi L, Guinea S, Nano O, et al. Comprehensive moni- toring of BPEL processes [ J]. IEEE Internet Computing, 2010,14(3) :50-57.
  • 4Adinolfi O, Cristaldi R, Coppolino L, et al. QoS- MONaaS:a Portable Architecture for QoS Monito- ring in the Cloud[C]//Sth International Conference on Signal Image Technology and Internet Based Systems. Naples, 2012 : 527-532.
  • 5Zadeh M H,Seyyedi M A. QoS Monitoring for Web Services by Times Series Forecasting [ C] //3rd IEEE International Conference on Computer Science and Information Technology. Chengdu, 2010 : 659 - 663.
  • 6Dashevskiy M,Luo Z. Time Series Prediction with Performance Guarantee[J]. IET Communications, 2011,5 (8) : 1044-1051.
  • 7Martinez-Rego D, Fontenla-Romero O, Alonso-Bet- anzos A, Efficiency of Local Models Ensembles for Time Series Prediction [J]. Expert Systems with Applications, 2011,38(6) .. 6884-6894.
  • 8Huang S C. Online Option Price Forecasting by Using Unscented Kaiman Filters and Support Vec tot MachinesFJ. Expert Systems with Applica- tions,2008,34(4) : 2819-2825.
  • 9Dunis C L,Rosillo R, Fuente D. Forecasting IBEX- 35 Moves Using Support Vector Machines [J]. Neural Computing and Applications, 2013,23 ( 1 ) : 229-236.
  • 10Wang Ling, Wang Xiuting, Fu Jingqi, et al. A Novel Probability Binary Particle Swarm Optimization Algorithm and Its Application[J]. Journal of Soft- ware, 2008,9 (3) : 28-2 5.

引证文献3

二级引证文献8

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部