Journal
2018 IEEE INTERNATIONAL CONFERENCE ON WEB SERVICES (IEEE ICWS 2018)
Volume -, Issue -, Pages 227-234Publisher
IEEE
DOI: 10.1109/ICWS.2018.00036
Keywords
performance analysis; service cloud; composite service application; parallelizable service; queuing theory
Funding
- National Key Research and Development Program of China [2017YFA0700601, 2016YFB0201405]
- Key Research and Development Program of Shandong Province [2018GGX101019, 2017CXGC0605, 2017CXGC0604, 2016GGX106001, 2016ZDJS01A09]
- Natural Science Foundation of Shandong Province for Major Basic Research Projects [ZR2017ZB0419]
- Young Scholars Program of Shandong University
- TaiShan Industrial Experts Program of Shandong Province [tscy20150305]
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Performance analysis is important for service clouds serving composite service application jobs containing parallelizable tasks, for optimizing the degree of parallelism (DOP) and resource allocation schemes could improve performance obviously. In this paper, we describe a novel tandem queuing network with a parallel multi-station multi-server system as an analytical model for service clouds serving composite service application jobs. We design a partition method (termed the 'pleasing partition') to help us propose an analytical model for parallelizable service which is the vital fraction of composite service. After that, we could obtain a complete probability distribution of response time, waiting time and other important performance metrics calculated by our proposed analytical model. Thus, to use this model, cloud operators could determine proper job configurations and resource allocation schemes, for achieving specific QoS (Quality of Service). Extensive simulations are conducted to validate that our analytical model has high accuracy in predicting performance metrics of composite service application jobs.
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