2018 IEEE International Conference on Web Services (ICWS) (2018)
San Francisco, CA, USA
Jul 2, 2018 to Jul 7, 2018
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.
cloud computing, parallel processing, probability, quality of service, resource allocation, service-oriented architecture, software metrics
X. Li, S. Liu, L. Pan, Y. Shi and X. Meng, "Performance Analysis of Service Clouds Serving Composite Service Application Jobs," 2018 IEEE International Conference on Web Services (ICWS), San Francisco, CA, USA, 2018, pp. 227-234.