Reusable and Customizable SaaS Software Development Approach Based on Variation Model

被引：0
|
作者：
Sun C.-A. [1 ]
Zhang Z.-X. [1 ]
Zhang X. [1 ]
机构：
[1] School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing
来源：
Ruan Jian Xue Bao/Journal of Software | 2018年 / 29卷 / 11期
基金：
中国国家自然科学基金;
关键词：
SaaS; Service composition; Variability management; VxBPEL; Web services;
D O I：
10.13328/j.cnki.jos.005294
中图分类号：
学科分类号：
摘要：
In the context of cloud computing, software is delivered as a service to the customer through the Internet, and such a software delivery mode is called SaaS (software as a service). Unlike the traditional software delivery mode, SaaS software is usually running on the server side, which provides services to multiple tenants at the same time. As a result, SaaS software should be designed to meet the individual needs of different tenants, they should be flexible enough to cater for the rapidly changing tenant's requirements, and the response to a talent's change should not affect other tenants. Using the adaptive service composition method based on variability management and its supporting platform, a reusable and customizable SaaS software development method for the context of cloud computing is proposed, and a supporting platform is developed to facilitate the adoption of the proposed method. The platform includes a SaaS mode supporting service composition engine and a remote customization tool. The proposed method first creates an abstract service composition model to meet the common requirements of different tenants, and then the supporting platform is used to interpret the model and derive multiple different process instances at runtime, which are concurrently executed and isolated. A case study is conducted to validate the feasibility of the proposed method and evaluate the performance of the supporting platform using a domain specific SaaS software. Experimental results show that the proposed method and platform present a viable alternative for multi-tenant and multi-instance delivery mode for SaaS software. © Copyright 2018, Institute of Software, the Chinese Academy of Sciences. All rights reserved.
引用
收藏
页码：3435 / 3454
页数：19
相关论文
共 29 条
  • [1] Ge X.H., Lu H.H., Ding A.X., Study on process engine service model based on SaaS, Telecommunications Information: Networking and Communications, 265, 12, pp. 27-30, (2010)
  • [2] Kapuruge M., Colman A., Han J., Achieving multi-tenanted business processes in SaaS applications, Proc. of the 12th Int'l Conf. on Web Information System Engineering (WISE 2011), pp. 143-157, (2011)
  • [3] Grivas S.G., Kumar T.U., Wache H., Cloud broker: Bringing intelligence into the cloud, Proc. of the IEEE 3rd Int'l Conf. on Cloud Computing (CLOUD 2010), pp. 544-545, (2010)
  • [4] Sun C.A., Rossing R., Sinnema M., Bulanov P., Aiello M., Modeling and managing the variability of Web service-based systems, Journal of Systems and Software, 83, 3, pp. 502-516, (2010)
  • [5] Sun C.A., Aiello M., Towards variable service compositions using VxBPEL, Proc. of the 10th Int'l Conf. on Software Reuse: High Confidence Software Reuse in Large Systems (ICSR 2008), pp. 257-261, (2008)
  • [6] Sun C.A., Xue T.H., Aiello M., ValySeC: A variability analysis tool for service compositions using VxBPEL, Proc. of the 5th IEEE Asia-Pacific Services Computing Conf. (APSCC 2010), pp. 307-314, (2010)
  • [7] Sun C.A., Wang P., Zhang X., Aiello M., VxBPEL_ODE: A variability enhanced service composition engine, Proc. of the Asia-Pacific Web Conf. 2014 Workshops on Web Technologies and Applications (APWeb 2014), pp. 69-81, (2014)
  • [8] Sun C.A., Xue T.H., Hu C.J., VxBPELEngine: A change-driven adaptive service composition engine, Chinese Journal of Computers, 36, 12, pp. 2441-2454, (2013)
  • [9] Koning M., Sun C.A., Sinnema M., Avgeriou P., VxBPEL: Supporting variability for Web services in BPEL, Information & Software Technology, 51, 2, pp. 258-269, (2009)
  • [10] Zhang X., Research on design technique for reusable and customizable business processes in the cloud computing context and its supporting tool, pp. 1-78, (2016)
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