Publications

Automated Composition and Optimization of Services for Variability-Intensive Domains

Mahdi Bashari and Ebrahim Bagheri and Weichang Du
Reference:
Links to Publication: [www][pdf]
Abstract:
The growth in the number of publicly available services on the Web has encouraged developers to rely more heavily on such services to deliver products in a faster, cheaper and more reliable fashion. Many developers are now using a collection of these services in tandem to build their applications. While there has been much attention to the area of service composition, there are few works that examine the possibility of automatically generating service compositions for variability-intensive application domains. High variability in a domain is often captured through an organized feature space, which has the potential for developing many different application instantiations. The focus of our work is to develop an end-to-end technique that would enable the automatic generation of composite services based on a specific configuration of the feature space that would be directly executable and presented in WS-BPEL format. To this end, we adopt concepts from software product line engineering and AI planning to deliver the automated composition of online services. We will further benefit from such notions as safeness and threat from AI planning to optimize the generated service compositions by introducing parallelism where possible. Furthermore, we show how the specification of the generated service composition can be translated into executable WS-BPEL code. More specifically, the core contributions of our work are: (1) we show how AI planning techniques can be used to generate a workflow based on a feature model configuration; (2) we propose a method for optimizing a workflow generated based on AI planning techniques; and (3) we demonstrate that the optimized workflow can be directly translated into WS-BPEL code. We evaluate our work from two perspectives: i) we will first formally prove that the methods that we have proposed are sound and complete from a theoretical perspective, and ii) we will show through experimentation that our proposed work is usable from a practical point of view.
Bibtex Entry:
@article{jss2018-2, author = {Mahdi Bashari and Ebrahim Bagheri and Weichang Du}, title = {Automated Composition and Optimization of Services for Variability-Intensive Domains}, journal = {​Journal of Systems and Software}, year = {2018}, webpdf = {http://ls3.rnet.ryerson.ca/wiki/images/f/fa/JSS-2.pdf}, url = {https://www.journals.elsevier.com/journal-of-systems-and-software}, abstract = {The growth in the number of publicly available services on the Web has encouraged developers to rely more heavily on such services to deliver products in a faster, cheaper and more reliable fashion. Many developers are now using a collection of these services in tandem to build their applications. While there has been much attention to the area of service composition, there are few works that examine the possibility of automatically generating service compositions for variability-intensive application domains. High variability in a domain is often captured through an organized feature space, which has the potential for developing many different application instantiations. The focus of our work is to develop an end-to-end technique that would enable the automatic generation of composite services based on a specific configuration of the feature space that would be directly executable and presented in WS-BPEL format. To this end, we adopt concepts from software product line engineering and AI planning to deliver the automated composition of online services. We will further benefit from such notions as safeness and threat from AI planning to optimize the generated service compositions by introducing parallelism where possible. Furthermore, we show how the specification of the generated service composition can be translated into executable WS-BPEL code. More specifically, the core contributions of our work are: (1) we show how AI planning techniques can be used to generate a workflow based on a feature model configuration; (2) we propose a method for optimizing a workflow generated based on AI planning techniques; and (3) we demonstrate that the optimized workflow can be directly translated into WS-BPEL code. We evaluate our work from two perspectives: i) we will first formally prove that the methods that we have proposed are sound and complete from a theoretical perspective, and ii) we will show through experimentation that our proposed work is usable from a practical point of view.}, year = {2018} }




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