Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
5-2016
Abstract
Recently, many large enterprises (e.g., Netflix, Amazon) have decomposed their monolithic application into services, and composed them to fulfill their business functionalities. Many hosting services on the cloud, with different Quality of Service (QoS) (e.g., availability, cost), can be used to host the services. This is an example of competing services. QoS is crucial for the satisfaction of users. It is important to choose a set of services that maximize the overall QoS, and satisfy all QoS requirements for the service composition. This problem, known as optimal service selection, is NPhard. Therefore, an effective method for reducing the search space and guiding the search process is highly desirable. To this end, we introduce a novel technique, called Probabilistic Hierarchical Refinement (PROHR). PROHR effectively reduces the search space by removing competing services that cannot be part of the selection. PROHR provides two methods, probabilistic ranking and hierarchical refinement, that enable smart exploration of the reduced search space. Unlike existing approaches that perform poorly when QoS requirements become stricter, PROHR maintains high performance and accuracy, independent of the strictness of the QoS requirements. PROHR has been evaluated on a publicly available dataset, and has shown significant improvement over existing approaches
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
Proceedings of the 38th IEEE International Conference on Software Engineering (ICSE), Austin, TX, USA, 2016 May 14-22
First Page
85
Last Page
95
ISBN
9781450339001
Identifier
10.1145/2884781.2884861
Publisher
IEEE
City or Country
USA
Citation
TAN, Tian Huat; CHEN, Manman; SUN, Jun; LIU, Yang; ANDRÉ, Étienne; XUE, Yinxing; and DONG, Jin Song.
Optimizing selection of competing services with probabilistic hierarchical refinement. (2016). Proceedings of the 38th IEEE International Conference on Software Engineering (ICSE), Austin, TX, USA, 2016 May 14-22. 85-95.
Available at: https://ink.library.smu.edu.sg/sis_research/4945
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/2884781.2884861