Talk on data analysis and optimization
- Dr.Tian Huat TAN
Singapore University of Technology and Design
- March 17 (Thursday) 11:20-
room 1509, National Institute of Informatics
Optimizing Selection of Competing Services with Probabilistic Hierarchical Refinement
- 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. 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 NP-hard. Therefore, an effective method for reducing the search space and guiding the search process is highly desirable.
In this talk I would like to introduce some recent progresses of optimal service selection and present a novel technique, called Probabilistic Hierarchical Refinement (PROHR), which effectively reduces the search space by removing competing services that cannot be part of the selection. PROHR contains two algorithms, probabilistic ranking and hierarchical refinement, that enable smart exploration of the reduced search space. This work has been accepted by ICSE 2016. More importantly, PROHR provides a novel and general way that leverages mixed integer programming for various NP-hard problems. This could be easily extended to other research domains, e.g., automated malware generation.
- Short BIO of the speaker:
- Dr. Tian Huat is a research fellow in Singapore of University of Technology and Design. He received his Ph.D. in Computer Science from the National University of Singapore. His research is focused on software engineering, cyber security, and service analysis. He has published in various top-tier conferences, such as ICSE, WWW, ISSTA, and FM.
yiyu[at]nii.ac.jp *Please replace [at] with @.