Login (DCU Staff Only)
Login (DCU Staff Only)

DORAS | DCU Research Repository

Explore open access research and scholarly works from DCU

Advanced Search

Personalized quality prediction for dynamic service management based on invocation patterns

Zhang, Li, Zhang, Bin, Pahl, Claus orcid logoORCID: 0000-0002-9049-212X, Xu, Lei and Zhu, Zhiliang (2013) Personalized quality prediction for dynamic service management based on invocation patterns. In: Eleventh International Conference on Service Oriented Computing ICSOC 2013, 2-5 Dec 2013, Berlin, Germany.

Recent service management needs, e.g., in the cloud, require ser-vices to be managed dynamically. Services might need to be selected or re-placed at runtime. For services with similar functionality, one approach is to identify the most suitable services for a user based on an evaluation of the quality (QoS) of these services. In environments like the cloud, further person-alisation is also paramount. We propose a personalized QoS prediction method, which considers the impact of the network, server environment and user input. It analyses previous user behaviour and extracts invocation patterns from moni-tored QoS data through pattern mining to predict QoS based on invocation QoS patterns and user invocation features. Experimental results show that the pro-posed method can significantly improve the accuracy of the QoS prediction.
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Uncontrolled Keywords:Service Quality; Web and Cloud Services; QoS Prediction; Invoca-tion Pattern Mining; Collaborative Filtering; Personalized Recommendation
Subjects:Computer Science > Software engineering
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:19229
Deposited On:04 Dec 2013 15:03 by Claus Pahl . Last Modified 21 Jan 2021 17:07

Full text available as:

[thumbnail of ICSOC13-LiZhang.pdf]
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader


Downloads per month over past year

Archive Staff Only: edit this record