The Open UniversitySkip to content
 

Requirements-driven self-optimization of composite services using feedback control

Chen, Bihuan; Peng, Xin; Yu, Yijun and Zhao, Wenyun (2014). Requirements-driven self-optimization of composite services using feedback control. IEEE Transactions on Services Computing, 8(1) pp. 107–120.

Full text available as:
[img]
Preview
PDF (Proof) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (686kB) | Preview
URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?...
DOI (Digital Object Identifier) Link: https://doi.org/10.1109/TSC.2014.2298866
Google Scholar: Look up in Google Scholar

Abstract

In an uncertain and changing environment, a composite service needs to continuously optimize its business process and service selection through runtime adaptation. To achieve the overall satisfaction of stakeholder requirements, quality tradeoffs are needed to adapt the composite service in response to the changing environments. Existing approaches on service selection and composition, however, are mostly based on quality preferences and business processes decisions made statically at the design time.

In this paper, we propose a requirements-driven self-optimization approach for composite services. It measures the quality of services (QoS), estimates the earned business value, and tunes the preference ranks through a feedback loop. The detection of unexpected earned business value triggers the proposed self-optimization process systematically. At the process level, a preference-based reasoner configures a requirements goal model according to the tuned preference ranks of QoS requirements, reconfiguring the business process according to its mappings from the goal configurations. At the service level, selection decisions are optimized by utilizing the tuned weights of QoS criteria.

We used an experimental study to evaluate the proposed approach. Results indicate that the new approach outperforms both fixed-weighted and floating-weighted service selection approaches with respect to earned business value and adaptation flexibility.

Item Type: Journal Item
Copyright Holders: 2013 IEEE
ISSN: 1939-1374
Project Funding Details:
Funded Project NameProject IDFunding Body
Adaptive Security and Privacy - Advanced Grant291652ERC
Keywords: quality of services (QoS); quality tradeoffs; self-optimization; business value; process reconfiguration; service selection
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Item ID: 39225
Depositing User: Yijun Yu
Date Deposited: 18 Dec 2014 11:19
Last Modified: 11 Sep 2018 18:32
URI: http://oro.open.ac.uk/id/eprint/39225
Share this page:

Metrics

Altmetrics from Altmetric

Citations from Dimensions

Download history for this item

These details should be considered as only a guide to the number of downloads performed manually. Algorithmic methods have been applied in an attempt to remove automated downloads from the displayed statistics but no guarantee can be made as to the accuracy of the figures.

Actions (login may be required)

Policies | Disclaimer

© The Open University   contact the OU