Dynamic adaptation of service compositions with variability models

•We propose a framework based on variability models to guide the dynamic adaptation of service compositions.•The proposed framework spans over design time and runtime.•An evaluation demonstrates several benefits of our approach, both at design time and at runtime. Web services run in complex context...

Full description

Saved in:
Bibliographic Details
Published in:The Journal of systems and software 2014-05, Vol.91, p.24-47
Main Authors: Alférez, G.H., Pelechano, V., Mazo, R., Salinesi, C., Diaz, D.
Format: Article
Language:English
Subjects:
Adaptation
Artificial Intelligence
Autonomic computing
Business Process Execution Language
Computer Science
Constraint programming
Decision making models
Design engineering
Dynamic adaptation
Dynamic software product line
Dynamical systems
Dynamics
Mathematical models
Mathematical problems
Models at runtime
Programming languages
Run time (computers)
Semantics
Software Engineering
Studies
Variability
Verification
Web service composition
Web services
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•We propose a framework based on variability models to guide the dynamic adaptation of service compositions.•The proposed framework spans over design time and runtime.•An evaluation demonstrates several benefits of our approach, both at design time and at runtime. Web services run in complex contexts where arising events may compromise the quality of the whole system. Thus, it is desirable to count on autonomic mechanisms to guide the self-adaptation of service compositions according to changes in the computing infrastructure. One way to achieve this goal is by implementing variability constructs at the language level. However, this approach may become tedious, difficult to manage, and error-prone. In this paper, we propose a solution based on a semantically rich variability model to support the dynamic adaptation of service compositions. When a problematic event arises in the context, this model is leveraged for decision-making. The activation and deactivation of features in the variability model result in changes in a composition model that abstracts the underlying service composition. These changes are reflected into the service composition by adding or removing fragments of Business Process Execution Language (WS-BPEL) code, which can be deployed at runtime. In order to reach optimum adaptations, the variability model and its possible configurations are verified at design time using Constraint Programming. An evaluation demonstrates several benefits of our approach, both at design time and at runtime.
ISSN:0164-1212
1873-1228
DOI:10.1016/j.jss.2013.06.034