An aspectual feature module based adaptive design pattern for autonomic computing systems

4Citations
Citations of this article
7Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Adaptability in software is the main fascinating concern for which today's software architects are really interested in providing the autonomic computing. Different programming paradigms have been introduced for enhancing the dynamic behavior of the programs. Few among them are the Aspect oriented programming (AOP) and Feature oriented programming (FOP) with both of them having the ability to modularize the crosscutting concerns, where the former is dependent on aspects, advice and lateral one on the collaboration design and refinements. In this paper we will propose a design pattern for Autonomic Computing System which is designed with Aspect-oriented design patterns we'll also study about the amalgamation of the Feature-oriented and Aspect-oriented software development methodology and its usage in developing a self-reconfigurable adaptive system. In this paper we used the design patterns which will satisfy the properties of an autonomic system: For monitoring we used the Observer design pattern, Decision making we used Adaptation Detector design pattern, and for Reconfiguration we used Feature-oriented Aspect insertion using participant pattern. The main objective of the system is to provide self-reconfiguring behavior at run-time by inserting into the current existing code with an aspectual feature module code without interrupting the user and to provide transparency while accessing the system. The pattern is described using a java-like notation for the classes and interfaces. A simple UML class and Sequence diagrams are depicted. © 2012 Springer-Verlag.

Cite

CITATION STYLE

APA

Mannava, V., & Ramesh, T. (2012). An aspectual feature module based adaptive design pattern for autonomic computing systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7198 LNAI, pp. 130–140). https://doi.org/10.1007/978-3-642-28493-9_15

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free