Derivation of Markov Models for Effectiveness Analysis of Adaptable Software Architectures for Mobile Computing.

Saved in:
Bibliographic Details
Title: Derivation of Markov Models for Effectiveness Analysis of Adaptable Software Architectures for Mobile Computing.
Authors: Grassi, Vincenzo, Mirandola, Raffaela
Source: IEEE Transactions on Mobile Computing. Apr-Jun2003, Vol. 2 Issue 2, p114. 18p. 2 Black and White Photographs, 21 Diagrams, 2 Charts.
Subjects: Mobile computing software, Markov processes
Abstract: Adaptable Software Architectures (SAs) have been suggested as a viable solution for the design of distributed applications that operate in a mobile computing environment to cope with the high heterogeneity and variability of this environment. Mobile code techniques can be used to implement this kind of SAs since they allow us to dynamically modify the Icad of the hosting nodes and the internode traffic to adapt to the resources available in the nodes and to the condition of the (often wireless) network link. However, moving code among nodes has a cost (e.g., in terms of network traffic and consumed energy for mobile nodes), so designing an adaptable SA based on mobile code techniques requires a careful analysis to determine its effectiveness since early design stages. In this respect, our main contribution consists of a methodology, called ASAP (Adaptable Software Architectures Performance), to automatically derive, starting from a design model of a mobility-based SA, a Markov model whose solution provides insights about the most effective adaptation strategy based on code mobility in a given execution environment. We assume that the SA model is expressed using the Unified Modeling Language (UML) because of its widespread use in software design, also suggesting some extension to this formalism to better express the "mobility structure" of the application, i.e., which are the mobile components, and the possible targets of their movement. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Mobile Computing is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Be the first to leave a comment!
You must be logged in first