ActivFORMS: A Formally-Founded Model-Based Approach to Engineer Self-Adaptive Systems
release_2erkrwordnaljgaxrrg3trlbxa
by
Danny Weyns, M. Usman Iftikhar
2022
Abstract
Self-adaptation equips a computing system with a feedback loop that enables
it dealing with change caused by uncertainties during operation, such as
changing availability of resources and fluctuating workloads. To ensure that
the system complies with the adaptation goals, recent research suggests the use
of formal techniques at runtime. Yet, existing approaches have three
limitations that affect their practical applicability: (i) they ignore
correctness of the behavior of the feedback loop, (ii) they rely on exhaustive
verification at runtime to select adaptation options to realize the adaptation
goals, which is time and resource demanding, and (iii) they provide limited or
no support for changing adaptation goals at runtime. To tackle these
shortcomings, we present ActivFORMS (Active FORmal Models for Self-adaptation).
ActivFORMS contributes an end-to-end approach for engineering self-adaptive
systems, spanning four main stages of the life cycle of a feedback loop:
design, deployment, runtime adaptation, and evolution. We also present
ActivFORMS-ta, a tool-supported instance of ActivFORMS that leverages timed
automata models and statistical model checking at runtime. We validate the
research results using an IoT application for building security monitoring that
is deployed in Leuven. The experimental results demonstrate that ActivFORMS
supports correctness of the behavior of the feedback loop, achieves the
adaptation goals in an efficient way, and supports changing adaptation goals at
runtime.
In text/plain
format
Archived Files and Locations
application/pdf 3.4 MB
file_k3qr4zusoza7bgviczdutct7w4
|
arxiv.org (repository) web.archive.org (webarchive) |
1908.11179v3
access all versions, variants, and formats of this works (eg, pre-prints)