Model-Driven Engineering of Adaptation Engines for Self-Adaptive

74 Pages·2013·1.67 MB·English

Model-Driven
Engineering of
Adaptation Engines for
Self-Adaptive Software:
Executable Runtime
Megamodels
Thomas Vogel, Holger Giese
Technische Berichte Nr. 66
des Hasso-Plattner-Instituts für
Softwaresystemtechnik
an der Universität Potsdam

Technische Berichte des Hasso-Plattner-Instituts für
Softwaresystemtechnik an der Universität Potsdam

Technische Berichte des Hasso-Plattner-Instituts für
Softwaresystemtechnik an der Universität Potsdam | 66

Thomas Vogel | Holger Giese

Model-Driven Engineering of Adaptation Engines
for Self-Adaptive Software

Executable Runtime Megamodels

Universitätsverlag Potsdam
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Die Schriftenreihe Technische Berichte des Hasso-Plattner-Instituts für
Softwaresystemtechnik an der Universität Potsdam wird herausgegeben
von den Professoren des Hasso-Plattner-Instituts für Softwaresystemtechnik
an der Universität Potsdam.

ISSN (print) 1613-5652
ISSN (online) 2191-1665

Das Manuskript ist urheberrechtlich geschützt.

Online veröffentlicht auf dem Publikationsserver der Universität Potsdam
URL http://pub.ub.uni-potsdam.de/volltexte/2013/6382/
URN urn:nbn:de:kobv:517-opus-63825
http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-63825

Zugleich gedruckt erschienen im Universitätsverlag Potsdam:
ISBN 978-3-86956-227-8
Abstract
Thedevelopmentofself-adaptivesoftwarerequirestheengineeringofanadaptationenginethatcontrols
and adapts the underlying adaptable software by means of feedback loops. The adaptation engine
often describes the adaptation by using runtime models representing relevant aspects of the adaptable
software and particular activities such as analysis and planning that operate on these runtime models.
Tosystematicallyaddresstheinterplaybetweenruntimemodelsandadaptationactivitiesinadaptation
engines, runtime megamodels have been proposed for self-adaptive software. A runtime megamodel
is a specific runtime model whose elements are runtime models and adaptation activities. Thus, a
megamodel captures the interplay between multiple models and between models and activities as well
as the activation of the activities.
In this article, we go one step further and present a modeling language for ExecUtable RuntimE
MegAmodels (EUREMA) that considerably eases the development of adaptation engines by following
a model-driven engineering approach. We provide a domain-specific modeling language and a runtime
interpreter for adaptation engines, in particular for feedback loops. Megamodels are kept explicit and
aliveatruntimeandbyinterpretingthem,theyaredirectlyexecutedtorunfeedbackloops.Additionally,
they can be dynamically adjusted to adapt feedback loops. Thus, EUREMA supports development by
making feedback loops, their runtime models, and adaptation activities explicit at a higher level of
abstraction. Moreover, it enables complex solutions where multiple feedback loops interact or even
operate on top of each other. Finally, it leverages the co-existence of self-adaptation and off-line
adaptation for evolution.
Keywords Model-Driven Engineering, Modeling Languages, Modeling, Models at Runtime, Mega-
models, Model Execution, Self-Adaptive Software, Adaptation Engines, Feedback Loops
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Contents
1 Introduction 1
1.1 State-of-the-Art in Engineering Adaptation Engines . . . . . . . . . . . . . . . . . . . 2
1.2 Approach: EUREMA – ExecUtable RuntimE MegAmodels . . . . . . . . . . . . . . . 4
1.3 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.4 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2 Terminology, Concepts, and Requirements 7
2.1 Feedback Loops . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2 Knowledge & Runtime Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.3 Sensors and Effectors & Monitor and Execute . . . . . . . . . . . . . . . . . . . . . . 10
2.4 Layered Architecture. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.5 Off-line Adaptation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3 Modeling a Feedback Loop 13
3.1 Overview of the EUREMA Language . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.2 Modeling a Single Feedback Loop . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.3 Trigger for Feedback Loops . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.4 Modularizing Feedback Loop Diagrams . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4 Modeling Multiple Feedback Loops 21
4.1 Independent Feedback Loops . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.2 Coordination of Multiple Feedback Loops . . . . . . . . . . . . . . . . . . . . . . . . 22
4.2.1 Sequencing Complete Feedback Loops . . . . . . . . . . . . . . . . . . . . . . 23
4.2.2 Sequencing Analysis and Planning of Feedback Loops . . . . . . . . . . . . . . 24
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5 Modeling Layered Architectures 27
5.1 Declarative Reflection – User-defined Reflection Models . . . . . . . . . . . . . . . . . 28
5.2 Procedural Reflection – EUREMA-based Reflection Models . . . . . . . . . . . . . . . 30
6 Modeling Off-line Adaptation 33
7 Execution 37
7.1 Metamodel and Execution Semantics . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
7.1.1 (1) Adaptation Activities, Runtime Models, and Feedback Loops . . . . . . . . 37
7.1.2 (2) Layered Architectures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
7.1.3 (3) Execution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
7.2 Metamodel and Interpreter Implementation . . . . . . . . . . . . . . . . . . . . . . . 41
8 Discussion and Evaluation 43
8.1 Requirements Coverage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
8.2 Application of MDE Techniques in EUREMA . . . . . . . . . . . . . . . . . . . . . . 45
8.3 Application of the EUREMA Language . . . . . . . . . . . . . . . . . . . . . . . . . . 46
8.3.1 Rainbow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
8.3.2 DiVA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
8.3.3 PLASMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
8.3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
8.4 Runtime Characteristics of the EUREMA Interpreter. . . . . . . . . . . . . . . . . . . 50
9 Conclusion and Future Work 53
Bibliography 55
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