Model-driven engineering of adaptation engines for self-adaptive software

Model-driven engineering of adaptation engines for self-adaptive software PDF

Author: Thomas Vogel

Publisher: Universitätsverlag Potsdam

Published: 2013

Total Pages: 74

ISBN-13: 3869562277

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The development of self-adaptive software requires the engineering of an adaptation engine that controls 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. To systematically address the interplay between runtime models and adaptation activities in adaptation 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 alive at runtime and by interpreting them, they are directly executed to run feedback loops. 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.

Model-driven Engineering of Self-adaptive Software

Model-driven Engineering of Self-adaptive Software PDF

Author: Thomas Vogel

Publisher:

Published: 2018

Total Pages: 0

ISBN-13:

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The development of self-adaptive software requires the engineering of an adaptation engine that controls the underlying adaptable software by a feedback loop. State-of-the-art approaches prescribe the feedback loop in terms of numbers, how the activities (e.g., monitor, analyze, plan, and execute (MAPE)) and the knowledge are structured to a feedback loop, and the type of knowledge. Moreover, the feedback loop is usually hidden in the implementation or framework and therefore not visible in the architectural design. Additionally, an adaptation engine often employs runtime models that either represent the adaptable software or capture strategic knowledge such as reconfiguration strategies. State-of-the-art approaches do not systematically address the interplay of such runtime models, which would otherwise allow developers to freely design the entire feedback loop. This thesis presents ExecUtable RuntimE MegAmodels (EUREMA), an integrated model-driven engineering (MDE) solution that rigorously uses models for engineering feedback ...

Software Engineering for Self-Adaptive Systems

Software Engineering for Self-Adaptive Systems PDF

Author: Betty H. C. Cheng

Publisher: Springer Science & Business Media

Published: 2009-06-19

Total Pages: 271

ISBN-13: 3642021603

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The carefully reviewed papers in this state-of-the-art survey describe a wide range of approaches coming from different strands of software engineering, and look forward to future challenges facing this ever-resurgent and exacting field of research.

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Models@run.time PDF

Author: Nelly Bencomo

Publisher: Springer

Published: 2014-07-05

Total Pages: 329

ISBN-13: 3319089153

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Traditionally, research on model-driven engineering (MDE) has mainly focused on the use of models at the design, implementation, and verification stages of development. This work has produced relatively mature techniques and tools that are currently being used in industry and academia. However, software models also have the potential to be used at runtime, to monitor and verify particular aspects of runtime behavior, and to implement self-* capabilities (e.g., adaptation technologies used in self-healing, self-managing, self-optimizing systems). A key benefit of using models at runtime is that they can provide a richer semantic base for runtime decision-making related to runtime system concerns associated with autonomic and adaptive systems. This book is one of the outcomes of the Dagstuhl Seminar 11481 on [email protected] held in November/December 2011, discussing foundations, techniques, mechanisms, state of the art, research challenges, and applications for the use of runtime models. The book comprises four research roadmaps, written by the original participants of the Dagstuhl Seminar over the course of two years following the seminar, and seven research papers from experts in the area. The roadmap papers provide insights to key features of the use of runtime models and identify the following research challenges: the need for a reference architecture, uncertainty tackled by runtime models, mechanisms for leveraging runtime models for self-adaptive software, and the use of models at runtime to address assurance for self-adaptive systems.

Formal Methods for Model-Driven Engineering

Formal Methods for Model-Driven Engineering PDF

Author: Marco Bernardo

Publisher: Springer

Published: 2012-06-26

Total Pages: 444

ISBN-13: 3642309828

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This book presents 11 tutorial lectures by leading researchers given at the 12th edition of the International School on Formal Methods for the Design of Computer, Communication and Software Systems, SFM 2012, held in Bertinoro, Italy, in June 2012. SFM 2012 was devoted to model-driven engineering and covered several topics including modeling languages; model transformations, functional and performance modeling and analysis; and model evolution management.

Software Engineering Research, Management and Applications

Software Engineering Research, Management and Applications PDF

Author: Roger Lee

Publisher: Springer

Published: 2014-11-01

Total Pages: 239

ISBN-13: 3319112651

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This edited book presents scientific results of the 12th International Conference on Software Engineering, Artificial Intelligence Research, Management and Applications (SERA 2014) held on August 31 – September 4, 2014 in Kitakyushu, Japan. The aim of this conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users, and students to discuss the numerous fields of computer science and to share their experiences and exchange new ideas and information in a meaningful way. Research results about all aspects (theory, applications and tools) of computer and information science, and to discuss the practical challenges encountered along the way and the solutions adopted to solve them. This publication captures 17 of the conference’s most promising papers.

Software Engineering for Self-Adaptive Systems III. Assurances

Software Engineering for Self-Adaptive Systems III. Assurances PDF

Author: Rogério de Lemos

Publisher: Springer

Published: 2018-01-16

Total Pages: 435

ISBN-13: 3319741837

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A major challenge for modern software systems is to become more cost-effective, while being versatile, flexible, resilient, energy-efficient, customizable, and configurable when reacting to run-time changes that may occur within the system itself, its environment or requirements. One of the most promising approaches to achieving such properties is to equip the software system with self-adaptation capabilities. Despite recent advances in this area, one key aspect that remains to be tackled in depth is the provision of assurances. Originating from a Dagstuhl seminar held in December 2013, this book constitutes the third volume in the series “Software Engineering for Self-Adaptive Systems”, and looks specifically into the provision of assurances. Opening with an overview chapter on Research Challenges, the book presents 13 further chapters written and carefully reviewed by internationally leading researchers in the field. The book is divided into topical sections on research challenges, evaluation, integration and coordination, and reference architectures and platforms.

Engineering Computational Emotion - A Reference Model for Emotion in Artificial Systems

Engineering Computational Emotion - A Reference Model for Emotion in Artificial Systems PDF

Author: M. Guadalupe Sánchez-Escribano

Publisher: Springer

Published: 2017-06-17

Total Pages: 272

ISBN-13: 3319594303

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This book provides a new perspective on emotion in artificial systems. It presents an insightful explanation of how emotion might emerge deep inside the systems, and emotional behaviour could be seen as a consequence of their internal management. The final approach attempts to account for a range of events associated with emotion, from functional and behavioural features to aspects related to the dynamics and the development of feeling. The book provides a theoretical foundation for engineering and designing computational emotion as a framework for developing future adaptive systems. It includes a painstaking analysis of the rationales for the features of the final approach, including aspects from the fields of Artificial Intelligence, Psychology, the Cognitive Sciences and Model-based Systems. Synthesizing knowledge from a variety of disciplines, it ultimately presents a model conceptualization following the perspectives of Engineering and the Cognitive Sciences.

Creativity in Intelligent Technologies and Data Science

Creativity in Intelligent Technologies and Data Science PDF

Author: Alla G. Kravets

Publisher: Springer Nature

Published: 2019-08-29

Total Pages: 487

ISBN-13: 3030297500

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This two-volume set constitutes the proceedings of the Third Conference on Creativity in Intellectual Technologies and Data Science, CIT&DS 2019, held in Volgograd, Russia, in September 2019. The 67 full papers, 1 short paper and 3 keynote papers presented were carefully reviewed and selected from 231 submissions. The papers are organized in topical sections in the two volumes. Part I: cyber-physical systems and Big Data-driven world. Part II: artificial intelligence and deep learning technologies for creative tasks; intelligent technologies in social engineering.

Advances in Service-Oriented and Cloud Computing

Advances in Service-Oriented and Cloud Computing PDF

Author: Maria Fazio

Publisher: Springer Nature

Published: 2020-12-04

Total Pages: 234

ISBN-13: 3030631613

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This volume contains the technical papers presented in the workshops, which took place at the 7th European Conference on Service-Oriented and Cloud Computing, ESOCC 2018, held in Como, Italy, in September 2018:Joint Cloudways and OptiMoCS Workshop; 14th International Workshop on Engineering Service-Oriented Applications and Cloud Services. Additionally the papers from ESOCC 2018 PhD Symposium and ESOCC 2018 EU Projects Track were included in the volume. The 22 full papers were carefully reviewed and selected from 34 submissions. The papers focus on specific topics in service-oriented and cloud computing domains such as limits and/or advantages of existing cloud solutions, future internet technologies, efficient and adaptive deployment and management of service-based applications across multiple clouds, novel cloud service migration practices and solutions, digitization of enterprises in the cloud computing era, federated cloud networking services.