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Im Rahmen des Projekts „Plattform selbstadaptiver Benutzungsschnittstellen zur Gerätebedienung als individuelles Assistenzsystem“ (kurz: PLUG-IN), das vom Bundesministerium für Bildung und Forschung (BMBF) gefördert wird, wurde im Dezember 2021 eine bevölkerungsrepräsentative Online-Befragung durchgeführt. Im Projekt PLUG-IN entwickeln Partner*innen aus Softwaretechnik, Design, Datenwissenschaft und Sozialwissenschaften ein Assistenzsystem für Benutzungsschnittstellen von Haushaltsgeräten. Das System soll in der Lage sein soll, sich optimal an die Nutzenden anzupassen. Dadurch soll eine individuelle und interaktive Bedienung sichergestellt werden. Mit der Befragung sollten Anforderungen an das System, die zu Projektbeginn mittels qualitativer Methoden generiert worden sind, validiert und der Zwischenstand der Entwicklung evaluiert werden. Es handelt sich um eine bevölkerungsrepräsentative Quotenstichprobe von 1.146 Personen im Alter von 18 bis 92 Jahren. Zentrale Ergebnisse der Befragung: - Der Backofen wird besonders häufig von Erwerbstätigen und Menschen im Mehrpersonenhaushalt – hier vor allem in Haushalten mit Kindern – genutzt. Die meisten Befragten bevorzugen klassische Heizarten wie z.B. Umluft/Heißluft. Automatikprogramme haben sich bislang kaum durchgesetzt. - Die Entscheidung darüber, welche Speise in der Küche zubereitet wird, ist sehr tagesformabhängig und hängt von vielen weiteren Faktoren wie persönlichen Vorlieben und verfügbarer Zeit ab. Wie häufig mit anderen Personen zusammen gekocht oder gebacken wird, variiert stark, kommt aber bei fast einem Drittel mindestens einmal pro Woche vor. - Neun von zehn Befragte nutzen ein Smartphone. Hinsichtlich technischer Neuentwicklungen bezeichnet sich die Mehrheit der Befragten als teilweise bis ziemlich neugierig. Diese Selbsteinschätzung scheint abhängig davon, ob digitale Geräte wie Smartphone und/oder Tablet genutzt werden. Auffällig ist, dass mehr als die Hälfte der Befragten von Schwierigkeiten bei der Bedienung technischer Geräte berichtet. - Knapp die Hälfte der Befragten ist grundsätzlich bereit, Daten zum Nutzungsverhalten mit dem Gerät zu teilen. Befragte im jüngeren und mittleren Alter mit mindestens einer chronischen Erkrankung signalisieren relativ starkes Interesse. Geht es um die Frage, welche Informationen zum Nutzungsverhalten vom System lokal gespeichert und verarbeitet werden dürfen (z. B. Entfernung zum Eingabefeld, Treffgenauigkeit einer Taste), ist jedoch auch die Gruppe der grundsätzlich Interessierten eher reserviert. Insgesamt besteht ein hoher Anspruch an eine transparente und verständliche Darstellung der Datenspeicherung, Verarbeitung und Wirkweise des PLUG-IN-Systems. - Die Mehrheit der Befragten bekundet Interesse an Veränderungsvorschlägen vom System. Etwa ein Drittel der Befragten möchte hingegen nicht, dass das System Vorschläge unterbreitet, sondern bevorzugt es, alle Änderungen selbst vorzunehmen – vor allem Befragte im höheren Alter. - 45 Prozent der Befragten sind bereit, einen digitalen Einrichtungsassistenten zu verwenden, der ihnen das System bei der ersten Benutzung näherbringt. Befragte im jüngeren und mittleren Alter mit guter subjektiver Gesundheit, Personen, die ein Smartphone und Tablet nutzen und diejenigen, die über einen hohen formalen Bildungsgrad verfügen, sind eher dazu bereit., As part of the project "Platform of self-adaptive user interfaces for device operation as an individual assistance system" (PLUG-IN), funded by the German Federal Ministry of Education and Research (BMBF), a population-representative quantitative online survey was conducted in December 2021. In the PLUG-IN project, partners from the fields of software engineering, design, data science and social sciences are developing an assistance system for user interfaces of household appliances. The system should be able to optimally adapt to the user. This should ensure individual and interactive operation. Aim of the survey was to validate requirements, which were generated at the beginning of the project using qualitative methods, and to evaluate the interim state of development. The sample was a population-representative quota sample of 1,146 people between the ages of 18 and 92. Key findings of the quantitative survey: - The oven is used particularly frequently by employed people and those in multi-person household, especially in households with children. Most respondents prefer classic heating modes. Automatic programs have hardly become established so far. - The decision about which food to prepare in the kitchen depends on many factors such as personal preferences and available time. The frequency with which people cook or bake together with others varies greatly, but almost one-third do so at least once a week. - Nine out of ten respondents use a smartphone. With regard to new technical developments, the majority of respondents describe themselves as partly to fairly curious. This self-assessment seems to depend on whether digital devices such as smartphones and/or tablets are used. It is striking that more than half of the respondents report difficulties in using technical devices. - Just under half of respondents are basically willing to share data on usage behavior with the PLUG-IN device. Younger and middle-aged respondents with at least one chronic condition are comparatively more interested. However, even the group of those interested in principle is rather reserved when it comes to the question of what information about usage behavior may be stored and processed locally by the PLUG-IN system (e.g., distance to the interface, touch accuracy). Overall, there is a high demand for a transparent and comprehensible presentation of the data storage, processing and mode of operation of the PLUG-IN system. - The majority of respondents express interest in the system making suggestions for changes. About one-third of respondents, on the other hand, do not want the system to make suggestions, but would like to make all changes themselves - especially older respondents. - Forty-five percent of respondents are willing to use a digital setup wizard to help them use the system for the first time. Younger respondents and middle-aged respondents with good subjective health, people who use a smartphone and a tablet, and people with a high level of formal education are more willing to do so.

Road grade greatly affects energy consumption and pollutant emission of heavy-duty vehicles. It is therefore necessary to develop representative three-parameter driving cycles while taking road grade into account. However, the low running efficiency of existing methods for developing driving cycles is a serious problem. To improve efficiency, inspired by the idea that intelligent algorithms with self-adaptivity can accelerate convergence, an adaptive Markov chain evolution (AMCE) method is proposed in this study. Based on the characteristics of the evolution strategies of a Markov chain evolution (MCE) satisfying the Markov property, a strategy boundary variable is defined to classify the MCE evolution strategies into two categories, one with the global and one with local search capability. Then, an adaptive probability equation is used to adjust the proportion of the evolution strategies, thus the evolution strategies can satisfy not only the Markov property of driving cycle but is also self-adaptive. By collecting driving data including the elevation information from heavy-duty vehicles driving on a highway, a three-parameter driving cycle with road grade was generated using the proposed method. In comparison with the MCE method, the proposed method can obtain the three-parameter representative driving cycles on the highway, and running efficiency was increased by 43.08% under the given conditions. Additionally, the rationality and necessity of the proposed method are fully verified.

Cyber-Physical Systems (CPS) are interconnected devices, reactive and dynamic to sensed external and internal triggers. The H2020 CERBERO EU Project is developing a design environment composed by modelling, deployment and verification tools for adaptive CPS. This paper focuses on its efficient support for run-time self-adaptivity.

This thesis presents an approach for the design time analysis of energy efficiency for static and self-adaptive software systems. The quality characteristics of a software system, such as performance and operating costs, strongly depend upon its architecture. Software architecture is a high-level view on software artifacts that reflects essential quality characteristics of a system under design. Design decisions made on an architectural level have a decisive impact on the quality of a system. Revising architectural design decisions late into development requires significant effort. Architectural analyses allow software architects to reason about the impact of design decisions on quality, based on an architectural description of the system. An essential quality goal is the reduction of cost while maintaining other quality goals. Power consumption accounts for a significant part of the Total Cost of Ownership (TCO) of data centers. In 2010, data centers contributed 1.3% of the world-wide power consumption. However, reasoning on the energy efficiency of software systems is excluded from the systematic analysis of software architectures at design time. Energy efficiency can only be evaluated once the system is deployed and operational. One approach to reduce power consumption or cost is the introduction of self-adaptivity to a software system. Self-adaptive software systems execute adaptations to provision costly resources dependent on user load. The execution of reconfigurations can increase energy efficiency and reduce cost. If performed improperly, however, the additional resources required to execute a reconfiguration may exceed their positive effect. Existing architecture-level energy analysis approaches offer limited accuracy or only consider a limited set of system features, e.g., the used communication style. Predictive approaches from the embedded systems and Cloud Computing domain operate on an abstraction that is not suited for architectural analysis. The execution of adaptations can consume additional resources. The additional consumption can reduce performance and energy efficiency. Design time quality analyses for self-adaptive software systems ignore this transient effect of adaptations. This thesis makes the following contributions to enable the systematic consideration of energy efficiency in the architectural design of self-adaptive software systems: First, it presents a modeling language that captures power consumption characteristics on an architectural abstraction level. Second, it introduces an energy efficiency analysis approach that uses instances of our power consumption modeling language in combination with existing performance analyses for architecture models. The developed analysis supports reasoning on energy efficiency for static and self-adaptive software systems. Third, to ease the specification of power consumption characteristics, we provide a method for extracting power models for server environments. The method encompasses an automated profiling of servers based on a set of restrictions defined by the user. A model training framework extracts a set of power models specified in our modeling language from the resulting profile. The method ranks the trained power models based on their predicted accuracy. Lastly, this thesis introduces a systematic modeling and analysis approach for considering transient effects in design time quality analyses. The approach explicitly models inter-dependencies between reconfigurations, performance and power consumption. We provide a formalization of the execution semantics of the model. Additionally, we discuss how our approach can be integrated with existing quality analyses of self-adaptive software systems. We validated the accuracy, applicability, and appropriateness of our approach in a variety of case studies. The first two case studies investigated the accuracy and appropriateness of our modeling and analysis approach. The first study evaluated the impact of design decisions on the energy efficiency of a media hosting application. The energy consumption predictions achieved an absolute error lower than 5.5% across different user loads. Our approach predicted the relative impact of the design decision on energy efficiency with an error of less than 18.94%. The second case study used two variants of the Spring-based community case study system PetClinic. The case study complements the accuracy and appropriateness evaluation of our modeling and analysis approach. We were able to predict the energy consumption of both variants with an absolute error of no more than 2.38%. In contrast to the first case study, we derived all models automatically, using our power model extraction framework, as well as an extraction framework for performance models. The third case study applied our model-based prediction to evaluate the effect of different self-adaptation algorithms on energy efficiency. It involved scientific workloads executed in a virtualized environment. Our approach predicted the energy consumption with an error below 7.1%, even though we used coarse grained measurement data of low accuracy to train the input models. The fourth case study evaluated the appropriateness and accuracy of the automated model extraction method using a set of Big Data and enterprise workloads. Our method produced power models with prediction errors below 5.9%. A secondary study evaluated the accuracy of extracted power models for different Virtual Machine (VM) migration scenarios. The results of the fifth case study showed that our approach for modeling transient effects improved the prediction accuracy for a horizontally scaling application. Leveraging the improved accuracy, we were able to identify design deficiencies of the application that otherwise would have remained unnoticed.

Evaluation is an important concern for building and monitoring the quality of software. The complex nature of self-adaptive systems demands continuous monitoring of their behavior and execution environment, which challenge the quality of their adaptations in dynamic environments. The characteristics of self-adaptive systems demand a continuous evaluation of their performances and improvement of the adaptation process. In this chapter our objective is to identify general guidelines for the evaluation of self-adaptive systems independent of their type, application domain, or implementation details. We propose a taxonomy for the evaluation of the quality of self-adaptive systems based on five dimensions: scope, time, mechanisms, perspective, and type. Further, we identify and introduce the main available evaluation approaches and analyze them based on the proposed taxonomy. Finally, we discuss several trade-offs concerning each dimension in the taxonomy, trade-offs which should be addressed during the evaluation of self-adaptive systems.

Heterogeneous Multiprocessor Systems-on-a-Chip (MPSoCs) with programmable hardware acceleration are currently gaining market share in the embedded device domain. Largest MPSoCs combine several software processing cores with programmable logic. In these systems, reaching the optimal implementation performance is difficult because many manual and time-consuming steps are required to build, from the application specification, a prototype with measurable performances. In this paper, a method is developed that, based on state-of-the-art tools and High-Level Synthesis, deploys within less than an hour a whole hardware-software rapid prototype from a unique dataflow-based application representation: DAMHSE (DAtaflow Method for Hardware/Software Exploration). A human-driven Design Space Exploration (DSE) is conducted in order to find the most performing architectural solution, and compilable/synthesizable code is generated. The method has been tested on an image processing system with software and hardware parallelism. Results show that the obtained absolute performance (pixel/cycles) reaches state-of-the-art, and that DAMHSE leads to a heterogeneous system where performance increases significantly when the application is granted with more hardware resources. One of the greatest challenges in creating such a design automation method resides in the application behavior that may change over time and affect application concurrency and system performance. In order to overcome this problem, the design-time DAtaflow Method for Hardware/Software Exploration (DAMHSE) method is complemented with a runtime application management system that dynamically dispatches jobs (tasks) among the available processing elements (CPUs and/or hardware accelerators). Experimental results show that the performance penalty due to runtime application mapping and scheduling is limited and that the computational performance of the adaptive system remains high. Apart from the vendor-specific HLS, the tools and the frameworks used by the proposed method are open source and tutorials are available to reproduce the results.

Design principles define a set of guidelines for the development of software. Their objective is to provide mechanisms to design and implement good software and to avoid making inappropriate design decisions which may affect the quality of the software during its life cycle and which make software difficult to maintain and extend. The object-oriented design community uses several design principles, among which the most diffused ones are: single responsibility, open-closed, Liskov substitution, interface segregation, and dependency inversion. In this paper, it is investigated how these design principles may be interpreted and adopted for the development of self-adaptive software which exploit MAPE feedback control loops. Attention is focused on the quality improvement of self-adaptive systems through these design principles.

International audience; In the context of mission-critical, safety-critical, and remote-controlled applications, it is required to equip systems with self-adapting capabilities. Adaptation is required in post-manufacturing to correct yield loss and achieve zero defective parts-per-million as well as during normal operation to account for different application scenarios and for varying environmental conditions. A self-adaptive system must be capable of providing the required high performances after manufacturing and throughout its normal operation regardless the application scenario wherein it is deployed and despite the varying environmental conditions. In this paper, we describe a generic post-manufacturing self-adaptation technique for RF circuits as well as concurrent self-adaptation techniques for a safety-critical medical sensor for glaucoma diagnosis and for a NFC system which is very sensitive to the environment in which it operates.

We present a context-oriented approach to design and implement self-adaptive component-based software in resource-constrained Cyber physical Systems (CPSs). Because of unpredictable environment dynamics, developers must design and implement CPS software to dynamically adapt to widely different situations. Our approach provides design concepts and language support to meet this requirement against severe resource constraints. To this end, we bring a notion of context-oriented design and programming down to platforms that-because of extreme resource constraints-currently leverage fairly undisciplined design techniques and rather rudimentary component-based frameworks. Early results demonstrate that our approach improves the quality of the resulting implementations facilitating testing, maintenance, and evolution at the price of a negligible system overhead.

We present programming abstractions for implementing adaptive Wireless Sensor Network (WSN) software. The need for adaptability arises in WSNs because of unpredictable environment dynamics, changing requirements, and resource scarcity. However, after about a decade of research in WSN programming, developers are still left with no dedicated support. To address this issue, we bring concepts from Context-Oriented Programming (COP) down to WSN devices. Contexts model the situations that WSN software needs to adapt to. Using COP, programmers use a notion of layered function to implement context-dependent behavioral variations of WSN code. To this end, we provide language-independent design concepts to organize the context-dependent WSN operating modes, decoupling the abstractions from their concrete implementation in a programming language. Our own implementation, called CONESC, extends nesC with COP constructs. Based on three representative applications, we show that CONESC greatly simplifies the resulting code and yields increasingly decoupled implementations compared to nesC. For example, by model-checking every function in either implementations, we show a ~50% reduction in the number of program states that programmers need to deal with, indicating easier debugging. In our tests, this comes at the price of a maximum 2.5% (4.5%) overhead in program (data) memory.

International audience; This paper presents an original approach of bandwidth-oriented self-adaptivity in the domain of Network-on-Chip, where reconfiguration is handled by network interfaces offering traffic with guarantee of service. Reconfiguration is first based on multiple FIFOs with variables bounds and implemented in a single dual-port memory with a dedicated controller. Secondly, it relies on multiple and compliant TDMA tables based on a new heuristic for path computation. Combination of both techniques provide significant bandwidth improvement with a negligible resource overhead. The proposed solution is demonstrated with cycle-accurate VHDL simulation and FPGA implementation for synthetic and image processing applications.

Since delay-sensitive and bandwidth-intense multimedia applications have emerged in the Internet, the demand for network resources has seen a steady increase during the last decade. Specifically, wireless networks have become pervasive and highly populated.These motivations are behind the problems considered in this dissertation.The topic of my PhD is about the application of game theory, queueing theory and learning techniques in wireless networks under some QoS constraints, especially in partially observable environments.We consider different layers of the protocol stack. In fact, we study the Opportunistic Spectrum Access (OSA) at the Medium Access Control (MAC) layer through Cognitive Radio (CR) approaches.Thereafter, we focus on the congestion control at the transport layer, and we develop some congestion control mechanisms under the TCP protocol.The roadmap of the research is as follows. Firstly, we focus on the MAC layer, and we seek for optimal OSA strategies in CR networks. We consider that Secondary Users (SUs) take advantage of opportunities in licensed channels while ensuring a minimum level of QoS. In fact, SUs have the possibility to sense and access licensed channels, or to transmit their packets using a dedicated access (like 3G). Therefore, a SU has two conflicting goals: seeking for opportunities in licensed channels, but spending energy for sensing those channels, or transmitting over the dedicated channel without sensing, but with higher transmission delay. We model the slotted and the non-slotted systems using a queueing framework. Thereafter, we analyze the non-cooperative behavior of SUs, and we prove the existence of a Nash equilibrium (NE) strategy. Moreover, we measure the gap of performance between the centralized and the decentralized systems using the Price of Anarchy (PoA).Even if the OSA at the MAC layer was deeply investigated in the last decade, the performance of SUs, such as energy consumption or Quality of Service (QoS) guarantee, was somehow ignored. Therefore, we study the OSA taking into account energy consumption and delay. We consider, first, one SU that access opportunistically licensed channels, or transmit its packets through a dedicated channel. Due to the partial spectrum sensing, the state of the spectrum is partially observable. Therefore, we use the Partially Observable Markov Decision Process (POMDP) framework to design an optimal OSA policy for SUs. Specifically, we derive some structural properties of the value function, and we prove that the optimal OSA policy has a threshold structure.Thereafter, we extend the model to the context of multiple SUs. We study the non-cooperative behavior of SUs and we prove the existence of a NE. Moreover, we highlight a paradox in this situation: more opportunities in the licensed spectrum may lead to worst performances for SUs. Thereafter, we focus on the study of spectrum management issues. In fact, we introduce a spectrum manager to the model, and we analyze the hierarchical game between the network manager and SUs.Finally, we focus on the transport layer and we study the congestion control for wireless networks under some QoS and Quality of Experience (QoE) constraints. Firstly, we propose a congestion control algorithm that takes into account applications' parameters and multimedia quality. In fact, we consider that network users maximize their expected multimedia quality by choosing the congestion control strategy. Since users ignore the congestion status at bottleneck links, we use a POMDP framework to determine the optimal congestion control strategy.Thereafter, we consider a subjective measure of the multimedia quality, and we propose a QoE-based congestion control algorithm. This algorithm bases on QoE feedbacks from receivers in order to adapt the congestion window size. Note that the proposed algorithms are designed based on some learning methods in order to face the complexity of solving POMDP problems., La dernière décennie a vu l'émergence d'Internet et l'apparition des applications multimédia qui requièrent de plus en plus de bande passante, ainsi que des utilisateurs qui exigent une meilleure qualité de service. Dans cette perspective, beaucoup de travaux ont été effectués pour améliorer l'utilisation du spectre sans fil.Le sujet de ma thèse de doctorat porte sur l'application de la théorie des jeux, la théorie des files d'attente et l'apprentissage dans les réseaux sans fil,en particulier dans des environnements partiellement observables. Nous considérons différentes couches du modèle OSI. En effet, nous étudions l'accès opportuniste au spectre sans fil à la couche MAC en utilisant la technologie des radios cognitifs (CR). Par la suite, nous nous concentrons sur le contrôle de congestion à la couche transport, et nous développons des mécanismes de contrôle de congestion pour le protocole TCP.

Since delay-sensitive and bandwidth-intense multimedia applications have emerged in the Internet, the demand for network resources has seen a steady increase during the last decade. Specifically, wireless networks have become pervasive and highly populated.These motivations are behind the problems considered in this dissertation.The topic of my PhD is about the application of game theory, queueing theory and learning techniques in wireless networks under some QoS constraints, especially in partially observable environments.We consider different layers of the protocol stack. In fact, we study the Opportunistic Spectrum Access (OSA) at the Medium Access Control (MAC) layer through Cognitive Radio (CR) approaches.Thereafter, we focus on the congestion control at the transport layer, and we develop some congestion control mechanisms under the TCP protocol.The roadmap of the research is as follows. Firstly, we focus on the MAC layer, and we seek for optimal OSA strategies in CR networks. We consider that Secondary Users (SUs) take advantage of opportunities in licensed channels while ensuring a minimum level of QoS. In fact, SUs have the possibility to sense and access licensed channels, or to transmit their packets using a dedicated access (like 3G). Therefore, a SU has two conflicting goals: seeking for opportunities in licensed channels, but spending energy for sensing those channels, or transmitting over the dedicated channel without sensing, but with higher transmission delay. We model the slotted and the non-slotted systems using a queueing framework. Thereafter, we analyze the non-cooperative behavior of SUs, and we prove the existence of a Nash equilibrium (NE) strategy. Moreover, we measure the gap of performance between the centralized and the decentralized systems using the Price of Anarchy (PoA).Even if the OSA at the MAC layer was deeply investigated in the last decade, the performance of SUs, such as energy consumption or Quality of Service (QoS) guarantee, was somehow ignored. Therefore, we study the OSA taking into account energy consumption and delay. We consider, first, one SU that access opportunistically licensed channels, or transmit its packets through a dedicated channel. Due to the partial spectrum sensing, the state of the spectrum is partially observable. Therefore, we use the Partially Observable Markov Decision Process (POMDP) framework to design an optimal OSA policy for SUs. Specifically, we derive some structural properties of the value function, and we prove that the optimal OSA policy has a threshold structure.Thereafter, we extend the model to the context of multiple SUs. We study the non-cooperative behavior of SUs and we prove the existence of a NE. Moreover, we highlight a paradox in this situation: more opportunities in the licensed spectrum may lead to worst performances for SUs. Thereafter, we focus on the study of spectrum management issues. In fact, we introduce a spectrum manager to the model, and we analyze the hierarchical game between the network manager and SUs.Finally, we focus on the transport layer and we study the congestion control for wireless networks under some QoS and Quality of Experience (QoE) constraints. Firstly, we propose a congestion control algorithm that takes into account applications' parameters and multimedia quality. In fact, we consider that network users maximize their expected multimedia quality by choosing the congestion control strategy. Since users ignore the congestion status at bottleneck links, we use a POMDP framework to determine the optimal congestion control strategy.Thereafter, we consider a subjective measure of the multimedia quality, and we propose a QoE-based congestion control algorithm. This algorithm bases on QoE feedbacks from receivers in order to adapt the congestion window size. Note that the proposed algorithms are designed based on some learning methods in order to face the complexity of solving POMDP problems., La dernière décennie a vu l'émergence d'Internet et l'apparition des applications multimédia qui requièrent de plus en plus de bande passante, ainsi que des utilisateurs qui exigent une meilleure qualité de service. Dans cette perspective, beaucoup de travaux ont été effectués pour améliorer l'utilisation du spectre sans fil.Le sujet de ma thèse de doctorat porte sur l'application de la théorie des jeux, la théorie des files d'attente et l'apprentissage dans les réseaux sans fil,en particulier dans des environnements partiellement observables. Nous considérons différentes couches du modèle OSI. En effet, nous étudions l'accès opportuniste au spectre sans fil à la couche MAC en utilisant la technologie des radios cognitifs (CR). Par la suite, nous nous concentrons sur le contrôle de congestion à la couche transport, et nous développons des mécanismes de contrôle de congestion pour le protocole TCP.

The growing complexity of software systems is causing a re-conception of their development and maintenance strategies. Humans should be relieved from an important part of these tasks, which should be performed by systems themselves, leading to consider self-adaptation as a basic architectural concern. Simultaneously, Multi-Agent Systems (MAS) have been developed as a generic approach to solve complex problems. They describe self-aware structures,conceived to be flexible and to be able to adapt to different situations. Advances approaches use organizations to provide further structuring, taking the form of complex agent architectures. Among them, Agreement Technologies (AT) provides an explicit insight into those architectural abstractions. However, they still do not provide mechanisms to change their composition patterns and element types, which are necessary to achieve real self-adaptivity. In this article, we propose an architectural solution for this: the required dynamism will be supported by an emergent agreement - an evolving architectural structure, based on combining predefined controls and protocols. These are handled in the context of the service-oriented, agent-based and organization-centric framework defined in AT and provided by their implementation within the THOMAS platform. This work provides the first architectural abstractions to support this emergent structure. A real-world example showing the interest of this approach is also provided, and some conclusions about its applicability are finally outlined., Sociedad Argentina de Informática e Investigación Operativa