A survey on engineering approaches for self-adaptive systems (extended version)

The complexity of information systems is increasing in recent years, leading to increased effort for maintenance and configuration. Self-adaptive systems (SASs) address this issue. Due to new computing trends, such as pervasive computing, miniaturization of IT leads to mobile devices with the emerging need for context adaptation. Therefore, it is beneficial that devices are able to adapt context. Hence, we propose to extend the definition of SASs and include context adaptation. This paper presents a taxonomy of self-adaptation and a survey on engineering SASs. Based on the taxonomy and the survey, we motivate a new perspective on SAS including context adaptation

Coordination Issues in Complex Socio-technical Systems: Self-organisation of Knowledge in MoK

The thesis proposes the Molecules of Knowledge (MoK) model for self-organisation of knowledge in knowledge-intensive socio-technical systems. The main contribution is the conception, definition, design, and implementation of the MoK model. The model is based on a chemical metaphor for self-organising coordination, in which coordination laws are interpreted as artificial chemical reactions ruling evolution of the molecules of knowledge living in the system (the information chunks), indirectly coordinating the users working with them. In turn, users may implicitly affect system behaviour with their interactions, according to the cognitive theory of behavioural implicit communication, integrated in MoK. The theory states that any interaction conveys tacit messages that can be suitably interpreted by the coordination model to better support users' workflows. Design and implementation of the MoK model required two other contributions: conception, design, and tuning of the artificial chemical reactions with custom kinetic rates, playing the role of the coordination laws, and development of an infrastructure supporting situated coordination, both in time, space, and w.r.t. the environment, along with a dedicated coordination language

Engineering Complex Computational Ecosystems

Self-organising pervasive ecosystems of devices are set to become a major vehicle for delivering infrastructure and end-user services. The inherent complexity of such systems poses new challenges to those who want to dominate it by applying the principles of engineering. The recent growth in number and distribution of devices with decent computational and communicational abilities, that suddenly accelerated with the massive diffusion of smartphones and tablets, is delivering a world with a much higher density of devices in space. Also, communication technologies seem to be focussing on short-range device-to-device (P2P) interactions, with technologies such as Bluetooth and Near-Field Communication gaining greater adoption. Locality and situatedness become key to providing the best possible experience to users, and the classic model of a centralised, enormously powerful server gathering and processing data becomes less and less efficient with device density. Accomplishing complex global tasks without a centralised controller responsible of aggregating data, however, is a challenging task. In particular, there is a local-to-global issue that makes the application of engineering principles challenging at least: designing device-local programs that, through interaction, guarantee a certain global service level. In this thesis, we first analyse the state of the art in coordination systems, then motivate the work by describing the main issues of pre-existing tools and practices and identifying the improvements that would benefit the design of such complex software ecosystems. The contribution can be divided in three main branches. First, we introduce a novel simulation toolchain for pervasive ecosystems, designed for allowing good expressiveness still retaining high performance. Second, we leverage existing coordination models and patterns in order to create new spatial structures. Third, we introduce a novel language, based on the existing ``Field Calculus'' and integrated with the aforementioned toolchain, designed to be usable for practical aggregate programming

Classification and prediction of whereabouts patterns from the Reality Mining dataset

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Engineering Smart Software Services for Intelligent Pervasive Systems

Pervasive computing systems, envisioned as systems that blend with the physical environment to enhance the quality of life of its users, are rapidly becoming a not so distant reality. However, many challenges must be addressed before realizing the goal of having such computing systems as part of our everyday life. One such challenge is related to the problem of how to develop in a systematic way the software that lies behind pervasive systems, operating them and allowing them to intelligently adapt both to users' changing needs and to variations in the environment. In spite of the important strides done in recent years concerning the engineering of software that places the actual, immediate needs and preferences of users in the center of attention, to the best of our knowledge no work has been devoted to the study of the engineering process for building software for pervasive systems. In this dissertation we focus on the engineering process to build smart software services for pervasive systems. Specifically, we first introduce as our first major contribution a model for the systematic construction of software for pervasive systems, which has been derived using analytical, evidence-based, and empirical methodologies. Then, on the basis of the proposed model, we investigate two essential mechanisms that provide support for the engineering of value-added software services for smart environments, namely the learning of users' daily routines and the continuous identification of users. For the case of learning users' daily routines, we propose what is our second main contribution: a novel approach that discovers periodic-frequent routines in event data from sensors and smart devices deployed at home. For the continuous identification of users we propose what is our third major contribution: a novel approach based on behavioral biometrics which is able to recognize identities without requiring any specific gesture, action, or activity from the users. The two approaches proposed have been extensively evaluated through studies in the lab, based on synthetic data, and in the wild, showing that they can be effectively applied to different scenarios and environments. In sum, the engineering model proposed in this dissertation is expected to serve as a basis to further the research and development efforts in key aspects that are necessary to build value-added smart software services that bring pervasive systems closer to the way they have been envisioned. Furthermore, the approaches proposed for learning users' daily routines and recognizing users' identities in smart environments are aimed at contributing to the investigation and development of the data analytics technology necessary for the smart adaptation and evolution of the software in pervasive systems to users' needs

A framework for engineering reusable self-adaptive systems

The increasing complexity and size of information systems result in an increasing effort for maintenance. Additionally, miniaturization of devices leads to mobility and the need for context-adaptation. Self-adaptive Systems (SASs) can adapt to changes in their environment or the system itself. So far, however, development of SASs is frequently tailored towards the requirements of use cases. The research for reusable elements — for implementation as well as design processes — is often neglected. Integrating reusable processes and implementation artifacts into a framework and offering a tool suite to developers would make development of SASs faster and less error-prone. This thesis presents the Framework for Engineering Self-adaptive Systems (FESAS). It offers a reusable implementation of a reference system, tools for implementation and design as well as a middleware for controlling system deployment. As a second contribution, this thesis introduces a new approach for self-improvement of SASs which complements the SAS with meta-adaptation

Collective Adaptive Systems: Qualitative and Quantitative Modelling and Analysis (Dagstuhl Seminar 14512)

This report documents the program and the outcomes of Dagstuhl Seminar 14512 "Collective Adaptive Systems: Qualitative and Quantitative Modelling and Analysis". Besides presentations on current work in the area, the seminar focused on the following topics: (i) Modelling techniques and languages for collective adaptive systems based on the above formalisms. (ii) Verification of collective adaptive systems. (iii) Humans-in-the-loop in collective adaptive systems