ASCENS: Engineering Autonomic Service-Component Ensembles

Today’s developers often face the demanding task of developing software for ensembles: systems with massive numbers of nodes, operating in open and non-deterministic environments with complex interactions, and the need to dynamically adapt to new requirements, technologies or environmental conditions without redeployment and without interruption of the system’s functionality. Conventional development approaches and languages do not provide adequate support for the problems posed by this challenge. The goal of the ASCENS project is to develop a coherent, integrated set of methods and tools to build software for ensembles. To this end we research foundational issues that arise during the development of these kinds of systems, and we build mathematical models that address them. Based on these theories we design a family of languages for engineering ensembles, formal methods that can handle the size, complexity and adaptivity required by ensembles, and software-development methods that provide guidance for developers. In this paper we provide an overview of several research areas of ASCENS: the SOTA approach to ensemble engineering and the underlying formal model called GEM, formal notions of adaptation and awareness, the SCEL language, quantitative analysis of ensembles, and finally software-engineering methods for ensembles

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

Engineering self-awareness with knowledge management in dynamic systems: a case for volunteer computing

The complexity of the modem dynamic computing systems has motivated software engineering researchers to explore new sources of inspiration for equipping such systems with autonomic behaviours. Self-awareness has recently gained considerable attention as a prominent property for enriching the self-adaptation capabilities in systems operating in dynamic, heterogeneous and open environments. This thesis investigates the role of knowledge and its dynamic management in realising various levels of self-awareness for enabling self­adaptivity with different capabilities and strengths. The thesis develops a novel multi-level dynamic knowledge management approach for managing and representing the evolving knowledge. The approach is able to acquire 'richer' knowledge about the system's internal state and its environment in addition to managing the trade-offs arising from the adaptation conflicting goals. The thesis draws on a case from the volunteer computing, as an environment characterised by openness, heterogeneity, dynamism, and unpredictability to develop and evaluate the approach. This thesis takes an experimental approach to evaluate the effectiveness of the of the dynamic knowledge management approach. The results show the added value of the approach to the self-adaptivity of the system compared to classic self­adaptation capabilities

Implementing artificial awareness with KnowLang

To become interaction-aware, an autonomic cyber-physical system needs to be aware of its physical environment and whereabouts and its current internal status. This ability is defined as artificial awareness and it helps intelligent software-intensive systems perceive changes, draw inferences for their own behavior and react. Originally, artificial awareness depends on the knowledge we transfer to a system and how we make the system use that knowledge, so it can exhibit intelligence. Artificial awareness requires a means of sensing changes, so the external and internal worlds can be perceived through their raw events and data. To build an efficient awareness mechanism, we need to provide a means of monitoring and knowledge representation along with a proper reasoner deriving awareness conclusions. In this paper, we present an approach to implementing artificial awareness with KnowLang, a special framework for knowledge representation and reasoning. KnowLang provides for a special knowledge context and a special reasoner operating in that context. The reasoner communicates with the host system via special ASK and TELL operators allowing for awareness conclusions and updates