We propose a framework that uses component redundancy for enabling self-adaptation, self-optimisa... more We propose a framework that uses component redundancy for enabling self-adaptation, self-optimisation and self-healing capabilities in component-based enterprise software systems.
Undergraduate Topics in Computer Science (UTiCS) delivers high-quality instructional content for ... more Undergraduate Topics in Computer Science (UTiCS) delivers high-quality instructional content for undergraduates studying in all areas of computing and information science. From core foundational and theoretical material to fi nal-year topics and applications, UTiCS books take a fresh, concise, and modern approach and are ideal for self-study or for a one-or two-semester course. The texts are all authored by established experts in their fi elds, reviewed by an international advisory board, and contain numerous examples and problems. Many include fully worked solutions.
Proceedings of the 1st Workshop on Middleware and Architectures for Autonomic and Sustainable Computing
You will find in these pages the Proceedings of the 1st Workshop on Middleware and Architectures ... more You will find in these pages the Proceedings of the 1st Workshop on Middleware and Architectures for Autonomic and Sustainable Computing (MAASC'11), which was held in Paris, France, in collocation with the 11th International Conference on New Technologies of Distributed Systems (NOTERE'11). MAASC was a particularly lively event, with two exciting keynote presentations, and four papers presented.
CHARIOT - Towards a Continuous High-Level Adaptive Runtime Integration Testbed
Integrated networked systems sense a common environment, learn to navigate the environment and sh... more Integrated networked systems sense a common environment, learn to navigate the environment and share their experiences. Sharing experiences simplifies learning, reducing costly trial and error in complex environments. However, integration produces dependencies that make constituent systems less robust to failures, unexpected outputs and performance anomalies. Even with APIs and reflective, self-aware techniques, system integration still requires expert programming and tuning. Self-integrating systems proposed in recent research automate integration, but can be challenging to validate at scale. We therefore propose CHARIOT, a common test environment to allow for different approaches and systems to be deployed, assessed and compared on a shared platform for the development of self-integrating systems. In this paper, we discuss the underlying requirements and challenges, potential metrics, and a system metamodel to accommodate these.
We propose a framework that uses component redundancy for enabling self-adaptation, self-optimisa... more We propose a framework that uses component redundancy for enabling self-adaptation, self-optimisation and self-healing capabilities in component-based enterprise software systems.
In many self-organising systems the ability to extract necessary resources from the external envi... more In many self-organising systems the ability to extract necessary resources from the external environment is essential to the system's growth and survival. Examples include the extraction of sunlight and nutrients in organic plants, of monetary income in business organisations and of mobile robots in swarm intelligence actions. When operating within competitive, ever-changing environments, such systems must distribute their internal assets wisely so as to improve and adapt their ability to extract available resources. As the system size increases, the asset-distribution process often gets organised around a multi-scale control topology. This topology may be static (fixed) or dynamic (enabling growth and structural adaptation) depending on the system's internal constraints and adaptive mechanisms. In this paper, we expand on a plant-inspired asset-distribution model and introduce a more general multi-scale model applicable across a wider range of natural and artificial system domains. We study the impact that the topology of the multi-scale control process has upon the system's ability to self-adapt asset distribution when resource availability changes within the environment. Results show how different topological characteristics and different competition levels between system branches impact overall system profitability, adaptation delays and disturbances when environmental changes occur. These findings provide a basis for system designers to select the most suitable topology and configuration for their particular application and execution environment.
In many self-organising systems the ability to extract necessary resources from the external envi... more In many self-organising systems the ability to extract necessary resources from the external environment is essential for growth and survival. E.g., extracting sunlight and nutrients in organic plants, monetary income in business organisations and mobile robots in intelligent swarms. When operating within competitive, changing environments, such systems must distribute their assets wisely, to improve and adapt their ability to extract available resources. As the system size increases, the assetdistribution process often gets organised around a multi-scale control topology. This topology may be static (fixed) or dynamic (enabling growth and structural adaptation) depending on the system's constraints and adaptive mechanisms. In this paper we expand on a plant-inspired asset-distribution model and study the impact that the topology of the multi-scale control process has upon the system's ability to self-adapt asset distribution when resource availability changes within the environment. Results show how different topological characteristics and different competition levels between system branches impact overall system profitability, adaptation delays and disturbances when environmental changes occur. These findings provide a basis for system designers to select the most suitable topology and configuration for their particular application and execution environment.
In this paper, we consider a hybrid vehicular network, in which vehicles transmit data via the ce... more In this paper, we consider a hybrid vehicular network, in which vehicles transmit data via the cellular network and dispose of a Vehicle-to-Vehicle (V2V) interface. In this context, we propose an auto-adaptive multi-hop clustering algorithm, which optimizes the usage of the cellular radio resource under the constraint of a maximum packet loss rate (PLR) in the V2V network. The larger the V2V-based clusters are, the higher the data compression ratio at the cluster head is, and the smaller the amount of required resource on the cellular link becomes. However, PLR becomes higher due to the collisions on the V2V channel when increasing the number of hops for cluster enlargement. The proposed algorithm thus dynamically adapts the maximum number of hops in clusters according to the vehicular traffic density. Through simulations, we show that it performs better in terms of aggregated cellular data and packet loss rate than any fixed-hop clustering algorithm in a dynamic scenario.
Designing and organising large numbers of autonomic resources into a coherent system is a difficu... more Designing and organising large numbers of autonomic resources into a coherent system is a difficult endeavour. It necessitates handling complex interactions among dynamic, heterogeneous components, autonomic managers and human policies. Several architectural models have been proposed for organising these interactions. This paper focuses on a decentralised approach, while also considering two other possibilities-centralised and hierarchical. An architectural model is proposed and a prototype implementation with corresponding experimental results are subsequently presented and discussed.
The architecture of coordination mechanisms is central to the performance and behaviour of (self-... more The architecture of coordination mechanisms is central to the performance and behaviour of (self-)integrated systems across natural, socio-technical and cyber-physical domains. Multi-scale coordination schemes are prevalent in large-scale systems with bounded performance requirements and limited resource constraints. However, theories to formalise how coordination can be implemented across multi-scale systems are often domain-specific, lacking generic, reusable principles. In these systems, feedback among system entities is a key component to coordination. Building on theories of hierarchies and complexity, in previous work we formalised Multi-Scale Abstraction Feedbacks (MSAF) as a design pattern to describe the architecture of feedback across system scales, highlighting the role played by micro-entities and macro-entities, as well as their interconnections. Focusing on exogenous coordination, this paper refines the MSAF pattern, describing a feedback cycle across scales as one where information flows bottomup and top-down through five actions: state information communication, state information abstraction, information processing, control information communication, and adaptation from control information. Abstracted state information at each scale is processed with control input from the scale above and provides control input to the scale below. Using the example of distributed task allocation through exogenous coordination, NetLogo simulations are implemented to analyse the impact that different exogenous coordination strategies, and their internal timing configurations, have on resource consumption and on convergence performance. The experimental insights and refinement of the MSAF pattern contribute to a general theory of multi-scale feedback and adaptation. This architectural pattern and associated analysis and evaluation tools are still developing, but offer a concrete basis for further expansion, improvement, and implementation, while addressing questions that are at the core of the behaviour of multi-scale systems. Highlights • Multi-scale self-* systems are described in terms of information flows that form multi-scale feedback cycles and comprise several entity types-generically identified via a Multi-Scale Abstraction Feedbacks (MSAF) design pattern; • Feedback cycles are defined via five actions: state information collection, state information abstraction, information processing, control information communication, and adaptation from control information. They are interconnected via two further actions: inter-cycle information abstraction and inter-cycle communication of control information; • Concrete implementations of multi-scale feedback strategies with exogenous designs are provided and evaluated, via a generic method, in terms of convergence behaviour, resource use, and timing; • Results obtained from simulations run in NetLogo highlight the inherent trade-offs that these strategies feature between coordination convergence time and resource usage, for various system topology configurations, as well as different timing configurations; 1
Knowledge Management for Democratic Governance of Socio-Technical Systems (Chapter in The Future of Digital Democracy. Lecture Notes in Computer Science.)
We propose a framework that uses component redundancy for enabling self-adaptation, self-optimisa... more We propose a framework that uses component redundancy for enabling self-adaptation, self-optimisation and self-healing capabilities in component-based enterprise software systems.
Multi - Level Online Learning and Reasoning for Self-Integrating Systems
Self-improving and self-integrating systems (SISSY) often employ runtime models to represent thei... more Self-improving and self-integrating systems (SISSY) often employ runtime models to represent their state and environment, and reason upon them to determine the required adaptation logic for reaching their goals. However, most model-based approaches rely on static modeling languages and cannot handle runtime uncertainty (e.g. dynamically integrated resources) that requires online language extensions. In previous work, we proposed an approach to extend the system's modeling language with new monitoring and action dimensions. However, the solution generates a high number of new language elements, slowing down the reasoning process for large systems. In this position paper, we propose a multi-level approach for extending the modeling language at runtime, and aim to provide online learning and reasoning at multiple levels of abstraction. Increasing the modeling abstraction decreases the number of concepts to reason about, hence improving scalability. We provide a preliminary validation of this proposal by detecting novel abstract dimensions from monitoring data from the smart home domain.
Information and communication technology (ICT) pervades every aspect of our daily lives. This inc... more Information and communication technology (ICT) pervades every aspect of our daily lives. This inclusion changes our communities and all of our human interactions. It also presents a significant set of challenges in correctly designing and integrating our resulting technical systems. For instance, the embedding of ICT functionality in more and more devices (such as household appliances or thermostats) leads to novel interconnections and a changing structure of the overall system. Not only technical systems are increasingly coupled, a variety of previously isolated natural and human systems have consolidated into a kind of overall system of systems-an interwoven system structure. This change of structure is fundamental and affects the whole production cycle of technical systems standard system integration and testing is not feasible any more. The increasingly complex challenges of developing the right type of modelling, analysis, and infrastructure for designing and maintaining ICT infrastructures has continued to motivate the self-organising, autonomic and organic computing systems community. In this workshop, we intend to study novel approaches to system of system integration and testing by applying self-*principles; specifically we want approaches that allow for a continual process of self-integration among components and systems that is self-improving and evolving over time towards an optimised and stable solution. The fifth edition of SISSY again offers an attractive and varied programme, featuring original research contributions along with invited talks and a closing discussion. The workshop is held as part of the FAS* conference alliance in conjunction with the
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Papers by Ada Diaconescu