1st International Workshop on „Self-optimisation in Autonomic and Organic Computing Systems" (SAOS13)

Initiatives like Autonomic Computing (AC) and Organic Computing (OC) are based on the insight that we are increasingly surrounded by large collections of autonomous systems, which are equipped with sensors and actuators, aware of their environment, communicating freely, and organising themselves in order to perform the required actions and services. The presence of networks of intelligent systems in our environment opens fascinating application areas but, at the same time, bears the problem of their controllability.

Hence, different design concepts (like the MAPE cycle and the Observer/Controller framework) have been developed to allow for a self-organised control process at runtime that relieves the designer from specifying all possibly occurring situations and configurations within the design process. Instead, the system itself takes over responsibility to find proper reactions on perceived changes in the environmental conditions. As designers are not able to foresee all possibly occurring situations and circumstances the system will face during its operation time the self-organisation process of the system will focus on self-optimising the system’s behaviour. Such self-optimising behaviour can be achieved at various levels of the system’s design, ranging from basic control architectures over self-organised coordination or collaboration methods and domain-specific optimisation techniques to the application and customisation of machine learning algorithms. Furthermore, several related topics (e.g. trust and security in collaborative systems) provide necessary functionality to enable self-optimising behaviour in AC and OC systems.

A special session will further address the question how methods, abstractions and ideas from the (statistical) physics perspective on complex adaptive systems – with examples coming from nature, society and technology – can be utilised in the design, modelling and analysis of organic and autonomic computing systems. Special emphasis will be laid on how the recently developed statistical mechanics of networks – encompassing complex and dynamic structures – can facilitate the design of robust and adaptive computing architectures that inherit some of the remarkable properties of natural systems. An important aim is to strengthen the ties between complementary research communities that otherwise rarely get in contact.


Part A: Architectural concepts for self-optimising behaviour


Part B: Algorithms and methods for self-optimisation  


Part C: Applications for self-optimisation

Applications with self-optimising system behaviour, i.e. from the following domains:


Part D: SPECIAL SESSION on “Complex Sciences in the Engineering of Computing Systems


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