|Proposal Submission Deadline:||April 18, 2014 (passed)|
|Presenter Notification:||April 25, 2014 (passed)|
|Extended Abstract and Presentation Handouts due:||August 1, 2014|
|Tutorial presentation:||September 8 & 12, 2014|
The study of complex networks and collective dynamics occurring in biological, social and technical systems has experienced a massive surge of interest both from academia and industry. Many of the results on the mechanisms underlying the self-organized formation of complex dynamic networks in natural and man-made systems have been derived based on a statistical physics perspective. In this tutorial, we provide a basic introduction to this perspective which will help attendees to benefit from the vast literature on self- organization and self-adaptation phenomena available in the fields of network science and complex systems. We cover basic models and abstractions for the study of static complex networks as well as dynamical processes like, e.g., information diffusion, random walks, synchronization or the propagation of cascading failures. We further introduce recent advances in the study of dynamic (social) networks and demonstrate how the resulting methods can be practically applied in the engineering of self-organizing and self-adaptive distributed systems and protocols.
In recent years, a number of different strands of research on self-organizing systems have come together to create a new “aggregate programming” approach to the engineering of distributed systems. Aggregate programming is motivated by a desire to avoid the notoriously intractable “local to global” problem, where the system designer must predict how to control individual devices to achieve a collective goal. Instead, the designer programs an abstraction of the collective, composing “building block” primitives from a library of special cases where the local-to-global problem is already solved. Unifying a number of the proposed aggregate programming approaches is the notion of a “computational field” that maps each device in the field’s domain to a local value in its range. This concept was originally developed for spatial computers, in which communication and geometric position are closely linked, but can support effective aggregate programming of many non-spatial networks as well. A mathematical foundation for such approaches has been formalized recently with a minimal “field calculus” that appears to be an effective unifying model, covering a wide range of aggregate programming models, both continuous (e.g., geometry-based) and discrete (e.g., graph-based). On this foundation, restricted languages can ensure various desirable properties such as scalability, self-stabilization, and robustness to perturbation. By building up a sufficiently broad collection of composable “building block” distributed algorithms, it is possible to enable simple and rapid development of complex distributed systems that are implicitly scalable and resilient. The ultimate aim of this line of research is to make the programming of robust distributed systems as simple and widespread as single-processor programming, thereby enabling widespread increases in the reliability, efficiency, and democracy of our technological infrastructure.
The focus of this tutorial is to provide a synopsis of self‐managing computing also known as Autonomic Computing. In doing so, we will introduce the techniques that enable computer systems to manage themselves so as to minimise the need for human input. This will also discuss how self‐managing systems can address some of the issues resulting from the ever‐increasing complexity of software administration and the growing difficulty encountered by software administrators in performing their job effectively.