Self-aware computing systems are understood in a broad sense seeking to integrate the different ways in which this term is used in the interdisciplinary research landscape. More specifically, self-aware computing systems are understood as having two main properties. They learn models, capturing knowledge about themselves and their environment (such as their structure, design, state, possible actions, and runtime behavior) on an ongoing basis; and reason using the models (to predict, analyze, consider, or plan), which enables them to act based on their knowledge and reasoning (for example, to explore, explain, report, suggest, self-adapt, or impact their environment) and do so in accordance with high-level goals, which can change.
In order to most effectively use such models at runtime, self-aware computing systems need increasingly powerful ways of observing their operational environment and their own performance and behavior and then building and refining their own models accordingly. An inherent principle of self-aware computing systems is having diverse feedback loops, which build a causal connection between the computing system and a reflective layer. The computing system is continuously observed and, based on this, the system is able to update and modify its models to reason about its goals, context, operational environment and its own resources, decisions and actions.
To effectively and efficiently realize these feedback loops, models and especially modifiable and updatable models@runtime are essential. The firstname.lastname@example.org paradigm proposes to use runtime models as abstractions of the computing system for the purpose of more efficient reasoning upon both its runtime observations and learned knowledge. Hence, email@example.com is especially looking for more innovative approaches to the causal connection between the system and the runtime model, with particular focus on a transaction concept for this causal connection for such issues as timing, roll-back ability and data-consistency.
The goal of this joint workshop is to provide a bridging podium for researchers working in the area of self-awareness, self-modelling, autonomous and organic computing, as well as self-adaptive and self-organizing systems with a focus on runtime representations that can be used by the system to reason about its goals, context, operational environment and its own resources, decisions and actions.
|Submissions:||June 25, 2018|
|Notification:||July 9, 2018|
|Event:||September 3, 2018-September 3, 2018|