Biblio

Modern software should satisfy multiple goals simultaneously: it should provide predictable performance, be robust to failures, handle peak loads and deal seamlessly with unexpected conditions and changes in the execution environment. For this to happen, software designs should account for the possibility of runtime changes and provide formal guarantees of the software's behavior. Control theory is one of the possible design drivers for runtime adaptation, but adopting control theoretic principles often requires additional, specialized knowledge. To overcome this limitation, automated methodologies have been proposed to extract the necessary information from experimental data and design a control system for runtime adaptation. These proposals, however, only process one goal at a time, creating a chain of controllers. In this paper, we propose and evaluate the first automated strategy that takes into account multiple goals without separating them into multiple control strategies. Avoiding the separation allows us to tackle a larger class of problems and provide stronger guarantees. We test our methodology's generality with three case studies that demonstrate its broad applicability in meeting performance, reliability, quality, security, and energy goals despite environmental or requirements changes.

The IoT will host a large number of co-existing cyber-physical applications. Continuous change, application interference, environment dynamics and uncertainty lead to complex effects which must be controlled to give performance and application guarantees. Application and platform self-configuration and self-awareness are one paradigm to approach this challenge. They can leverage context knowledge to control platform and application functions and their interaction. They could play a dominant role in large scale cyber-physical systems and systems-of-systems, simply because no person can oversee the whole system functionality and dynamics. IoT adds a new dimension because Internet based services will increasingly be used in such system functions. Autonomous vehicles accessing cloud services for efficiency and comfort as well as to reach the required level of safety and security are an example. Such vehicle platforms will communicate with a service infrastructure that must be reliable and highly responsive. Automated continuous self-configuration of data storage might be a good basis for such services up to the point where the different self-x strategies might affect each other, in a positive or negative form. This paper contains three contributions from different domains representing the current status of self-aware systems as they will meet in the Internet-of-Things and closes with a short discussion of upcoming challenges.