The Feedback Computing Workshop (FCW) brings together researchers and practitioners working with adaptive, feedback-driven computer systems and software. The scope of the workshop includes modeling, analyzing, designing, optimizing and adapting computing systems with respect to metrics such as performance, predictability, availability, security, power consumption, and thermal concerns. Feedback can affect a wide range of computing systems including high-performance grids, cloud and web service infrastructures, mobile systems, Internet servers, SOCs, embedded systems, Internet of Things and sensor networks. FCW welcomes discussion on any type of feedback-driven systems. FCW recognizes the growing role of feedback in managing computer systems and is a timely response to the following two trends:

1. Computer systems are growing larger, smarter, and more complex. They are embedded in the physical world, human interactions, and societal infrastructure. Systematic and feedback-driven approaches can address the dynamic interactions between computer systems and the real world, especially in emerging fields such as cyber-physical systems, cloud computing, social networks, and mobile applications.

2. Advances in disciplines such as machine learning, mathemati­cal optimization, network theories, decision theories, and data engineering provide new foundations and techniques that empower feedback approaches to address computing systems at scale and to achieve goals such as autonomy, adaptation, stabilization, robustness, and performance optimization.

In 2016, FCW will feature invited talks from prolific researchers and practitioners working with feedback-driven systems. In addition, FCW will also feature research contribu­tions and position papers on advancing feedback control technolo­gies and their applications in computing systems, broadly defined. Topics of interest include but are not limited to:

- Theoretical foundations for feedback computing

- New control paradigms and system architecture

- Sensing, actuation, and data management in feedback computing

- Learning and modeling of computing system dynamics

- Design patterns and software engineering

- Experiences and best practices from real systems

- Studies with new or emerging types of feedback, e.g., Twitter analysis, approximate computing, crash reports, markets or user studies

- Applications in domains such as big data, cloud comput­ing, computer networks, cyber-physical systems, data center resource management, distributed systems, mobility, power management and sustainability, real-time systems, and social networks

We solicit research papers containing original research results and challenge papers motivating new research directions. In addition, the workshop will facilitate discussion and collaborative research among the participants.