Optimal Sampling for Interactive Networked Applications

Over the last decade, academia and industry of networked systems have become more and more interested in novel real-time applications.

These applications arise for one in the area of Cyber-Physical Systems (CPS) where essentially time-sensitive processes are to be governed by direct actuation.

On the other hand, these applications also arise in the context of providing automated feedback to human users, for instance, in augmented reality as well as cognitive assistance.

Such interactive applications are very powerful with respect to their future implications for professional education, ambient intelligence as well as leisure. It is therefore likely that they will have a profound impact on networked systems.

Nevertheless, from a fundamental perspective, we have very little understanding of the efficient operations of networked systems for such interactive applications today.

The goal of this project is to provide fundamental performance models for these interactive applications and the operation of underlying networked systems.

In contrast to state-of-the-art, our key approach is to capture the essential trade-offs through a novel notion of utility of the received information over time, and subsequently to strive for system optimizations. Central to our application are novel sampling policies, which we derive by leveraging Markov Decision Processes.

By this, we aim at providing a cornerstone for the design of future networked systems exposed to interactive applications.