The objective of this subproject is to develop tractable, data-driven models of end-to-end system performance.
In this subproject we are interested in fundamental questions, such as how different wireless transmission technologies translate into different latency and reliability profiles, how corresponding computing technologies and abstractions (operating systems, virtualization, containers) can be made predictable, and how the concatenation of communication and compute functions translates into stochastic latency and reliability profiles.
These profiles serve as input to optimization where compute resources can be leveraged to compensate for communication deficiencies, and vice versa. We will approach these steps through performance modeling and optimization theory on the one hand, and we envision to approach the parameterization of fundamental models through machine learning. We will investigate how theoretical results can translate into practically relevant results, and how this depends on the considered systems and environments.
Areas within Predictability:
