Data-driven Performance Modeling and Optimization

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.


Contacts

Project manager

Gyorgy Dan

György Dán
Edge computing, Resource management
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Co-project manager

Lei Feng

Lei Feng
Mechatronics, Supervisory ctrl, Optimization, Model-checking
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Co-project manager

Elena Fersman

Elena Fersman
Machine Intelligence, Telecom and distributed systems, R&D, Leadership and management

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Co-project manager

James Gross

James Gross
Wireless, Predictability, Edge computing
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