Description of the project

New generations of mobile access networks promise low delay and high-speed throughput data connections paired with in-network processing capabilities. IoT data and local information available to users’ devices will feed AI based applications executed in proximity on edge servers. PARFAIT tackles new resource allocation problems emerging due to the need of distributed edge orchestration of both computing and communication, while service composition will routinely include AI based applications and their microservice components.
The unknown footprint of modern AI based applications will require advanced learning capabilities in order to permit efficient and reliable edge service orchestration.

The PARFAIT project develops theoretical foundations for distributed and scalable resource allocation schemes on edge computing infrastructures tailored for AI-intensive processing tasks. Algorithmic solutions will be developed based on the theory of constrained, delayed, and distributed Markov decision processes in order to account for edge service orchestration actions and quantify the effect of orchestration policies.

Furthermore, using both game and team formulations, the project will pave the way for a theory of decentralized orchestration, a missing building block in order to connect the application quest for data proximity and synchronization problems arising when multiple edge orchestrators cooperate under local or partial system view.

Finally, to achieve efficient online edge service orchestration, such solutions will be empowered with reinforcement learning techniques to define a suit of orchestration algorithms able to at once adapt over time to the applications’ load and cope with the uncertain information available from AI based applications’ footprints.
Validation activities will be designed in order to demonstrate real-world solutions for practical orchestration use cases, using both large scale simulation experiments and research testbeds.