Planning And leaRning For AI-Edge compuTing (PARFAIT) – PARFAIT

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 and service composition will routinely include such applications and their microservice components. PARFAIT tackles new resource allocation problems emerging due to the need of distributed edge orchestration of both computing and communication, in a context where the unknown footprint of AI-based applications requires advanced learning capabilities 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-based processing tasks. Algorithmic solutions will be developed based on the theory of constrained, delayed, and distributed Markov decision processes 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 necessary to match the application quest for data proximity and the synchronization problems that arise 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 to demonstrate real-world solutions for practical orchestration use cases, using both large scale simulation experiments and research testbeds.