0000000000435322
AUTHOR
Giovanni Schembra
Designing a multi-layer edge-computing platform for energy-efficient and delay-aware offloading in vehicular networks
Abstract Vehicular networks are expected to support many time-critical services requiring huge amounts of computation resources with very low delay. However, such requirements may not be fully met by vehicle on-board devices due to their limited processing and storage capabilities. The solution provided by 5G is the application of the Multi-Access Edge Computing (MEC) paradigm, which represents a low-latency alternative to remote clouds. Accordingly, we envision a multi-layer job-offloading scheme based on three levels, i.e., the Vehicular Domain, the MEC Domain and Backhaul Network Domain. In such a view, jobs can be offloaded from the Vehicular Domain to the MEC Domain, and even further o…
Multi-Layer Offloading at the Edge for Vehicular Networks
This paper proposes a multi-layer platform for job offloading in vehicular networks. Offloading is performed from vehicles in the Vehicular Domain towards Multi-Access Edge Computing (MEC) Servers deployed at the edge of the network, and between MEC Servers. Offloading decisions at both domains are challenging for the overall system performance. Optimization at the MEC Layer domain is obtained by model-based Reinforcement Learning, while a strategy to decide the best offloading rate from the Vehicular Domain is defined to achieve the desired trade-off between costs and performance. Numerical analysis shows the achieved performance.