MANTRA: A Distributed MAB-Based Multi-Agent Framework for Latency- and Energy-Aware Offloading in Vehicular Networks

Job offloading represents one powerful key-enabler for delay-sensitive applications in Intelligent Transport Systems (ITS). Vehicles can rely on job offloading to move their jobs to the Road Side Units (RSUs) devices, which are typically equipped with better computing power. Ultimately, this procedure can help relieve the computational burden on the vehicles, to the benefit of the overall processing latency. However, fixed vehicular infrastructure cannot guarantee ubiquitous, ever-present support for job offloading, especially in the case of hard-to-reach, remote areas where the power grid and/or connectivity are not present. One way to solve this issue is to resort to portable, batterypowered RSUs, which can be installed potentially everywhere and allow for greater flexibility. However, this calls for a management framework that strikes the trade-off between RSU battery-life on one hand, and processing latency on the other. Moreover, this framework should be able to work in a fully-distributed way, without the need of an existing infrastructure. With all of this in mind, our contribution is two-fold. First, we introduce the idea of MEC-in-a-box (M-Box) stations, batterypowered RSUs that can operate even in the absence of a fixed infrastructure or connectivity. Second, in order to support the operation of M-Box stations, we design MANTRA, an offloading framework for vehicular networks that balances energy consumption and processing latency in the M-Box stations. MANTRA allows the M-Box stations to autonomously i) turn on and off their processing units to alternatively increase their processing power or save energy, and ii) manage the amount of jobs offloaded towards the other M-Box stations to balance the network load. We evaluated the performance of MANTRA vs other approaches, including a centralized solution with a full knowledge of the system. First, the results highlight how MANTRA is able to match the performance of the centralized approach, and is also capable of surpassing all the other baselines in various experimental scenarios.