Dependency-Aware Service Migration for Backhaul-Free Vehicular Edge Computing Networks
Qiu-Xia Fan, Li Chen, Changsheng You, Yunfei Chen, Huarui Yin
Abstract
Vehicular edge computing (VEC) is a promising paradigm to improve vehicular services through offloading complex computation tasks to the edge servers. However, the high mobility of vehicles requires frequent service migration among edge servers to guarantee uninterrupted services when vehicles traverse multiple cells. This brings great challenges. In this paper, we design a dependency-aware backhaul-free migration scheme to enable service migration without relying on backhaul with constraints on task dependencies. Specifically, the vehicle proactively fetches the migrated results based on task dependencies from the original server and migrates the results to its dynamically connected servers along the traveling path. Considering the incurred intermittent communication and computation due to vehicle mobility, a joint offloading and migration optimization problem for determining the time to offload tasks and fetch results is formulated with a time-varying Markov decision process (MDP) to minimize the total energy consumption. Time-varying transition probability functions are derived to characterize the dynamics during intermittent offloading and fetching. Based on the MDP framework, an efficient online value iteration algorithm is developed by exploiting temporal correlation to estimate the time-varying value functions. Simulation results demonstrate that our proposed algorithm can achieve superior energy-saving performance compared to the baseline online schemes.