QoS Violation Probability Minimization in Federating Vehicular-Fogs With Cloud and Edge Systems
Binayak Kar, Kuan-Min Shieh, Yuan‐Cheng Lai, Ying–Dar Lin, Huei-Wen Ferng
Abstract
Due to the fact that most user equipments (UEs) do not have enough computation power, time-sensitive tasks should be offloaded to other computation resources. In such cases, a cloud is an ideal destination to which computation tasks can be offloaded because of its huge data processing power. However, the distance between a UE and the cloud may increase the latency of data transmission. At the same time, edges and vehicular-fogs (consisting of electric vehicles with computing resources) are closer to UEs and mostly remain under-utilized. To address this issue, a federated architecture with UEs, vehicular-fogs (parking lot fog and traffic intersection fog), edges, and cloud is proposed along with a probabilistic offloading strategy. The quality of service (QoS) violation probability can be minimized by finding the optimal offloading probabilities while satisfying the delay constraint through an optimal offloading probability estimation algorithm based on the subgradient method proposed in this paper. Our results show that the QoS violation probability and the average waiting time can be decreased by 10%-12% and 45%, respectively, for a federated architecture with two vehicular-fogs as compared to an architecture without any fogs. As offloading to edges and cloud has longer communication delays than offloading to vehicular-fogs, the offloading probabilities to the edges in the architecture with two vehicular-fogs in the case of heavy traffic are reduced by nearly 35% as compared to the architecture without any fogs.