| Title | Folo: Latency and Quality Optimized Task Allocation in Vehicular Fog Computing |
| Authors | Zhu, Chao Tao, Jin Pastor, Giancarlo Xiao, Yu Ji, Yusheng Zhou, Quan Li, Yong Yla-Jaaski, Antti |
| Affiliation | Aalto Univ, Dept Commun & Networking, Espoo 00076, Finland Aalto Univ, Dept Comp Sci, Espoo 00076, Finland Aalto Univ, Dept Elect Engn & Automat, Espoo 00076, Finland Peking Univ, Coll Engn, Beijing 100871, Peoples R China Natl Inst Informat, Tokyo 1018430, Japan Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China |
| Keywords | Binary particle swarm optimization (BPSO) computation offloading dynamic task allocation (DTA) linear programming (LP) vehicular fog computing (VFC) |
| Issue Date | 2019 |
| Publisher | IEEE INTERNET OF THINGS JOURNAL |
| Abstract | With the emerging vehicular applications, such as real-time situational awareness and cooperative lane change, there exist huge demands for sufficient computing resources at the edge to conduct time-critical and data-intensive tasks. This paper proposes Folo, a novel solution for latency and quality optimized task allocation in vehicular fog computing (VFC). Folo is designed to support the mobility of vehicles, including vehicles that generate tasks and the others that serve as fog nodes. Considering constraints on service latency, quality loss, and fog capacity, the process of task allocation across stationary and mobile fog nodes is formulated into a joint optimization problem. This task allocation in VFC is known as a nondeterministic polynomial-time hard problem. In this paper, we present the task allocation to fog nodes as a bi-objective minimization problem, where a trade-off is maintained between the service latency and quality loss. Specifically, we propose an event-triggered dynamic task allocation framework using linear programming-based optimization and binary particle swarm optimization. To assess the effectiveness of Folo, we simulated the mobility of fog nodes at different times of a day based on real-world taxi traces and implemented two representative tasks, including video streaming and real-time object recognition. Simulation results show that the task allocation provided by Folo can be adjusted according to actual requirements of the service latency and quality, and achieves higher performance compared with naive and random fog node selection. To he more specific, Folo shortens the average service latency by up to 27% while reducing the quality loss by up to 56%. |
| URI | http://hdl.handle.net/20.500.11897/547723 |
| ISSN | 2327-4662 |
| DOI | 10.1109/JIOT.2018.2875520 |
| Indexed | SCI(E) EI |
| Appears in Collections: | 工学院 |
