Folo: Latency and Quality Optimized Task Allocation in Vehicular Fog Computing
No Thumbnail Available
Access rights
openAccess
URL
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Date
2019-06
Major/Subject
Mcode
Degree programme
Language
en
Pages
12
Series
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 non-deterministic polynomial-time hard (NP-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 (DTA) framework using Linear Programming based Optimization (LBO) and Binary Particle Swarm Optimization (BPSO). 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 be more specific, Folo shortens the average service latency by up to 27% while reducing the quality loss by up to 56%.Description
| openaire: EC/H2020/815191/EU//PriMO-5G
Keywords
Computing Offloading, Vehicular Fog Computing (VFC), Dynamic Task Allocation, Linear programming (LP), Binary Particle Swarm Optimization (BPSO)
Other note
Citation
Zhu, C, Tao, J, Pastor Figueroa, G, Xiao, Y, Ji, Y, Zhou, Q, Li, Y & Ylä-Jääski, A 2019, ' Folo: Latency and Quality Optimized Task Allocation in Vehicular Fog Computing ', IEEE Internet of Things Journal, vol. 6, no. 3, pp. 4150 - 4161 . https://doi.org/10.1109/JIOT.2018.2875520