Folo: Latency and Quality Optimized Task Allocation in Vehicular Fog Computing

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Folo: Latency and Quality Optimized Task Allocation in Vehicular Fog Computing. / Zhu, Chao; Tao, Jin; Pastor Figueroa, Giancarlo; Xiao, Yu; Ji, Yusheng; Zhou, Quan; Li, Yong; Ylä-Jääski, Antti.

In: IEEE Internet of Things Journal, Vol. 6, No. 3, 06.2019, p. 4150 - 4161.

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@article{79cdeb370ef94532812c63c568ddd48b,
title = "Folo: Latency and Quality Optimized Task Allocation in Vehicular Fog Computing",
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{\%}.",
keywords = "Computing Offloading, Vehicular Fog Computing (VFC), Dynamic Task Allocation, Linear programming (LP), Binary Particle Swarm Optimization (BPSO)",
author = "Chao Zhu and Jin Tao and {Pastor Figueroa}, Giancarlo and Yu Xiao and Yusheng Ji and Quan Zhou and Yong Li and Antti Yl{\"a}-J{\"a}{\"a}ski",
note = "| openaire: EC/H2020/815191/EU//PriMO-5G",
year = "2019",
month = "6",
doi = "10.1109/JIOT.2018.2875520",
language = "English",
volume = "6",
pages = "4150 -- 4161",
journal = "IEEE Internet of Things Journal",
issn = "2327-4662",
publisher = "Institute of Electrical and Electronics Engineers",
number = "3",

}

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TY - JOUR

T1 - Folo: Latency and Quality Optimized Task Allocation in Vehicular Fog Computing

AU - Zhu, Chao

AU - Tao, Jin

AU - Pastor Figueroa, Giancarlo

AU - Xiao, Yu

AU - Ji, Yusheng

AU - Zhou, Quan

AU - Li, Yong

AU - Ylä-Jääski, Antti

N1 - | openaire: EC/H2020/815191/EU//PriMO-5G

PY - 2019/6

Y1 - 2019/6

N2 - 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%.

AB - 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%.

KW - Computing Offloading

KW - Vehicular Fog Computing (VFC)

KW - Dynamic Task Allocation

KW - Linear programming (LP)

KW - Binary Particle Swarm Optimization (BPSO)

U2 - 10.1109/JIOT.2018.2875520

DO - 10.1109/JIOT.2018.2875520

M3 - Article

VL - 6

SP - 4150

EP - 4161

JO - IEEE Internet of Things Journal

JF - IEEE Internet of Things Journal

SN - 2327-4662

IS - 3

ER -

ID: 28656240