AVE: Autonomous Vehicular Edge Computing Framework with ACO-Based Scheduling

Jiang, Feng; Liu, Zhi; Wu, Celimuge; Ji, Yusheng

doi:10.1109/tvt.2017.2714704

Cited by 300 publications

(141 citation statements)

References 32 publications

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“…An alternative method is to make task offloading decisions by the task generators in a distributed manner. An autonomous vehicular edge framework which enables V-V and V-I offloading is proposed in [23], followed by a task scheduling algorithm based on ant colony optimization. However, when the number of vehicles is large, the computational complexity can be quite high.…”

Section: B Task Offloading Algorithmsmentioning

confidence: 99%

Adaptive Learning-Based Task Offloading for Vehicular Edge Computing Systems

Sun

Guo

Song

et al. 2019

IEEE Trans. Veh. Technol.

304

119

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The vehicular edge computing (VEC) system integrates the computing resources of vehicles, and provides computing services for other vehicles and pedestrians with task offloading. However, the vehicular task offloading environment is dynamic and uncertain, with fast varying network topologies, wireless channel states and computing workloads. These uncertainties bring extra challenges to task offloading. In this work, we consider the task offloading among vehicles, and propose a solution that enables vehicles to learn the offloading delay performance of their neighboring vehicles while offloading computation tasks. We design an adaptive learning-based task offloading (ALTO) algorithm based on the multi-armed bandit (MAB) theory, in order to minimize the average offloading delay. ALTO works in a distributed manner without requiring frequent state exchange, and is augmented with input-awareness and occurrence-awareness to adapt to the dynamic environment. The proposed algorithm is proved to have a sublinear learning regret. Extensive simulations are carried out under both synthetic scenario and realistic highway scenario, and results illustrate that the proposed algorithm achieves low delay performance, and decreases the average delay up to 30% compared with the existing upper confidence bound based learning algorithm.Index Terms-Vehicular edge computing, task offloading, online learning, multi-armed bandit.

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Section: B Task Offloading Algorithmsmentioning

confidence: 99%

Adaptive Learning-Based Task Offloading for Vehicular Edge Computing Systems

Sun

Guo

Song

et al. 2019

IEEE Trans. Veh. Technol.

304

119

View full text Add to dashboard Cite

show abstract

“…Task offloading decisions can also be made by each client node independently in a distributed manner, corresponding to the vehicle-vehicle offloading mode. Feng et al [12] proposed a distributed VeFN architecture, where RSUs and vehicles can offload tasks to their neighboring nodes based on their distributed decisions. They designed a task offloading algorithm based on ant colony optimization, in order to maximize the sum utility of offloaded tasks related to delay, and evaluate it in a system level simulator using real traces.…”

Section: B State-of-the-art On Computing In Vehicular Networkmentioning

confidence: 99%

“…They designed a task offloading algorithm based on ant colony optimization, in order to maximize the sum utility of offloaded tasks related to delay, and evaluate it in a system level simulator using real traces. However, the RSUs and vehicles are treated equally in [12], while in real systems, RSUs often know more about the network conditions, which should be more effectively exploited.…”

Section: B State-of-the-art On Computing In Vehicular Networkmentioning

confidence: 99%

Exploiting Moving Intelligence: Delay-Optimized Computation Offloading in Vehicular Fog Networks

et al. 2019

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Future vehicles will have rich computing resources to support autonomous driving and be connected by wireless technologies. Vehicular fog networks (VeFN) have thus emerged to enable computing resource sharing via computation task offloading, providing wide range of fog applications. However, the high mobility of vehicles makes it hard to guarantee the delay that accounts for both communication and computation throughout the whole task offloading procedure. In this article, we first review the state-of-the-art of task offloading in VeFN, and argue that mobility is not only an obstacle for timely computing in VeFN, but can also benefit the delay performance. We then identify machine learning and coded computing as key enabling technologies to address and exploit mobility in VeFN. Case studies are provided to illustrate how to adapt learning algorithms to fit for the dynamic environment in VeFN, and how to exploit the mobility with opportunistic computation offloading and task replication.

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“…However, the centralized control requires large signaling overheads for vehicular states update, and the proposed algorithm has high complexity. A distributed task offloading algorithm is proposed in [14] based on ant colony optimization, which is of much lower complexity. However, it still requires exchanges of vehicular states.…”

Section: Introductionmentioning

confidence: 99%

Learning-Based Task Offloading for Vehicular Cloud Computing Systems

Sun

Guo

Zhou

et al. 2018

2018 IEEE International Conference on Communications (ICC)

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Vehicular cloud computing (VCC) is proposed to effectively utilize and share the computing and storage resources on vehicles. However, due to the mobility of vehicles, the network topology, the wireless channel states and the available computing resources vary rapidly and are difficult to predict. In this work, we develop a learning-based task offloading framework using the multi-armed bandit (MAB) theory, which enables vehicles to learn the potential task offloading performance of its neighboring vehicles with excessive computing resources, namely service vehicles (SeVs), and minimizes the average offloading delay. We propose an adaptive volatile upper confidence bound (AVUCB) algorithm and augment it with load-awareness and occurrenceawareness, by redesigning the utility function of the classic MAB algorithms. The proposed AVUCB algorithm can effectively adapt to the dynamic vehicular environment, balance the tradeoff between exploration and exploitation in the learning process, and converge fast to the optimal SeV with theoretical performance guarantee. Simulations under both synthetic scenario and a realistic highway scenario are carried out, showing that the proposed algorithm achieves close-to-optimal delay performance.

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AVE: Autonomous Vehicular Edge Computing Framework with ACO-Based Scheduling

Cited by 300 publications

References 32 publications

Adaptive Learning-Based Task Offloading for Vehicular Edge Computing Systems

Adaptive Learning-Based Task Offloading for Vehicular Edge Computing Systems

Exploiting Moving Intelligence: Delay-Optimized Computation Offloading in Vehicular Fog Networks

Learning-Based Task Offloading for Vehicular Cloud Computing Systems

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