An Adaptive Deep Q-learning Service Migration Decision Framework for Connected Vehicles

Wang, Chenglong; Peng, Jun; Fu, Jiang; Zhang, Xiaoyong; Liu, Weirong; Gu, Xin; Huang, Zhiwu

doi:10.1109/smc42975.2020.9283380

Cited by 7 publications

(1 citation statement)

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“…2) Task Migration in Deep Learning Mode: In [33], the authors propose a multi-agent deep reinforcement learning algorithm that maximizes the comprehensive utility of communication, computing and routing planning in a distributed manner, thus reducing service delay, migration cost and travel time. The authors in [34] proposed a deep Q-learning service migration decision algorithm and a neural networkbased service migration framework that realizes the adaptive migration of task offloading when connected vehicles move. To minimize the data processing cost within the system and ensure the delay constraints of applications, the author in [35] formulates a unified communication, computing, caching and collaborative computing framework and develops a cooperative data scheduling scheme to model the data scheduling as a deep reinforcement learning problem which is solved by an enhanced deep Q-Network algorithm with a single target Qnetwork.…”

Section: Task Migrationmentioning

confidence: 99%

Energy-Delay Minimization of Task Migration Based on Game Theory in MEC-assisted Vehicular Networks

Wang,

Lv,

Lin

et al. 2022

Preprint

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Roadside units (RSUs), which have strong computing capability and are close to vehicle nodes, have been widely used to process delay-and computation-intensive tasks of vehicle nodes. However, due to their high mobility, vehicles may drive out of the coverage of RSUs before receiving the task processing results. In this paper, we propose a mobile edge computing-assisted vehicular network, where vehicles can offload their tasks to a nearby vehicle via a vehicle-to-vehicle (V2V) link or a nearby RSU via a vehicle-to-infrastructure link. These tasks are also migrated by a V2V link or an infrastructureto-infrastructure (I2I) link to avoid the scenario where the vehicles cannot receive the processed task from the RSUs. Considering mutual interference from the same link of offloading tasks and migrating tasks, we construct a vehicle offloading decision-based game to minimize the computation overhead. We prove that the game can always achieve Nash equilibrium and convergence by exploiting the finite improvement property. We then propose a task migration (TM) algorithm that includes three task-processing methods and two task-migration methods. Based on the TM algorithm, computation overhead minimization offloading (COMO) algorithm is presented. Extensive simulation results show that the proposed TM and COMO algorithms reduce the computation overhead and increase the success rate of task processing.

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Section: Task Migrationmentioning

confidence: 99%