Task Offloading Strategy Based on Reinforcement Learning Computing in Edge Computing Architecture of Internet of Vehicles

Wang, Kun; Wang, Xiaofeng; Li, Xuan; Jolfaei, Alireza

doi:10.1109/access.2020.3023939

Cited by 48 publications

(29 citation statements)

References 36 publications

(45 reference statements)

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“…A cellular MEC system supporting D2D communication consisting of a single base station with a MEC server deployed and multiple users is considered, as shown in Fig 3 . Assuming that some users in the system need to perform computationally intensive tasks tolerated by the time delay, the MEC server deployed on the base station side can provide task offloading services for the server [ 17 ]. Therefore, in addition to perform the task locally, the server can also offload the task to the MEC server through the cellular link for execution.…”

Section: Methodsmentioning

confidence: 99%

Deployment optimization of multi-stage investment portfolio service and hybrid intelligent algorithm under edge computing

Zhang

Shen

2021

PLoS ONE

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The purposes are to improve the server deployment capability under Mobile Edge Computing (MEC), reduce the time delay and energy consumption of terminals during task execution, and improve user service quality. After the server deployment problems under traditional edge computing are analyzed and researched, a task resource allocation model based on multi-stage is proposed to solve the communication problem between different supporting devices. This model establishes a combined task resource allocation and task offloading method and optimizes server execution by utilizing the time delay and energy consumption required for task execution and comprehensively considering the restriction processes of task offloading, partition, and transmission. For the MEC process that supports dense networks, a multi-hybrid intelligent algorithm based on energy consumption optimization is proposed. The algorithm converts the original problem into a power allocation problem via a heuristic model. Simultaneously, it determines the appropriate allocation strategy through distributed planning, duality, and upper bound replacement. Results demonstrate that the proposed multi-stage combination-based service deployment optimization model can solve the problem of minimizing the maximum task execution energy consumption combined with task offloading and resource allocation effectively. The algorithm has good performance in handling user fairness and the worst-case task execution energy consumption. The proposed hybrid intelligent algorithm can partition tasks into task offloading sub-problems and resource allocation sub-problems, meeting the user’s task execution needs. A comparison with the latest algorithm also verifies the model’s performance and effectiveness. The above results can provide a theoretical basis and some practical ideas for server deployment and applications under MEC.

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Section: Methodsmentioning

confidence: 99%

Deployment optimization of multi-stage investment portfolio service and hybrid intelligent algorithm under edge computing

Zhang

Shen

2021

PLoS ONE

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“…Reinforcement Learning (RL) is very suitable for solving decision-making problems, such as computational offloading decision [22]. The RL algorithm can create experience to learn and complete the optimization goal by a trial-return feedback mechanism that is different from traditional optimization algorithms.…”

Section: Methodsmentioning

confidence: 99%

Computing resource allocation scheme of IOV using deep reinforcement learning in edge computing environment

Zhang

Zhang²,

Fan

et al. 2021

EURASIP J. Adv. Signal Process.

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With the emergence and development of 5G technology, Mobile Edge Computing (MEC) has been closely integrated with Internet of Vehicles (IoV) technology, which can effectively support and improve network performance in IoV. However, the high-speed mobility of vehicles and diversity of communication quality make computing task offloading strategies more complex. To solve the problem, this paper proposes a computing resource allocation scheme based on deep reinforcement learning network for mobile edge computing scenarios in IoV. Firstly, the task resource allocation model for IoV in corresponding edge computing scenario is determined regarding the computing capacity of service nodes and vehicle moving speed as constraints. Besides, the mathematical model for task offloading and resource allocation is established with the minimum total computing cost as objective function. Then, deep Q-learning network based on deep reinforcement learning network is proposed to solve the mathematical model of resource allocation. Moreover, experience replay method is used to solve the instability of nonlinear approximate function neural network, which can avoid falling into dimension disaster and ensure the low-overhead and low-latency operation requirements of resource allocation. Finally, simulation results show that proposed scheme can effectively allocate the computing resources of IoV in edge computing environment. When the number of user uploaded data is 10K bits and the number of terminals is 15, it still shows the excellent network performance of low-overhead and low-latency.

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“…Other works are still focused on scheduling but targeting the energy consumption [11], [12], vehicular networks [13], [14], network resources allocation [15] or security [16].…”

Section: Related Workmentioning

confidence: 99%

Leveraging Reinforcement Learning for online scheduling of real-time tasks in the Edge/Fog-to-Cloud computing continuum

Mattia

Beraldi

2021

2021 IEEE 20th International Symposium on Network Computing and Applications (NCA)

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The computing continuum model is a widely accepted and used approach that make possible the existence of applications that are very demanding in terms of low latency and high computing power. In this three-layered model, the Fog or Edge layer can be considered as the weak link in the chain, indeed the computing nodes whose compose it are generally heterogeneous and their uptime cannot be compared with the one offered by the Cloud. Taking into account these inexorable characteristics of the continuum, in this paper, we propose a Reinforcement Learning based scheduling algorithm that makes per-job request decisions (online scheduling) and that is able to maintain an acceptable performance specifically targeting real-time applications. Through a series of simulations and comparisons with other fixed scheduling strategies, we demonstrate how the algorithm is capable of deriving the best possible scheduling policy when Fog or Edge nodes have different speeds and can unpredictably fail.

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Task Offloading Strategy Based on Reinforcement Learning Computing in Edge Computing Architecture of Internet of Vehicles

Cited by 48 publications

References 36 publications

Deployment optimization of multi-stage investment portfolio service and hybrid intelligent algorithm under edge computing

Deployment optimization of multi-stage investment portfolio service and hybrid intelligent algorithm under edge computing

Computing resource allocation scheme of IOV using deep reinforcement learning in edge computing environment

Leveraging Reinforcement Learning for online scheduling of real-time tasks in the Edge/Fog-to-Cloud computing continuum

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