Joint Service Caching and Computation Offloading Scheme Based on Deep Reinforcement Learning in Vehicular Edge Computing Systems

Xue, Zheng; Liu, Chang; Liao, Canliang; Han, Guangjie; Sheng, Zhengguo

doi:10.1109/tvt.2023.3234336

Cited by 49 publications

(12 citation statements)

References 41 publications

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“…Considering vehicle mobility and network changes, [27] studied a joint optimization problem by integrating service caching and computation offloading in a general VEC scenario with time-varying task requests. To minimize the average task processing delay, long-term mixed integer nonlinear programming (MINLP) is used to formulate the problem, and an algorithm based on Deep Reinforcement Learning is proposed to obtain a suboptimal solution with low computational complexity.…”

Section: Related Workmentioning

confidence: 99%

An optimization scheme for vehicular edge computing based on Lyapunov function and deep reinforcement learning

Zhu,

Li,

Tan

2024

Preprint

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Traditional vehicular edge computing research usually ignores the mobility of vehicles, the dynamic variability of the vehicular edge environment, the large amount of real-time data required for vehicular edge computing, the limited resources of edge servers, and collaboration issues. In response to these challenges, this paper proposes an allocation and collaboration scheme of vehicle edge computing resources based on the Lyapunov function and Twin Delayed Deep Deterministic Policy Gradient (TD3). In this solution, this paper uses Digital Twin technology (DT) to simulate the vehicular edge environment. The edge server DT is used to simulate the vehicular edge environment under the edge server, and the base station DT is used to simulate the entire vehicular edge system environment. Based on the real-time data obtained from DT simulation, this paper defines the Lyapunov function to simplify the migration cost of vehicle tasks between servers into a multi-objective dynamic optimization problem. It solves the problem by applying the TD3 algorithm. Experimental results show that compared with other algorithms, this scheme can effectively optimize the allocation and collaboration of vehicular edge computing resources and reduce the delay and energy consumption caused by vehicle task processing.

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Section: Related Workmentioning

confidence: 99%

An optimization scheme for vehicular edge computing based on Lyapunov function and deep reinforcement learning

Zhu,

Li,

Tan

2024

Preprint

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“…Coordinated allocation of processing resources and communication between mobile devices and MEC servers is crucial to optimize system performance in heterogeneous networks. While recent efforts have been made to jointly design compute offloading and caching in MEC systems [ 1 , 6 , 7 , 8 , 11 ], the issue of edge server service utility rationalization has been neglected. Therefore, further research is required to optimize offloading and caching in heterogeneous network computing systems.…”

Section: Related Workmentioning

confidence: 99%

“…In recent years, the rapid development of the Internet of Things (IoT) and artificial intelligence (AI) has led to the emergence of a wide range of complex applications, including augmented reality (AR) navigation, autonomous driving, face recognition, autonomous control, and smart healthcare [ 1 ]. However, these applications, which are typically latency-sensitive and computationally intensive, place significant demands on smart devices, which are unable to match these requirements for high CPU power and battery life [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, these applications, which are typically latency-sensitive and computationally intensive, place significant demands on smart devices, which are unable to match these requirements for high CPU power and battery life [ 2 ]. Mobile edge computing (MEC) has thus emerged as a new paradigm to move computing away from centralized cloud infrastructure and toward the logical edges of the network, where base stations (BSs) with MEC servers installed, known as MEC-enabled BSs, are able to help mobile terminals (MTs) overcome their resource shortage [ 1 ]. At the same time, mobile edge computing helps large-scale environmental monitoring, is of great help in the prevention and control of natural disasters, and effectively improves the response efficiency of emergency communication networks [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, to optimize the system’s overall utility, it is vital to determine which services can be cached on the BS. Recent research has focused on jointly designing service caching, computing offloading, and resource allocation in MEC [ 1 , 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Contract-Optimization Approach (COA): A New Approach for Optimizing Service Caching, Computation Offloading, and Resource Allocation in Mobile Edge Computing Network

Sun

Chen

2023

Sensors

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An optimal method for resource allocation based on contract theory is proposed to improve energy utilization. In heterogeneous networks (HetNets), distributed heterogeneous network architectures are designed to balance different computing capacities, and MEC server gains are designed based on the amount of allocated computing tasks. An optimal function based on contract theory is developed to optimize the revenue gain of MEC servers while considering constraints such as service caching, computation offloading, and the number of resources allocated. As the objective function is a complex problem, it is solved utilizing equivalent transformations and variations of the reduced constraints. A greedy algorithm is applied to solve the optimal function. A comparative experiment on resource allocation is conducted, and energy utilization parameters are calculated to compare the effectiveness of the proposed algorithm and the main algorithm. The results show that the proposed incentive mechanism has a significant advantage in improving the utility of the MEC server.

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A Novel Deep Federated Learning-Based and Profit-Driven Service Caching Method

Ouyang,

Xia,

Peng

et al. 2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Joint Service Caching and Computation Offloading Scheme Based on Deep Reinforcement Learning in Vehicular Edge Computing Systems

Abstract: Zhengguo (2023) Joint service caching and computation offloading scheme based on deep reinforcement learning in vehicular edge computing systems. IEEE Transactions on Vehicular Technology. pp. 1-14.

Cited by 49 publications

References 41 publications

An optimization scheme for vehicular edge computing based on Lyapunov function and deep reinforcement learning

An optimization scheme for vehicular edge computing based on Lyapunov function and deep reinforcement learning

Contract-Optimization Approach (COA): A New Approach for Optimizing Service Caching, Computation Offloading, and Resource Allocation in Mobile Edge Computing Network

A Novel Deep Federated Learning-Based and Profit-Driven Service Caching Method

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