Vehicle Collaborative Partial Offloading Strategy in Vehicular Edge Computing

Ruoyu Chen, Yanfang Fan (), Shuang Yuan and Yanbo Hao
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Ruoyu Chen: Institute of Intelligent Information Processing, Beijing Information Science and Technology University, Beijing 100192, China
Yanfang Fan: Computer School, Beijing Information Science and Technology University, Beijing 100192, China
Shuang Yuan: Computer School, Beijing Information Science and Technology University, Beijing 100192, China
Yanbo Hao: Computer School, Beijing Information Science and Technology University, Beijing 100192, China

Mathematics, 2024, vol. 12, issue 10, 1-17

Abstract: Vehicular Edge Computing (VEC) is a crucial application of Mobile Edge Computing (MEC) in vehicular networks. In VEC networks, the computation tasks of vehicle terminals (VTs) can be offloaded to nearby MEC servers, overcoming the limitations of VTs’ processing power and reducing latency caused by distant cloud communication. However, a mismatch between VTs’ demanding tasks and MEC servers’ limited resources can overload MEC servers, impacting Quality of Service (QoS) for computationally intensive tasks. Additionally, vehicle mobility can disrupt communication with static MEC servers, further affecting VTs’ QoS. To address these challenges, this paper proposes a vehicle collaborative partial computation offloading model. This model allows VTs to offload tasks to two types of service nodes: collaborative vehicles and MEC servers. Factors like a vehicle’s mobility, remaining battery power, and available computational power are also considered when evaluating its suitability for collaborative offloading. Furthermore, we design a deep reinforcement learning-based strategy for collaborative partial computation offloading that minimizes overall task delay while meeting individual latency constraints. Experimental results demonstrate that compared to traditional approaches without vehicle collaboration, this scheme significantly reduces latency and achieves a significant reduction (around 2%) in the failure rate under tighter latency constraints.

Keywords: mobile edge computing; vehicular edge computing; computation offloading; deep reinforcement learning; partial offloading (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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