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. 2025 Aug 1;15(1):28126.
doi: 10.1038/s41598-025-13856-w.

Multihop cost awareness task migration with networking load balance technology for vehicular edge computing

Shih-Yang Lin  1 , Jie-Qin Wang  2 , Shi-Ming Peng  2   3 , Miao-Hui Yang  2 , Shuo Jia  2
Affiliations

Multihop cost awareness task migration with networking load balance technology for vehicular edge computing

Shih-Yang Lin et al. Sci Rep. .

Abstract

6G technology aims to revolutionize the mobile communication industry by revamping the role of vehicular wireless connections. Its network architecture will evolve towards multi-access edge computing (MEC) distributing cloud applications to support inter-vehicle applications such as cooperative driving. As the number of tasks offloaded to MEC servers increases, local MEC servers associated with vehicles may encounter insufficient computing resources for task offloading. This issue can be mitigated if neighboring servers can collaboratively provide computing capabilities to the local server for task migration. This paper investigates dynamic resource allocation and task migration mechanisms for cooperative vehicular edge computing (VEC) servers to expand computing capabilities of local server. Then, the multihop cost awareness task migration (MCATM) mechanism is proposed in this paper, which ensures that tasks can be migrated to the most suitable VEC server when the local server is overloaded. The MCATM mechanism begins by addressing whether the nearest VEC server can handle the computational tasks. We subsequently address the issue of duplicate selection to choose an appropriate VEC server for task migration among n-hop neighboring servers. Next, we focus on finding efficient transmission paths between the local and destination VEC servers to facilitate seamless task migration. The MCATM includes (i) the weight variable analytic hierarchy process (WVAHP) to select a suitable server among multihop cooperative VEC servers for task migration, and (ii) the pre-allocation with cost balance (PACB) path selection algorithm. The simulation results demonstrate that the MCATM enables the migration of computational tasks to appropriate neighboring VEC servers with the aim of increasing the task migration success rate while balancing network traffic and computing server capabilities.

Keywords: 5G C-V2X; Cooperative VEC; Vehicular edge computing; Weight variable AHP.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
The cooperative VEC system.
Fig. 2
Fig. 2
The system architecture.
Fig. 3
Fig. 3
The structure of WVAHP.
Fig. 4
Fig. 4
Example of PACB path selection.
Algorithm 1
Algorithm 1
Pre-Allocation with Cost Balance(PACB).
Fig. 5
Fig. 5
Example of VEC network topology.
Fig. 6
Fig. 6
Analysis of the VEC network.
Fig. 7
Fig. 7
Reward curve of LySAC.
Fig. 8
Fig. 8
LySAC average delay under 100 episodes.
Fig. 9
Fig. 9
Delay curve of LySAC in the testing stage under 100 episodes.
Fig. 10
Fig. 10
Standard deviation of the selected count.
Fig. 11
Fig. 11
Comparison of task delays under (a) fixed task size (500 kb), and (b) various task size (400 ~ 500 kb).
Fig. 12
Fig. 12
Comparison of link utilization.
Fig. 13
Fig. 13
Comparison of total delay of task computations under server service capabilities of (a) 20%, (b) 40%, (c) 60%, and (d) 80%.
Fig. 14
Fig. 14
Comparison of the average delay.
Fig. 15
Fig. 15
Success rate of task migration.
Fig. 16
Fig. 16
Comparison of the elapse time.
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