Task Offloading and Resource Allocation Method for End-to-End Delay Optimization in Digital Twin Edge Networks

LI Song; LI Shun; WANG Bowen; SUN Yanjing

doi:10.11999/JEIT240344

Volume 47 Issue 3

Mar. 2025

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LI Song, LI Shun, WANG Bowen, SUN Yanjing. Task Offloading and Resource Allocation Method for End-to-End Delay Optimization in Digital Twin Edge Networks[J]. Journal of Electronics & Information Technology, 2025, 47(3): 633-644. doi: 10.11999/JEIT240344

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LI Song, LI Shun, WANG Bowen, SUN Yanjing. Task Offloading and Resource Allocation Method for End-to-End Delay Optimization in Digital Twin Edge Networks[J]. Journal of Electronics & Information Technology, 2025, 47(3): 633-644. doi: 10.11999/JEIT240344

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Task Offloading and Resource Allocation Method for End-to-End Delay Optimization in Digital Twin Edge Networks

doi: 10.11999/JEIT240344 cstr: 32379.14.JEIT240344

LI Song^{1, 2
,
,},
LI Shun¹,
WANG Bowen¹,
SUN Yanjing^{1, 2}

1.
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
2.
Xuzhou Engineering Research Center of Intelligent Industry Safety and Emergency Collaboration, Xuzhou 221116, China

Funds: The National Natural Science Foundation of China (62071472, 62101556), The Natural Science Foundation of Jiangsu Province of China (BK20210489), The Fundamental Research Funds for the Central Universities (2020ZDPYMS26), The Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX24_2763), The Graduate Innovation Program of China University of Mining and Technology (2024WLJCRCZL133), Xi’an Key Laboratory of Network Convergence Communication (2022NCC-N103), The Project of Technical Innovation of Hainan Scientific Research Institutes (KYYSGY2024-005), The Ministry of Industry and Information Technology (CBG01N23-01-04)

Received Date: 2024-04-29
Rev Recd Date: 2025-02-12

Available Online: 2025-02-20

Publish Date: 2025-03-01

Abstract

Abstract

Objective The rapid development of wireless communication and the Internet of Things (IoT) has led to significant growth in compute-intensive and delay-sensitive applications, which impose stricter latency requirements. However, local devices often face challenges in meeting these demands due to limitations in storage, computing power, and battery life. Mobile Edge Computing (MEC) has emerged as a key technology to address these issues. Despite its potential, the dynamic and complex nature of edge networks presents significant challenges in task offloading and resource allocation. DIgital Twin Edge Networks (DITEN), which map digital twins to physical devices in real-time, offer a promising solution. By integrating MEC with Digital Twin (DT) technology, this approach not only alleviates resource limitations in devices but also optimizes resource allocation in the digital domain, minimizing physical resource waste. This paper tackles the End-to-End (E2E) optimization problem in the offloading, computation, and result feedback process within edge computing networks. A DT-assisted joint task offloading, device association, and resource allocation scheme is proposed for E2E delay optimization, providing theoretical support for improving resource utilization in edge networks. Methods The optimization problem in this paper involves a non-convex objective function with both binary and continuous constraints, making it a mixed integer non-convex problem. To address this, the original problem is decomposed into four subproblems: computation and communication resource optimization, device association optimization, offloading decision optimization, and transmission bandwidth optimization. Within the Alternating Optimization (AO) framework, the Internal Convex Approximation (ICA) method is applied to convert the non-convex problem into a convex one. Additionally, the many-to-one matching problem is transformed into a one-to-one matching problem, and the Hungarian Algorithm (HA) is employed to solve the device association subproblem. Finally, the ICA-HA-AO is proposed to address the E2E delay optimization problem effectively. Results and Discussions The ICA-HA-AO algorithm approximates non-convex constraints as convex ones through constraint transformation and iteratively solves the original problem, determining optimal strategies for task offloading, device association, and resource allocation. Simulation results show that the ICA-HA-AO algorithm achieves optimal performance across varying task resource requirements, bandwidth, edge processing rates, and task volumes. Compared to the worst-performing benchmark scheme, delays are reduced by approximately 0.8 s, 1.5 s, 0.5 s, and 1.2 s, respectively (Fig. 5–Fig. 8). As the DT deviation increases, the delay also increases more significantly, with a rise of about 0.13 s when the deviation increases from 0.01 to 0.02, emphasizing the importance of setting the DT deviation (Fig. 9). When the number of devices remains constant and the number of Access Points (APs) increases, the delay continues to decrease, highlighting the significance of AP deployment in practice. Additionally, when the number of APs remains fixed and the number of devices increases, the delay increases accordingly. However, the ICA-HA-AO algorithm effectively controls the rate of delay increase. For instance, when the number of devices is 10, 15, and 20, the delay increase is reduced from 0.39 s to 0.21 s (Fig. 10). These results demonstrate that the ICA-HA-AO algorithm can more efficiently utilize and schedule resources, achieving optimal resource allocation. Conclusions This paper investigates the joint optimization problem of task offloading, device association, and resource allocation in DITEN. Firstly, within the edge computing network, physical and DT models are established for a network comprising sensors, edge servers, and actuators. A comprehensive task model is developed, and the E2E delay for tasks is derived. The optimization problem for minimizing E2E delay is then formulated, subject to constraints such as power and energy consumption. Secondly, to solve the proposed mixed integer non-convex optimization problem, the original problem is decomposed into four subproblems. Based on the ICA and HA methods, an ICA-HA-AO algorithm is proposed to solve the problem iteratively. Finally, simulation results demonstrate that the proposed ICA-HA-AO algorithm significantly reduces E2E delay and outperforms benchmark schemes. Future work may explore integrating this method with techniques to improve spectrum utilization, thereby further enhancing spectrum efficiency and overall performance in DITEN systems.
- Mobile Edge Computing (MEC),
- Digital Twin (DT),
- Task offloading,
- Resource allocation,
- Alternating optimization

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References(19)

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