Design of Dynamic Resource Awareness and Task Offloading Schemes in Multi-Access Edge Computing Networks

ZHANG Bingxue; LI Xisheng; YOU Jia

doi:10.11999/JEIT250640

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ZHANG Bingxue, LI Xisheng, YOU Jia. Design of Dynamic Resource Awareness and Task Offloading Schemes in Multi-Access Edge Computing Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250640

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ZHANG Bingxue, LI Xisheng, YOU Jia. Design of Dynamic Resource Awareness and Task Offloading Schemes in Multi-Access Edge Computing Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250640

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Design of Dynamic Resource Awareness and Task Offloading Schemes in Multi-Access Edge Computing Networks

doi: 10.11999/JEIT250640 cstr: 32379.14.JEIT250640

1.
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.
Beijing Engineering Research Center of Industrial Spectrum Imaging, Beijing 100083, China

Funds: National Key Research and Development Program of China(2019YFB2101900)

Accepted Date: 2025-12-29
Rev Recd Date: 2025-12-29

Available Online: 2026-01-08

Abstract

Abstract

Objective With the development of industrial Internet of Things and the widespread use of multi-mode terminal equipment, multi-access edge computing has become a key technology to support low delay and energy-efficient industrial applications. The task offloading mechanism of edge computing is the core method to solve the large number and complex task processing requirements of multi-mode terminals. In the multi-access edge computing system, the network selection of end users has a great impact on the offloading mechanism and resource allocation. However, the existing network selection mechanism focuses on the user's selection decision, and ignores the impact of user’s task execution, task data offloading transmission and processing on network performance. For the research on the formulation of task offloading mechanism, the existing research focuses on the offloading delay, energy consumption optimization and resource allocation, ignoring the impact of multi-access heterogeneous network collaborative computing on resource costs and the dynamic resource balance between heterogeneous networks. In order to meet these challenges, this paper considers the impact of users’ diverse needs and heterogeneous resource providers’ differentiated capabilities on the decision-making of offloading in a complex computing environment, and makes the decision-making of user task execution cost optimization and rational allocation of dynamic resources in multi-access heterogeneous networks, so as to reduce the system operation cost, improve the quality of service, and efficiently and cooperatively utilize heterogeneous resources. Methods According to the multi-access edge computing network model, this paper establishes the cost calculation model for the task execution time, energy consumption and communication resource consumption of different networks for the end-user task selection. Based on the auction theory, it establishes the cost-effective model of computing task evaluation and bidding for the interaction between users and edge servers, and establishes the objective optimization problem according to the combinatorial two-way auction theory. Then, a dynamic resource sensing and task offloading algorithm based on auction mechanism is proposed. Through the two-way broadcast of the task information to be accessed and the required resources, network selection judgment and dynamic resource allocation are carried out. Only when the available resources meet the user resource constraints can the server offer effective bidding. An effective bidding edge server is proposed to compete for the opportunity of user task execution until the user obtains an optimal bidding and corresponding server to complete the auction matching process of the user task. Results and Discussions The dynamic resource allocation and task offloading algorithm based on auction mechanism considers the heterogeneous network status and resource usage, and selects the task offloading location according to the resource allocation. By setting the simulation system parameters, the edge computing model of heterogeneous wireless network cooperation is constructed, and the impact of network size on task offload cost and task offload data volume is analyzed. The simulation results show that the dynamic resource allocation and task offloading algorithm based on auction mechanism can reduce the system cost by at least 5% compared with other benchmark algorithms (Fig. 3), which is more obvious when there are more end users. Changes in the number of servers in heterogeneous networks have a certain impact on users' selection of a network for task offloading (Fig. 4, 5, 6). Under different algorithms, the proposed algorithm has a 10% improvement in the amount of task offload data compared with the benchmark algorithm (Fig. 7. 8). Finally, the impact of the change of the communication resource cost parameter on the user’s choice of 5G public network for task offloading is studied. The larger the communication cost parameter, the amount of data processed by the end user’s choice of 5G public network offloading task is significantly reduced (Fig. 9). Conclusions Aiming at the complex data processing requirements of multi-mode terminals, this paper constructs a multi-access edge computing cooperation network architecture for multi-mode terminals. The flexible and intelligent selection of wireless communication network by multi-mode terminals provides more resources for end-user task offloading. A server bidding and user target bidding model is established based on the auction model, and a dynamic resource perception and task unloading algorithm based on the auction mechanism is proposed to offload multi-mode terminal tasks, network selection and resource allocation. The algorithm first dynamically adjusts and selects the offloading network and allocates computing and communication resources according to the access tasks, and then selects the task offloading location with the minimum execution cost according to the bidding competition of each edge server. The results show that the proposed algorithm can effectively reduce the system cost compared with the benchmark algorithm, and improve the amount of data offloading from end-user tasks to multi edge servers, make full use of edge computing resources, and improve the system energy efficiency and operation efficiency.
- Multi-access edge computing,
- Task offloading,
- Network selection,
- Dynamic resource allocation

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References(28)

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