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

Received Date: 2025-07-07
Accepted Date: 2025-12-29
Rev Recd Date: 2025-12-29

Available Online: 2026-01-08

Abstract

Abstract

Objective With the growth of the industrial Internet of Things and the widespread use of multimode terminals, multi-access edge computing has become a key technology that supports low-latency and energy-efficient industrial applications. Task offloading is central to addressing the large volume and complex processing requirements of multimode terminals. In multi-access edge computing systems, end-user network selection strongly affects offloading and resource allocation. However, existing network-selection mechanisms emphasize user decisions while neglecting the effects of task execution, task-data transmission, and processing on network performance. Current studies on offloading design emphasize delay, energy optimization, and resource allocation, but overlook how collaborative computing across heterogeneous networks affects resource cost and dynamic resource balance. To address these issues, this study considers users’ diverse requirements and the differentiated capabilities of heterogeneous resource providers. It focuses on cost-efficient task-execution decisions and dynamic-resource allocation in multi-access heterogeneous networks to reduce system cost, improve service quality, and support cooperative use of heterogeneous resources. Methods Following the MEC network model, this study establishes cost-calculation models for task-execution time, energy consumption, and communication-resource consumption for different networks during end-user task selection. Using auction theory, it constructs a cost-effectiveness model for task evaluation and bidding between users and edge servers, and formulates the objective optimization problem based on combinatorial two-way auction theory. A dynamic resource-sensing and task offloading algorithm based on an auction mechanism is then proposed. Through two-way broadcasting of pending tasks and required resources, the algorithm performs network-selection assessment and dynamic allocation of computing and communication resources. Servers submit valid bids only when their available resources satisfy user constraints. Servers that issue valid bids compete for task-execution opportunities until the user obtains the optimal bid and corresponding server, which completes the auction-matching process. Results and Discussions The proposed dynamic-resource allocation and task offloading algorithm accounts for heterogeneous-network conditions and resource usage, and selects offloading locations based on resource availability. By setting simulation parameters, a heterogeneous wireless-network cooperation model is constructed. The effects of network size on offloading cost and offloaded data volume are analyzed. Simulation results show that the algorithm reduces system cost by at least 5% compared with benchmark algorithms (Fig. 3), with larger advantages when the number of end users increases. Changes in the number of servers influence users’ network-selection behavior (Fig. 4, 5, 6). Across algorithms, the proposed method increases the amount of offloaded data by approximately 10% relative to benchmark schemes (Fig. 7, 8). Finally, the study analyzes how variation in communication-resource cost parameters affects users’ preference for offloading via the 5G public network. Higher communication-cost parameters markedly reduce the data volume offloaded through the 5G network (Fig. 9). Conclusions To address complex data-processing demands from multimode terminals, this study develops a cooperative multi-access edge computing architecture for multimode devices. Flexible and intelligent wireless-network selection provides additional resources for end-user task offloading. A server-bidding and user-target bidding model is built using an auction framework, and a dynamic resource-perception and task offloading algorithm is proposed. The algorithm first adjusts and selects the offloading network and allocates computing and communication resources according to incoming tasks. It then determines the offloading location with minimum execution cost based on competition among edge servers. Results indicate that the proposed algorithm lowers system cost compared with benchmark approaches, increases the amount of data offloaded to multiple edge servers, improves utilization of edge-computing resources, and enhances system energy efficiency and operational efficiency.
- Multi-access edge computing,
- Task offloading,
- Network selection,
- Dynamic resource allocation

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References(28)

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