mlsanityData to 14 Oct 2026

Mobility and Cost Aware Inference Accelerating Algorithm for Edge Intelligence

arXiv 2312.16497 ↗First posted 27 Dec 2023cs.DC cs.AIcited by 3

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Abstract (arXiv)

The edge intelligence (EI) has been widely applied recently. Spliting the model between device, edge server, and cloud can improve the performance of EI greatly. The model segmentation without user mobility has been investigated deeply by previous works. However, in most use cases of EI, the end devices are mobile. Only a few works have been carried out on this aspect. These works still have many issues, such as ignoring the energy consumption of mobile device, inappropriate network assumption, and low effectiveness on adaptiving user mobility, etc. Therefore, for addressing the disadvantages of model segmentation and resource allocation in previous works, we propose mobility and cost aware model segmentation and resource allocation algorithm for accelerating the inference at edge (MCSA). Specfically, in the scenario without user mobility, the loop interation gradient descent (Li-GD) algorithm is provided. When the mobile user has a large model inference task needs to be calculated, it will take the energy consumption of mobile user, the communication and computing resource renting cost, and the inference delay into account to find the optimal model segmentation and resource allocation strategy. In the scenario with user mobility, the mobiity aware Li-GD (MLi-GD) algorithm is proposed to calculate the optimal strategy. Then, the properties of the proposed algorithms are investigated, including convergence, complexity, and approximation ratio. The experimental results demonstrate the effectiveness of the proposed algorithms.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 12 of its 41 references that are in the map. First posted on arXiv 27 Dec 2023.

Builds on

12 of 41 references.

References · 12

  1. 2023A Survey on Multimodal Large Language Modelscited by 909
  2. 2014Convex Optimization: Algorithms and Complexitycited by 575
  3. 2019Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computingcited by 152
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  5. 2020CoEdge: Cooperative DNN Inference with Adaptive Workload Partitioning over Heterogeneous Edge Devicescited by 67
  6. 2018JALAD: Joint Accuracy- and Latency-Aware Deep Structure Decoupling for Edge-Cloud Executioncited by 40
  7. 2018Edge-Host Partitioning of Deep Neural Networks with Feature Space Encoding for Resource-Constrained Internet-of-Things Platformscited by 35
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  9. 2022HFedMS: Heterogeneous Federated Learning with Memorable Data Semantics in Industrial Metaversecited by 14
  10. 2020Joint Multi-User DNN Partitioning and Computational Resource Allocation for Collaborative Edge Intelligencecited by 12
  11. 2018Deep Learning Based Caching for Self-Driving Car in Multi-access Edge Computingcited by 11
  12. 2020Novel Wake-up Scheme for Energy-Efficient Low-Latency Mobile Devices in 5G Networkscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026OrderMoE: An expert similarity driven distributed edge MoE inferencecited by 3
  2. 2026HetRoute Heterogeneous and Cost-aware Collaborative Routing Framework for Distributed Edge MoE Inferencecited by 0
  3. 2026TopoCompress: Topology Aware Token Compression Algorithm for Distributed Edge MoE Inferencecited by 0