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A mobile edge computing-based applications execution framework for Internet of Vehicles

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Abstract

Mobile edge computing (MEC) is a promising technology for the Internet of Vehicles, especially in terms of application offloading and resource allocation. Most existing offloading schemes are sub-optimal, since these offloading strategies consider an application as a whole. In comparison, in this paper we propose an application-centric framework and build a finer-grained offloading scheme based on application partitioning. In our framework, each application is modelled as a directed acyclic graph, where each node represents a subtask and each edge represents the data flow dependency between a pair of subtasks. Both vehicles and MEC server within the communication range can be used as candidate offloading nodes. Then, the offloading involves assigning these computing nodes to subtasks. In addition, the proposed offloading scheme deal with the delay constraint of each subtask. The experimental evaluation show that, compared to existing non-partitioning offloading schemes, this proposed one effectively improves the performance of the application in terms of execution time and throughput.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. U20A20177, 61772377, 91746206), the Fundamental Research Funds for the Central Universities, and Science and Technology planning project of ShenZhen (JCYJ20170818112550194).

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Authors and Affiliations

  1. School of Computer Science, Wuhan University, Wuhan, 430000, China

    Libing Wu, Rui Zhang & Qingan Li

  2. School of Cyber Science and Engineering, Wuhan University, Wuhan, 430000, China

    Libing Wu, Chao Ma & Xiaochuan Shi

  3. Shenzhen Research Institute of Wuhan University, Shenzhen, 518052, China

    Libing Wu

Authors
  1. Libing Wu
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  2. Rui Zhang
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  3. Qingan Li
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  4. Chao Ma
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  5. Xiaochuan Shi
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Corresponding authors

Correspondence to Libing Wu or Qingan Li.

Additional information

Libing Wu received his PhD degree in Computer Science from Wuhan University, China. Now he is a Professor in School of Cyber Science and Engineering and School of Computer Science, and Shenzhen Research Institute of Wuhan University, China. His current research interests include network communication, grid computing and Internet of Things.

Rui Zhang received her Master degree in computer application technology from Nanchang Hangkong University, China. She is studying in the School of Computer Science, Wuhan University, China. Her current research interests include Internet of Vehicles, Artificial Intelligence and Edge Computing.

Qingan Li received the BS and PhD degrees in computer science from Wuhan University, China in 2008 and 2013 respectively. He also received the PhD degree in computer science from City University of Hong Kong, China, in 2014. He is currently an associate professor in the School of Computer Science at Wuhan University, China. His current research interests include software optimization and embedded systems.

Chao Ma (member of IEEE and professional member of CCF) received the BE degree in computer science and PhD degree in software theory from Wuhan University, China in 2005 and 2010, respectively. He is currently an Assistant Professor of School of Cyber Science and Engineering at Wuhan University, China. His research interests include time series analytics, representation learning, program comprehension, knowledge graph and generative adversarial networks.

Xiaochuan Shi received the BS degree in Computer Science and Techology from Wuhan University, China in 2006 and the MS degree in Software Theory from Wuhan University, China, in 2008. He received the PhD degree in computer architecture from Wuhan University, China in 2011. He is currently an Associate Professor with the School of Cyber Science and Engineering, Wuhan University, China. His research interests include Big data analysis, reinforcement learning, data modeling, wireless sensor networks.

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Wu, L., Zhang, R., Li, Q. et al. A mobile edge computing-based applications execution framework for Internet of Vehicles. Front. Comput. Sci. 16, 165506 (2022). https://doi.org/10.1007/s11704-021-0425-6

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  • DOI: https://doi.org/10.1007/s11704-021-0425-6

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