Export file:


  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text


  • Citation Only
  • Citation and Abstract

A study on the design methodology of TAC3 for edge computing

1 School of Computer Engineering, Jinling Institute of Technology, Nanjing 211169, China
2 Faculty of Electronic Information Engineering, Jinling Institute of Technology, Nanjing 211169, China
3 School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
4 King Saud University, Riyadh 11362, Saudi Arabia

Special Issues: Future Techniques and Applications of Smart Cities by Fog/Edge Computing

The following scenarios, such as complex application requirements, ZB (Zettabyte) order of magnitude of network data, and tens of billions of connected devices, pose serious challenges to the capabilities and security of the three pillars of ICT: Computing, network, and storage. Edge computing came into being. Following the design methodology of "description-synthesis-simulation-optimization", TAC3 (Tile-Architecture Cluster Computing Core) was proposed as the lightweight accelerated ECN (Edge Computing Node). ECN with a Tile-Architecture be designed and simulated through the method of executable description specification and polymorphous parallelism DSE (Design Space Exploration). By reasonable configuration of the edge computing environment and constant optimization of typical application scenarios, such as convolutional neural network and processing of image and graphic, we can meet the challenges of network bandwidth, end-cloud delay and privacy security brought by massive data of the IoE. The philosophy of "Edge-Cloud complements each other, and Edge-AI energizes each other" will become a new generation of IoE behavior principle.
  Article Metrics

Keywords edge computing; EC architecture; design methodology; TAC3

Citation: Yong Zhu, Zhipeng Jiang, Xiaohui Mo, Bo Zhang, Abdullah Al-Dhelaan, Fahad Al-Dhelaan. A study on the design methodology of TAC3 for edge computing. Mathematical Biosciences and Engineering, 2020, 17(5): 4406-4421. doi: 10.3934/mbe.2020243


  • 1. W. Shi, H. Sun, J. Cao, Q. Zhang, W. Liu, Edge Computing-An Emerging Computing Model for the Internet of Everything Era, J. Comput. Res. Dev., 5 (2017), 907-924.
  • 2. D. Boru, D. Kliazovich, F. Granelli, P. Bouvry, A. Zomaya, Energy-efficient data replication in cloud computing datacenters, Cluster Comput., 18 (2015), 385-402.
  • 3. Q. Fan, N. Ansari, Application Aware Workload Allocation for Edge Computing based IoT, IEEE Int. Things J., 5 (2018), 2146-2153.
  • 4. J. Zhang, B. Chen, Y. Zhao, X. Cheng, F. Hu, Data Security and Privacy-Preserving in Edge Computing Paradigm: Survey and Open Issues, IEEE Access, 6 (2018), 18209-18237.
  • 5. S. Garg, A. Singh, K. Kaur, J. Aujla, S. Batra, N. Kumar, et al., Edge Computing-Based Security Framework for Big Data Analytics in VANETs, IEEE Network, 33 (2019),72-81.
  • 6. B. Song, M. Hassan, A. Alamri, A. Alelaiwi, Y. Tian, M. Pathan, et al., A two-stage approach for task and resource management in multimedia cloud environment, Computing, 98 (2016), 119-145.
  • 7. Y. Tamura, S. Yamada, Reliability Analysis Based on a Jump Diffusion Model with Two Wiener Processes for Cloud Computing with Big Data, Entropy, 17 (2015), 4533-4546.
  • 8. Z. Pan, C. Yang, V. S. Sheng, N. Xiong, W. Meng. Machine learning for wireless multimedia data security, Sec. Communi. Networks, 2019 (2019), 7682306.
  • 9. Z. Zhao, F. Liu, Z. Cai, N. Xiao, Edge Computing: Platforms, Applications and Challenges, J. Comput. Res. Dev., 55 (2018), 327-337.
  • 10. Z. Huang, K. Lin, C. S. Shih, Supporting Edge Intelligence in Service-Oriented Smart IoT Applications, 2016 IEEE International Conference on Computer and Information Technology (CIT), IEEE, 2016. Available from: https://ieeexplore.ieee.org/abstract/document/7876378.
  • 11. Y. Mao, C. You, J. Zhang, K. Huang, K. B. Letaief, A Survey on Mobile Edge Computing: The Communication Perspective, IEEE Commun. Surv. Tutorials, 19 (2017), 2322-2358.
  • 12. B. Rimal, D. P. Van, M. Maier, Mobile-Edge Computing Empowered Fiber-Wireless Access Networks in the 5G Era, IEEE Commun. Mag., 55 (2017), 192200.
  • 13. R. Kemp, N. Palmer, T. Kielmann, H. Bal, Cuckoo: A Computation Offloading Framework for Smartphones, International Conference on Mobile Computing, Applications, and Services, 2010, 59-79. Available from: https://link.springer.com/chapter/10.1007/978-3-642-29336-8_4.
  • 14. F. Bonomi, R. Milito, J. Zhu, S. Addepalli, Fog computing and its role in the internet of things, Proceedings of first edition of the Mcc workshop on Mobile cloud computing, 2012, 13-16. Available from: https://dl.acm.org/doi/abs/10.1145/2342509.2342513.
  • 15. Y. Tian, M. M. Kaleemullah, M. A. Rodhaan, B. Song, A. Al-Dhelaan, T. Ma, A Privacy Preserving Location Service for Cloud-of-Things System, J. Parallel Dis. Comput., 123 (2019), 215-222.
  • 16. R. Serban, I. Culic, Configuring a cisco ir829gw as an internet of things device, 2016 15th RoEduNet Conference: Networking in Education and Research, 2016. Available from: https://ieeexplore.ieee.org/abstract/document/7753219.
  • 17. B. Al-Otibi, N. Al-Nabhan, Y. Tian, Privacy-preserving Vehicular Rogue Node Detection Scheme for Fog Computing, Sensors, 19 (2019), 965.
  • 18. K. Li, C. Liu, Edge intelligence: State-of-the-art and expectations, Big Data Res., 5 (2019), 72-78.
  • 19. Z. Zhou, S. Yu, X. Chen, Edge intelligence: A new nexus of edge computing and artificial intelligence, Big Data Res., 5 (2019), 56-66.
  • 20. Y. Cao, P. Hou, D. Brown, J. Wang, S. Chen, Distributed Analytics and Edge Intelligence: Pervasive Health Monitoring at the Era of Fog Computing, Proceedings of the 2015 Workshop on Mobile Big Data, 2015, 43-48. Available from: https://dl.acm.org/doi/abs/10.1145/2757384.2757398.
  • 21. Y. Liang, Mobile Intelligence Sharing Based on Agents in Mobile Peer-to-Peer Environment, Intelligent Information Technology and Security Informatics (IITSI), 2010 Third International Symposium, 2010. Available from: https://ieeexplore.ieee.org/abstract/document/5453713.
  • 22. P. H. Su, C. Shih, J. Hsu, K. Lin, Y. Wang, Decentralized fault tolerance mechanism for intelligent IoT/M2M middleware, 2014 IEEE World Forum on Internet of Things (WF-IoT), 2014. Available from: https://ieeexplore.ieee.org/abstract/document/6803115.
  • 23. T. Ma, H. Rong, Y. Hao, J. Cao, Y. Tian, M. A. Al-Rodhaan, A Novel Sentiment Polarity Detection Framework for Chinese, IEEE Trans. Affect. Comput., 2019 (2019).
  • 24. Edge Computing Consortium and Alliance of Industrial Internet, Edge Computing Reference Architecture 3.0, Alliance of Industrial Internet, 2018.
  • 25. A. Elias, N. Golubovic, C. Krintz, R. Wolski, Where's the Bear?-Automating Wildlife Image Processing Using IoT and Edge Cloud Systems, 2017 IEEE/ACM Second International Conference on Internet-of-Things Design and Implementation (IoTDI), 2017. Available from: https://ieeexplore.ieee.org/abstract/document/7946882.
  • 26. Y. Lu, Y. Chen, T. Li, Convolutional Neural Network Construction Method for Embedded FPGAs Oriented Edge Computing, J. Comput. Res. Dev., 55 (2018), 551-562.
  • 27. R. McDonald, D. Burger, S. W. Keckler, K. Sankaralingam, R. Nagarajan, TRIPS Processor Reference Manual, The University of Texas at Austin, 2005.
  • 28. Y. Zhu, Study on the Polymorphism Parallelism of Tile Architecture, J. Jinling Inst. Tech., 2 (2017).
  • 29. K. Sankaralingam, R. Nagarajan, H. Liu, C. Kim, J. Huh, D. Burger, et al., Exploiting ILP, TLP, and DLP with the polymorphous trips architecture, 30th Annual International Symposium on Computer Architecture, 2003. Available from: https://ieeexplore.ieee.org/abstract/document/1207019.
  • 30. A. Kagi, J. R. Goodman, D. Burger, Memory Bandwidth Limitations of Future Microprocessors, 23rd International Symposium on Computer Architecture, 1996. Available from: https://ieeexplore.ieee.org/abstract/document/1563037.
  • 31. H. Hanson, M. S. Hrishikesh, V. Agarwal, S. W. Keckler, D. Burger, Static Energy Reduction Techniques for Microprocessor Caches, IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 11 (2003), 303-313.    
  • 32. K. Natarajan, H. Hanson, S. W. Keckler, C. R. Moore, D. Burger, Microprocessor Pipeline Energy Analysis, Proceedings of the 2003 International Symposium on Low Power Electronics and Design, 2003. Available from: https://ieeexplore.ieee.org/abstract/document/1231878.


Reader Comments

your name: *   your email: *  

© 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Copyright © AIMS Press All Rights Reserved