Abstract
Thanks to recent advancements in edge computing, the traditional centralized cloud-based approach to deploy Artificial Intelligence (AI) techniques will be soon replaced or complemented by the so-called edge AI approach. By pushing AI at the network edge, close to the large amount of raw input data, the traffic traversing the core network as well as the inference latency can be reduced. Despite such neat benefits, the actual deployment of edge AI across distributed nodes raises novel challenges to be addressed, such as the need to enforce proper addressing and discovery procedures, to identify AI components, and to chain them in an interoperable manner. Named Data Networking (NDN) has been recently argued as one of the main enablers of network and computing convergence, which edge AI should build upon. However, the peculiarities of such a new paradigm entails to go a step further. In this paper we disclose the potential of NDN to support the orchestration of edge AI. Several motivations are discussed, as well as the challenges which serve as guidelines for progress beyond the state of the art in this topic.
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References
Wang, X., et al.: Convergence of edge computing and deep learning: a comprehensive survey. IEEE Commun. Surv. Tutorials 22(2), 869–904 (2020)
Ramos, E., et al.: Distributing intelligence to the edge and beyond. IEEE Comput. Intell. Mag. 14(4), 65–92 (2019)
Amadeo, M., et al.: NDNe: enhancing named data networking to support cloudification at the edge. IEEE Commun. Lett. 20(11), 2264–2267 (2016)
Scherb, C., et al.: Resolution strategies for networking the IoT at the edge via named functions. In: 2018 15th IEEE CCNC, pp. 1–6 (2018)
Mtibaa, A., et al.: Towards edge computing over named data networking. In: 2018 IEEE International Conference on Edge Computing (EDGE), pp. 117–120 (2018)
Amadeo, M., et al.: IoT services allocation at the edge via named data networking: from optimal bounds to practical design. IEEE Trans. Netw. Serv. Manage. 16(2), 661–674 (2019)
Zhang, L., et al.: Named data networking. ACM SIGCOMM Comput. Commun. Rev. 44(3), 66–73 (2014)
Tschudin, C., Sifalakis, M.: Named functions and cached computations. In: IEEE CCNC, pp. 851–857 (2014)
Wang, X., Han, Y., Wang, C., Zhao, Q., Chen, X., Chen, M.: In-edge AI: intelligentizing mobile edge computing, caching and communication by federated learning. IEEE Network 33(5), 156–165 (2019)
Quevedo, J., et al.: Consumer driven information freshness approach for content centric networking. In: 2014 IEEE INFOCOM Workshop, pp. 482–487 (2014)
Carzaniga, A., et al.: Content-based publish/subscribe networking and information-centric networking. In: ACM SIGCOMM Workshop on Information-centric Networking, pp. 56–61 (2011)
Amadeo, M., et al.: Securing the mobile edge through named data networking. In: 2018 IEEE WF-IoT, pp. 80–85 (2018)
Acknowledgments
This work has been partially supported by the A COGnItive dynamic sysTem to allOw buildings to learn and adapt’ (COGITO) project, funded by the Italian Government (PON ARS01_00836).
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Campolo, C., Lia, G., Amadeo, M., Ruggeri, G., Iera, A., Molinaro, A. (2020). Towards Named AI Networking: Unveiling the Potential of NDN for Edge AI. In: Grieco, L.A., Boggia, G., Piro, G., Jararweh, Y., Campolo, C. (eds) Ad-Hoc, Mobile, and Wireless Networks. ADHOC-NOW 2020. Lecture Notes in Computer Science(), vol 12338. Springer, Cham. https://doi.org/10.1007/978-3-030-61746-2_2
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DOI: https://doi.org/10.1007/978-3-030-61746-2_2
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