Abstract
Emerging use-cases like smart manufacturing and smart cities pose challenges in terms of latency, which cannot be satisfied by traditional centralized infrastructure. Edge networks, which bring computational capacity closer to the users/clients, are a promising solution for supporting these critical low latency services. Different from traditional centralized networks, the edge is distributed by nature and is usually equipped with limited compute capacity. This creates a complex network to handle, subject to failures of different natures, that requires novel solutions to work in practice. To reduce complexity, edge application technology enablers, advanced infrastructure and application orchestration techniques need to be in place where AI and ML are key players.
- A. Y. Ding et al., 'Roadmap for edge AI: A dagstuhl perspective," CoRR, vol. abs/2112.00616, 2021.Google Scholar
- G. Wikström et al., '6g -- connecting a cyber-physical world: A research outlook towards 2030," Ericsson, White paper, Feb. 2022.Google Scholar
- A. Sefidcon, W. John, M. Opsenica, and B. Skubic, 'The network compute fabric -- advancing digital transformation with ever-present service continuity," Ericsson Technology Review, June 2021.Google Scholar
Cross Ref
- A. Haas et al., 'Bringing the web up to speed with webassembly," in Proceedings of the 38th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2017, (New York, NY, USA), p. 185--200, Association for Computing Machinery, 2017.Google Scholar
- M. Nieke, L. Almstedt, and R. Kapitza, 'Edgedancer: Secure mobile webassembly services on the edge," in Proceedings of the 4th International Workshop on Edge Systems, Analytics and Networking, EdgeSys '21, (New York, NY, USA), p. 13--18, Association for Computing Machinery, 2021.Google Scholar
- K. R. Weiss, T. M. Khoshgoftaar, and D. Wang, 'A survey of transfer learning," Journal of Big Data, vol. 3, pp. 1--40, 2016.Google Scholar
Cross Ref
- S. Solorio-Fernández, J. A. Carrasco-Ochoa, and J. F. Martínez-Trinidad, 'A review of unsupervised feature selection methods," Artificial Intelligence Review, vol. 53, pp. 907--948, 2019.Google Scholar
Digital Library
- M. E. F. G. Sanz and A. Johnsson, 'Exploring approaches for heterogeneous transfer learning in edge clouds," in IEEE/IFIP Network Operations and Management Symposium (NOMS), 2022.Google Scholar
- H. Larsson, J. Taghia, F. Moradi, and A. Johnsson, 'Source selection in transfer learning for improved service performance predictions," in 2021 IFIP Networking Conference and Workshops, 2021.Google Scholar
- F. Moradi, R. Stadler, and A. Johnsson, 'Performance prediction in dynamic clouds using transfer learning," 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM), 2019.Google Scholar
- X. Wang, F. S. Samani, A. Johnsson, and R. Stadler, 'Online feature selection for low-overhead learning in networked systems," in 2021 17th International Conference on Network and Service Management (CNSM), pp. 527-- 529, IEEE, 2021.Google Scholar
- X. Hu, P. Zhou, P. Li, J. Wang, and X. Wu, 'A survey on online feature selection with streaming features," Frontiers of Computer Science, vol. 12, pp. 479--493, 2016.Google Scholar
- J. Taghia, F. Moradi, H. Larsson, X. Lan, M. Ebrahimi, and A. Johnsson, 'Policy-induced unsupervised feature selection: A networking case study," in IEEE INFOCOM 2022-IEEE Conference on Computer Communications, 2022.Google Scholar
- W. Y. B. L. et. al, 'Federated learning in mobile edge networks: A comprehensive survey," IEEE Communications Surveys & Tutorials, vol. 22, pp. 2031--2063, 2020.Google Scholar
Cross Ref
- Q. Xia, W. Ye, Z. Tao, J. Wu, and Q. Li, 'A survey of federated learning for edge computing: Research problems and solutions," High-Confidence Computing, 2021.Google Scholar
- A. A. Barakabitze, A. Ahmad, R. Mijumbi, and A. Hines, '5G network slicing using SDN and NFV: A survey of taxonomy, architectures and future challenges," Computer Networks, vol. 167, p. 106984, 2020.Google Scholar
Digital Library
- N. Shahriar, S. Taeb, S. R. Chowdhury, M. Zulfiqar, M. Tornatore, R. Boutaba, J. Mitra, and M. Hemmati, 'Reliable slicing of 5G transport networks with bandwidth squeezing and multi-path provisioning," IEEE Transactions on Network and Service Management, vol. 17, no. 3, 2020.Google Scholar
Cross Ref
- A. Marotta, D. Cassioli, M. Tornatore, Y. Hirota, Y. Awaji, and B. Mukherjee, 'Reliable slicing with isolation in optical metro-aggregation networks," in 2020 Optical Fiber Communications Conference and Exhibition (OFC), pp. 1-- 3, 2020.Google Scholar
- B. M. Khorsandi, F. Tonini, and C. Raffaelli, 'Centralized vs. distributed algorithms for resilient 5G access networks," Photonic Network Communications, vol. 37, pp. 376--387, Jun 2019.Google Scholar
Digital Library
- H. D. Chantre and N. L. Saldanha da Fonseca, 'The location problem for the provisioning of protected slices in NFV-based MEC infrastructure," IEEE Journal on Selected Areas in Communications, vol. 38, no. 7, pp. 1505-- 1514, 2020.Google Scholar
Cross Ref
- T. L. F. Project, 'State of the edge 2021: A market and ecosystem report for edge computing," whitepaper, 2021.Google Scholar
- E. Amato, F. Tonini, C. Raffaelli, and P. Monti, 'A resource sharing method for reliable slice as a service provisioning in 5G metro networks," in 2021 International Conference on Optical Network Design and Modeling (ONDM), pp. 1--3, 2021.Google Scholar
Recommendations
Ambient intelligence - a state of the art from artificial intelligence perspective
EPIA'07: Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligenceAmbient Intelligence (AmI) deals with a new world where computing devices are spread everywhere (ubiquity), allowing the human being to interact in physical world environments in an intelligent and unobtrusive way. These environments should be aware of ...






Comments