{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T15:46:03Z","timestamp":1768319163263,"version":"3.49.0"},"reference-count":51,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2023,5,13]],"date-time":"2023-05-13T00:00:00Z","timestamp":1683936000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Italian National Recovery and Resilience Plan (NRRP) of NextGenerationEU","award":["PE0000001"],"award-info":[{"award-number":["PE0000001"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Internet Things"],"published-print":{"date-parts":[[2023,5,31]]},"abstract":"<jats:p>This article deals with an artificial intelligence (AI) framework to support Internet-of-everything (IoE) applications over sixth-generation wireless (6G) networks. An integrated IoE-Edge Intelligence ecosystem is designed to effectively face the problems of Virtual Machines (VMs) placement based on their popularity, computation offloading optimization, and system reliability improvement predicting compute nodes faults. The main objective of the article is to increase performance in terms of minimization of worst end-to-end (e2e) delay, percentage of requests in outage, and the enhancement of reliability. The article focuses on the following main issues: (i) proposal of a channel-aware federated learning (FL) approach to forecast the popularity of the VMs required by IoE devices; (ii) use of an AI-based channel conditions forecasting module at the benefits of the FL process; (iii) development of a suitable VMs placement on the basis of their popularity and of an efficient tasks allocation technique based on a modified version of the auction theory (AT) and a proper matching game; (iv) enhancement of the system reliability by an echo-state-network (ESN), located on each computation node and running in the background to predict failures and anticipate tasks migration. Numerical results validate the effectiveness of the proposed strategy for IoE applications over 6G networks.<\/jats:p>","DOI":"10.1145\/3584705","type":"journal-article","created":{"date-parts":[[2023,2,17]],"date-time":"2023-02-17T11:56:58Z","timestamp":1676635018000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["A Channel-aware FL Approach for Virtual Machine Placement in 6G Edge Intelligent Ecosystems"],"prefix":"10.1145","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4970-1361","authenticated-orcid":false,"given":"Benedetta","family":"Picano","sequence":"first","affiliation":[{"name":"University of Florence, Firenze, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5934-3321","authenticated-orcid":false,"given":"Romano","family":"Fantacci","sequence":"additional","affiliation":[{"name":"University of Florence, Firenze, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,5,13]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2009.02.039"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/OJCOMS.2022.3140272"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2016.2598848"},{"key":"e_1_3_1_5_2","doi-asserted-by":"crossref","unstructured":"Christina Chaccour Mehdi Soorki Walid Saad Mehdi Bennis and Petar Popovski. 2022. Can Terahertz Provide High-Rate Reliable Low Latency Communications for Wireless VR?. IEEE Internet of Things Journal 9 12 (2022) 9712\u20139729.","DOI":"10.1109\/JIOT.2022.3142674"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3053283"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2018.2846549"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2014.2316834"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2015.2487344"},{"key":"e_1_3_1_10_2","first-page":"775","volume-title":"Proceedings of the 2018 13th IEEE Conference on Industrial Electronics and Applications","author":"Ding Y.","year":"2018","unstructured":"Y. Ding, G. Liao, and S. Liu. 2018. Virtual machine placement based on degradation factor ant colony algorithm. In Proceedings of the 2018 13th IEEE Conference on Industrial Electronics and Applications. 775\u2013779."},{"key":"e_1_3_1_11_2","first-page":"181","volume-title":"Proceedings of the 2018 IEEE 16th International Conference on Industrial Informatics","author":"Ding Z.","year":"2018","unstructured":"Z. Ding, Y. Tian, and M. Tang. 2018. Efficient fitness function computation of genetic algorithm in virtual machine placement for greener data centers. In Proceedings of the 2018 IEEE 16th International Conference on Industrial Informatics. 181\u2013186."},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0097-8493(99)00047-3"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/SERE-C.2014.16"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/GLOCOM.2018.8647845"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2018.2811395"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.nancom.2017.08.001"},{"key":"e_1_3_1_17_2","first-page":"1","volume-title":"Proceedings of the 2016 IEEE World Conference on Factory Communication Systems","author":"Henneke D.","year":"2016","unstructured":"D. Henneke, L. Wisniewski, and J. Jasperneite. 2016. Analysis of realizing a future industrial network by means of Software-Defined Networking (SDN). In Proceedings of the 2016 IEEE World Conference on Factory Communication Systems. 1\u20134."},{"key":"e_1_3_1_18_2","first-page":"13","volume-title":"Proceedings of the 2018 IEEE Workshop on Machine Learning Techniques for Software Quality Evaluation","author":"Huch F.","year":"2018","unstructured":"F. Huch, M. Golagha, A. Petrovska, and A. Krauss. 2018. Machine learning-based run-time anomaly detection in software systems: An industrial evaluation. In Proceedings of the 2018 IEEE Workshop on Machine Learning Techniques for Software Quality Evaluation. 13\u201318."},{"key":"e_1_3_1_19_2","first-page":"364","volume-title":"Proceedings of the 2015 International Conference on Intelligent Informatics and Biomedical Sciences","author":"Kao H.","year":"2015","unstructured":"H. Kao, Y. Yang, and C. Huang. 2015. Dynamic virtual machines placement in a cloud environment by multi-objective programming approaches. In Proceedings of the 2015 International Conference on Intelligent Informatics and Biomedical Sciences. 364\u2013365."},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICOIN48656.2020.9016505"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/TAP.2014.2373371"},{"key":"e_1_3_1_22_2","unstructured":"Jakub Konecny H. Brendan McMahan Felix X. Yu Peter Richtarik Ananda Theertha Suresh and Dave Bacon. 2016. Federated Learning: Strategies for Improving Communication Efficiency. arXiv:1610.05492. Retrieved from https:\/\/arxiv.org\/abs\/1610.05492"},{"key":"e_1_3_1_23_2","first-page":"554","volume-title":"Proceedings of the 8th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks","author":"Koutsopoulos I.","year":"2010","unstructured":"I. Koutsopoulos and G. Iosifidis. 2010. Auction mechanisms for network resource allocation. In Proceedings of the 8th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks. 554\u2013563."},{"key":"e_1_3_1_24_2","first-page":"285","volume-title":"Proceedings of the 2015 38th International Conference on Telecommunications and Signal Processing","author":"Kukr\u00e1l T.","year":"2015","unstructured":"T. Kukr\u00e1l, M. Koz\u00e1k, T. H\u00e9gr, and L. Boh\u00e1\u010d. 2015. VM migration measurement and failure detection. In Proceedings of the 2015 38th International Conference on Telecommunications and Signal Processing. 285\u2013288."},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2014.6848063"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2018.2797187"},{"key":"e_1_3_1_27_2","first-page":"1","article-title":"Resource management for pervasive edge computing-assisted wireless VR streaming in industrial internet of things","author":"Lin P.","year":"2021","unstructured":"P. Lin, Q. Song, D. Wang, R. Yu, L. Guo, and V. Leung. 2021. Resource management for pervasive edge computing-assisted wireless VR streaming in industrial internet of things. IEEE Transactions on Industrial Informatics 17, 11 (2021), 1\u20131.","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2017.2647981"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2018.2844341"},{"key":"e_1_3_1_30_2","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.1109\/WACV.2017.118","volume-title":"Proceedings of the 2017 IEEE Winter Conference on Applications of Computer Vision","author":"Munawar A.","year":"2017","unstructured":"A. Munawar, P. Vinayavekhin, and G. De Magistris. 2017. Spatio-temporal anomaly detection for industrial robots through prediction in unsupervised feature space. In Proceedings of the 2017 IEEE Winter Conference on Applications of Computer Vision. 1017\u20131025."},{"key":"e_1_3_1_31_2","first-page":"1536","volume-title":"Proceedings of the 2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference","author":"Nangu Shota","year":"2020","unstructured":"Shota Nangu, Tomotaka Kimura, and Kouji Hirata. 2020. Optimization of virtual machine placement for balancing network and server load in edge computing environments. In Proceedings of the 2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference. 1536\u20131540."},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3077550"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/iccchina.2019.8855868"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.001.1900287"},{"key":"e_1_3_1_35_2","first-page":"86","volume-title":"Proceedings of the 2013 11th IEEE International Conference on Industrial Informatics.","author":"Sato K.","year":"2013","unstructured":"K. Sato, M. Samejima, and N. Komoda. 2013. Dynamic optimization of virtual machine placement by resource usage prediction. In Proceedings of the 2013 11th IEEE International Conference on Industrial Informatics.86\u201391."},{"key":"e_1_3_1_36_2","unstructured":"Virginia Smith Chao-Kai Chiang Maziar Sanjabi and Ameet Talwalkar. 2017. Federated multi-task learning. In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS\u201917) . Curran Associates Inc. 4427\u20134437."},{"key":"e_1_3_1_37_2","first-page":"135","volume-title":"Proceedings of the 2017 IEEE 15th International Conference on Industrial Informatics.","author":"Sonklin C.","year":"2017","unstructured":"C. Sonklin, M. Tang, and Y. Tian. 2017. A decrease-and-conquer genetic algorithm for energy efficient virtual machine placement in data centers. In Proceedings of the 2017 IEEE 15th International Conference on Industrial Informatics.135\u2013140."},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2017.2690678"},{"key":"e_1_3_1_39_2","first-page":"1545","volume-title":"Proceedings of the 2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference","author":"Takeda Ayaka","year":"2020","unstructured":"Ayaka Takeda, Tomotaka Kimura, and Kouji Hirata. 2020. Joint optimization of edge server and virtual machine placement in edge computing environments. In Proceedings of the 2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference. 1545\u20131548."},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.21227\/2jhd-wp15"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/INFCOMW.2018.8406881"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2017.2672701"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2017.8057198"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2952917"},{"key":"e_1_3_1_45_2","unstructured":"Yong Xiao Guangming Shi and Marwan Krunz. 2020. Towards Ubiquitous AI in 6G with Federated Learning. arXiv preprint arXiv:2004.13563 (2020)."},{"key":"e_1_3_1_46_2","doi-asserted-by":"crossref","unstructured":"Qiang Yang Yang Liu Tianjian Chen and Yongxin Tong. 2019. Federated machine learning: Concept and applications. ACM Trans. Intell. Syst. Technol. 10 2 (2019) 19.","DOI":"10.1145\/3298981"},{"key":"e_1_3_1_47_2","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1109\/ACCESS.2016.2614701","article-title":"Distributed ABS-slot access in dense heterogeneous networks: A potential game approach with generalized interference model","volume":"5","author":"Yao K.","year":"2017","unstructured":"K. Yao, Q. Wu, Y. Xu, and J. Jing. 2017. Distributed ABS-slot access in dense heterogeneous networks: A potential game approach with generalized interference model. IEEE Access 5 (2017), 94\u2013104.","journal-title":"IEEE Access"},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/GLOCOM.2018.8647616"},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1109\/CLOUD.2018.00030"},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1109\/SURV.2012.110112.00125"},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2018.2808171"},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.1109\/WOCC.2019.8770689"}],"container-title":["ACM Transactions on Internet of Things"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3584705","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3584705","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:51:37Z","timestamp":1750182697000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3584705"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,13]]},"references-count":51,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,5,31]]}},"alternative-id":["10.1145\/3584705"],"URL":"https:\/\/doi.org\/10.1145\/3584705","relation":{},"ISSN":["2691-1914","2577-6207"],"issn-type":[{"value":"2691-1914","type":"print"},{"value":"2577-6207","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,13]]},"assertion":[{"value":"2022-05-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-02-09","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-05-13","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}