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AI-Assisted Network Traffic Prediction Without Warm-Up Periods
KTH, School of Electrical Engineering and Computer Science (EECS).
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Communication Systems, CoS.ORCID iD: 0000-0001-8517-7996
Ericsson Research, Stockholm, Sweden..
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Communication Systems, CoS.ORCID iD: 0000-0003-0525-4491
2022 (English)In: 95th IEEE Vehicular Technology Conference - Spring, VTC 2022-SPRING, Institute of Electrical and Electronics Engineers (IEEE) , 2022Conference paper, Published paper (Refereed)
Abstract [en]

Network traffic prediction in cellular networks improves reliability and efficiency of network resource use via proactive network management schemes. To this end, future traffic arrivals are anticipated via machine learning (ML)-based network traffic predictions based on historical network traffic data. Current literature on ML-based network traffic predictions employs warm-up periods, which are the required duration traffic flows are observed to make meaningful predictions. However, most flows are shorter than the warm-up period. This paper proposes a residual neural network (ResNet) architecture for individual network flow predictions, based on a deep-learning approach that removes the required warm-up period seen in other proposed methods. The ResNet architecture demonstrates the ability to accurately predict the magnitude of packet count, size, and duration of flows using only the information available at the arrival of the first packet such as IP addresses and utilized transport-layer protocols. The results indicate that the proposed method is able to predict the order of magnitude of individual flow characteristics with over 80% accuracy, outperforming traditional ML methods such as linear regression and decision trees.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2022.
Series
IEEE Vehicular Technology Conference VTC
Keywords [en]
Internet Flows, IP Networks, ResNet, Machine Learning, Prediction
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:kth:diva-321010DOI: 10.1109/VTC2022-Spring54318.2022.9860997ISI: 000861825803043Scopus ID: 2-s2.0-85137811066OAI: oai:DiVA.org:kth-321010DiVA, id: diva2:1708541
Conference
IEEE 95th Vehicular Technology Conference: (VTC-Spring), JUN 19-22, 2022, Helsinki, FINLAND
Note

Part of proceedings: ISBN 978-1-6654-8243-1

QC 20221104

Available from: 2022-11-04 Created: 2022-11-04 Last updated: 2023-01-24Bibliographically approved

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Bolakhrif, AminÖzger, MustafaCavdar, Cicek

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