TY - GEN
T1 - Exploring Machine Learning for Classification of QUIC Flows over Satellite
AU - Secchi, Raffaello
AU - Cassara, Pietro
AU - Gotta, Alberto
N1 - ACKNOWLEDGMENT This work is partially funded by the European Space Agency, ESA-ESTEC, Noordwijk, The Netherlands, under contract n. 4000130962/20/NL/NL/FE (“SatNEx V— Satellite Network of Experts V”). The view expressed herein can in no way be taken to reflect the official opinion of the European Space Agency.
PY - 2022/5/16
Y1 - 2022/5/16
N2 - Automatic traffic classification is increasingly important in networking due to the current trend of encrypting transport information (e.g., behind HTTP encrypted tunnels) which prevent intermediate nodes to access end-to-end transport headers. This paper proposes an architecture for supporting Quality of Service (QoS) in hybrid terrestrial and SATCOM networks based on automated traffic classification. Traffic profiles are constructed by machine-learning (ML) algorithms using the series of packet sizes and arrival times of QUIC connections. Thus, the proposed QoS method does not require explicit setup of a path (i.e. it provides soft QoS), but employs agents within the network to verify that flows conform to a given traffic profile. Results over a range of ML models encourage integrating ML technology in SATCOM equipment. The availability of higher computation power at low-cost creates the fertile ground for implementation of these techniques.
AB - Automatic traffic classification is increasingly important in networking due to the current trend of encrypting transport information (e.g., behind HTTP encrypted tunnels) which prevent intermediate nodes to access end-to-end transport headers. This paper proposes an architecture for supporting Quality of Service (QoS) in hybrid terrestrial and SATCOM networks based on automated traffic classification. Traffic profiles are constructed by machine-learning (ML) algorithms using the series of packet sizes and arrival times of QUIC connections. Thus, the proposed QoS method does not require explicit setup of a path (i.e. it provides soft QoS), but employs agents within the network to verify that flows conform to a given traffic profile. Results over a range of ML models encourage integrating ML technology in SATCOM equipment. The availability of higher computation power at low-cost creates the fertile ground for implementation of these techniques.
UR - http://www.scopus.com/inward/record.url?scp=85137269601&partnerID=8YFLogxK
U2 - 10.1109/ICC45855.2022.9838463
DO - 10.1109/ICC45855.2022.9838463
M3 - Published conference contribution
AN - SCOPUS:85137269601
T3 - IEEE International Conference on Communications
SP - 4709
EP - 4714
BT - ICC 2022 - IEEE International Conference on Communications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Communications, ICC 2022
Y2 - 16 May 2022 through 20 May 2022
ER -