TY - GEN
T1 - On the Performance of TCP in Reconfigurable Data Center Networks
AU - Aykurt, Kaan
AU - Zerwas, Johannes
AU - Blenk, Andreas
AU - Kellerer, Wolfgang
N1 - Publisher Copyright:
© 2022 IFIP.
PY - 2022
Y1 - 2022
N2 - Today's data centers are hosting various applications under the same roof. The diversity among deployed applications leads to a complex traffic mix in Data Center Networks (DCNs). Reconfigurable Data Center Networks (RD-CNs) have been designed to fulfill the demanding requirements of ever-changing data center traffic. However, they pose new challenges for network traffic engineering, e.g., interference between reconfigurations and congestion control (CC). This raises a fundamental research problem: can the current transport layer protocols handle frequent network updates?; This paper focuses on the Transmission Control Protocol (TCP) and presents a measurement study of TCP variants in RDCNs. The quantitative analysis of the measurements shows that migrated flows suffer from frequent reconfigurations. The effect of reconfigurations on the cost, e.g. increased Flow Completion Time (FCT), depending on the traffic mix is modeled with Machine Learning (ML) methods. The availability of such a model will provide insights into the relationship between the reconfiguration settings and the FCT. Our model explains 88% of the variance in the FCT increase under different reconfiguration settings.
AB - Today's data centers are hosting various applications under the same roof. The diversity among deployed applications leads to a complex traffic mix in Data Center Networks (DCNs). Reconfigurable Data Center Networks (RD-CNs) have been designed to fulfill the demanding requirements of ever-changing data center traffic. However, they pose new challenges for network traffic engineering, e.g., interference between reconfigurations and congestion control (CC). This raises a fundamental research problem: can the current transport layer protocols handle frequent network updates?; This paper focuses on the Transmission Control Protocol (TCP) and presents a measurement study of TCP variants in RDCNs. The quantitative analysis of the measurements shows that migrated flows suffer from frequent reconfigurations. The effect of reconfigurations on the cost, e.g. increased Flow Completion Time (FCT), depending on the traffic mix is modeled with Machine Learning (ML) methods. The availability of such a model will provide insights into the relationship between the reconfiguration settings and the FCT. Our model explains 88% of the variance in the FCT increase under different reconfiguration settings.
KW - TCP measurements
KW - reconfigurable data center networks
UR - http://www.scopus.com/inward/record.url?scp=85143906792&partnerID=8YFLogxK
U2 - 10.23919/CNSM55787.2022.9964863
DO - 10.23919/CNSM55787.2022.9964863
M3 - Conference contribution
AN - SCOPUS:85143906792
T3 - Proceedings of the 2022 18th International Conference of Network and Service Management: Intelligent Management of Disruptive Network Technologies and Services, CNSM 2022
SP - 127
EP - 135
BT - Proceedings of the 2022 18th International Conference of Network and Service Management
A2 - Charalambides, Marinos
A2 - Papadimitriou, Panagiotis
A2 - Cerroni, Walter
A2 - Kanhere, Salil
A2 - Mamatas, Lefteris
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th International Conference of Network and Service Management, CNSM 2022
Y2 - 31 October 2022 through 4 November 2022
ER -