Abstract
Data centers have networks that provide, for a large number of users, various services such as video streaming, financial services, file storage, among others. Like any network-based system, a Data Center (DC) is subject to congestion, a fact that can cause slowness and instability, affecting the performance of applications for end users. Among the technologies for data center management, Software Defined Networking (SDN) separate the control from the data plane, allowing applications to access statistical information and other details about current state of the network through a controller. In this sense, SDN brings a new opportunity to detect congestion on DC networks in a centralized manner. However, identifying the occurrence of congestion in networks is an arduous task due to the high number of related data. With these data, this work seeks to apply supervised classification to identify congestion events. The experimental campaign demonstrated that the SDN-based supervised classification mechanisms find more congestion events then the native congestion control algorithms from Transmission Control Protocol (TCP). Moreover, among diverse supervised algorithms, we demonstrate which one has better accuracy and efficient performance when analyzing Cubic and DCTCP variants.
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Acknowledgements
This work was funding by the National Council for Scientific and Technological Development (CNPq), the Santa Catarina State Research and Innovation Support Foundation (FAPESC), UDESC, and developed at LabP2D. This work received financial support from the Coordination for the Improvement of Higher Education Personnel - CAPES - Brazil (PROAP/AUXPE) 0093/2021.
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da Silva de Oliveira, F., Pillon, M.A., Miers, C.C., Koslovski, G.P. (2023). Identifying Network Congestion on SDN-Based Data Centers with Supervised Classification. In: Barolli, L. (eds) Advanced Information Networking and Applications. AINA 2023. Lecture Notes in Networks and Systems, vol 654. Springer, Cham. https://doi.org/10.1007/978-3-031-28451-9_20
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