A Study of the Predictive Earliness of Traffic Flow Characterization for Software Defined Networking
Software Defined Networking (SDN) is a new network paradigm that decouples the control from the data plane in order to provide a more structured approach to develop applications and services. In traditional networks the routing of flows is defined by masks and tends to be rather static. With SDN, the granularity of routing decisions can be downscaled to single TCP sessions, and can be performed dynamically within a single data stream. In this context We propose a novel approach – coined as micro flow aware routing – aimed at implementing routing of flows based on the properties of transport-level information, which is closely related to the type of application. Our proposed scheme relies on the early characterization of the flow based on statistical predictors, which are computed over a time window spanning the first exchanged packets over the session. We evaluate different window lengths over real traffic data to examine the Pareto trade-off between the earliness of flow characterization and its predictive accuracy. These results stimulate further research towards ensuring the practicality of the scheme.
KeywordsSoftware defined networking Traffic engineering Internet traffic classification Machine learning
This work was supported in part by the Basque Government through the EMAITEK program, and by the Spanish Ministry of Economy, Industry and Competitiveness through the State Secretariat for Research, Development and Innovation under the “Adaptive Management of 5G Services to Support Critical Events in Cities” (5G-City) project (TEC2016-76795-C6-5-R). Finally authors would like to thank you the University of the Basque Country IT services for allowing us to conduct this experiment on their premises with real production data.
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