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machine-learned classifiers for Protocol Selection on a Shared Network 1

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1st International Conference on machine Learning for Networking (MLN)

作者： Anvari, Hamidreza Huard, Jesse Lu, Paul Univ Alberta Dept Comp Sci Edmonton AB Canada

ISBN: (数字)9783030199456

ISBN: (纸本)9783030199456;9783030199449

Knowledge about the state of a data network can be used to achieve high performance. For example, knowledge about the protocols in use by background traffic might influence which protocol to choose for a new foreground data transfer. Unfortunately, global knowledge can be difficult to obtain in a dynamic distributed system like a wide-area network (WAN). Therefore, we introduce and evaluate a machine-learning (ML) approach to network performance, called optimization through protocol selection (OPS). Using local round-trip time (RTT) time-series data, a classifier predicts the mix of protocols in current use. Then, a decision process selects the best protocol to use for the new foreground transfer, so as to maximize throughput while maintaining fairness. We show that a protocol oracle would choose TCP-BBR for the new foreground traffic if TCP-BBR is already in use in the background, for proper throughput. Similarly, the protocol oracle would choose TCP-CUBIC for the new foreground traffic if only TCP-CUBIC is in use in the background, for fairness. Empirically, our k-nearest-neighbour (K-NN) classifier, utilizing dynamic time warping (DTW) measure, results in a protocol decision accuracy of 0.80 for k = 1. The OPS approach's throughput is 4 times higher than that achieved with a suboptimal protocol choice. Furthermore, the OPS approach has a Jain fairness index of 0.96 to 0.99, as compared to a Jain fairness of 0.60 to 0.62, if a suboptimal protocol is selected.

关键词： machine-learned classifier Protocol selection Data transfer Wide-area networks High-performance network Fairness Shared network

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Active Probing for Improved machine-learned Recognition of Network Traffic 1

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3rd International Conference on machine Learning for Networking (MLN)

作者： Anvari, Hamidreza Lu, Paul Univ Alberta Dept Comp Sci Edmonton AB Canada

ISBN: (数字)9783030708665

ISBN: (纸本)9783030708665;9783030708658

Information about the network protocols used by the background traffic can be important to the foreground traffic. Whether that knowledge is exploited via optimization through protocol selection (OPS) or through other forms of parameter tuning, a machine-learned classifier is one tool to identifying background traffic protocols. Unfortunately, global knowledge can be difficult to obtain in a dynamic distributed system like a shared, wide-area network (WAN). Previous techniques for protocol identification have focused on passive or end-point signals for classification. For example, end-to-end round trip time (RTT) can, especially when gathered as a time series, reveal a lot about what is happening on the network. Other related signals, such as bandwidth, and the number of retransmissions can also be used for protocol classification. However, as noted, these signals are typically gathered by passive means, which may limit their usefulness. We introduce and provide a proof-of-concept of active probing, which is the systematic and deliberate perturbation of traffic on a network for the purpose of gathering information. The time-series data generated by active probing improves our machine-learned classifiers because different network protocols react differently to the probing. Whereas passive probing might be limiting the time series observations to a period of steady state (e.g., saturated network), active probing forces the system out of that steady state. We show that active probing improves on prior work (with passive probing of RTT) by between 7% to 16% in additional accuracy (depending on the window size), and reaching 90% averages in precision, recall, and F1-scores.

关键词： machine-learned classifier Active probing Protocol selection Data transfer Wide-area networks Fairness Shared network

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