Application-layer transfer configurations play a crucial role in achieving desirable performance in high-speed networks. However, finding the optimal configuration for a given transfer task is a difficult problem as it depends on various factors including dataset characteristics, network settings, and background traffic. The state-of-the-art transfer tuning solutions rely on real-time sample transfers to evaluate various configurations and estimate the optimal one. However, existing approaches to run sample transfers incur high delay and measurement errors, thus significantly limit the efficiency of the transfer tuning algorithms. In this paper, we introduce adaptive feed forward deep neural network (DNN) to minimize the error rate of sample transfers without increasing their execution time. We ran 115K file transfers in four different high-speed networks and used their logs to train an adaptive DNN that can quickly and accurately predict the throughput of sample transfers by analyzing instantaneous throughput values. The results gathered in various networks with rich set of transfer configurations indicate that the proposed model reduces error rate by up to 50% compared to the state-of-the-art solutions while keeping the execution time low. We also show that one can further reduce delay or error rate by tuning hyperparameters of the model to meet specific needs of user or application. Finally, transfer learning analysis reveals that the model developed in one network would yield accurate results in other networks with similar transfer convergence characteristics, alleviating the needs to run an extensive data collection and model derivation efforts for each network.
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Tiramisu: Fast Multilayer Network Verification
Today's distributed network control planes are highly sophisticated, with multiple interacting protocols operating at layers 2 and 3. The complexity makes network configurations highly complex and bug-prone. State-of-the-art tools that check if control plane bugs can lead to violations of key properties are either too slow, or do not model common network features. We develop a new, general multilayer graph control plane model that enables using fast, property-customized verification algorithms. Our tool, Tiramisu can verify if policies hold under failures for various real-world and synthetic configurations in < 0.08s in small networks and < 2.2s in large networks. Tiramisu is 2-600X faster than state-of-the-art without losing generality.
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- Award ID(s):
- 1763512
- NSF-PAR ID:
- 10187222
- Date Published:
- Journal Name:
- 17th USENIX Symposium on Networked Systems Design and Implementation
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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