Simulating Network Paths with Recurrent Buffering Units
Authors: Divyam Anshumaan, Sriram Balasubramanian, Shubham Tiwari, Nagarajan Natarajan, Sundararajan Sellamanickam, Venkat N. Padmanabhan
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | yielding promising results on synthetic and real-world network traces. ... We make three key contributions: ... (3) Efficient and practical solution scales to sequences of length tens of thousands ... yet produces realistic traces in synthetic and real-world network settings (Section 5). |
| Researcher Affiliation | Collaboration | 1Microsoft Research India 2University of Maryland, College Park |
| Pseudocode | No | The paper presents mathematical equations and describes procedures in text, but does not include a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing its source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | Datasets: We (1) design a synthetic benchmark using ns-3 as in (Ashok et al. 2020), consisting of 4200 traces for 4 different TCP protocols, on a variety of cross-traffic patterns and network configurations; and (2) use a subset of traces from a real physical network testbed Pantheon (Yan et al. 2018) for 2 TCP protocols. |
| Dataset Splits | No | The paper specifies using 'TCP Cubic protocol ... for training, and the other TCP protocols (Vegas, New Reno, and LEDBAT) for testing' for its experiments, which is a train/test split by protocol. However, it does not explicitly mention a separate 'validation' split of the datasets themselves (e.g., percentages or counts). |
| Hardware Specification | Yes | Training RBU on the largest dataset (ns-3) takes only about 3 minutes per epoch on V100 GPU. |
| Software Dependencies | No | We implement all the models in Py Torch. ... with their Tensor Flow code. The paper mentions software like PyTorch and TensorFlow but does not specify version numbers for reproducibility. |
| Experiment Setup | Yes | For LSTMwin and LSTMpkt, we (a) normalize the delays and the sending rates, and (b) use a 2-layer LSTM with 256 hidden units and a fully connected layer with discretized yt as output (100-dimensional), tuned to maximize mean delay and throughput distribution match, on the training protocol. For RBU, we (a) use the same LSTM architecture, to be consistent, for the window-level model in (7), with discretized cw in (8) as output, (b) set γ = 0.1 in (8) and size of ht in (1) to 1, which works well across datasets, and (c) use single-bottleneck buffer RBU model (just as the ground-truth) for ns-3, and 2-path RBU model for Pantheon. We use stochastic gradient-descent to learn the model parameters jointly, with mini-batching, and weight decay on the model parameters. |