Flow Size Prediction with Short Time Gaps
Bibliographic record
Abstract
Having a priori knowledge about network flow sizes is invaluable in network traffic control. Previous efforts on estimating flow sizes have focused on long flows, where each flow is identified by a large time gap in the sequence of packets. In this work, using extensive measurements, we investigate the feasibility of predicting the size of short flows, where the flow duration can be in the order of microseconds. Specifically, we deploy several popular workloads in a public cloud testbed, and collect both network and host traces for each workload. The network trace contains standard packet metadata, while the host trace contains high-level host statistics (e.g., memory usage and disk I/O) and low-level function call traces (e.g., malloc(), send()) that are captured during the execution of each workload via host instrumentation using eBPF. These traces are then used to train machine learning models for flow size prediction with varying time gaps ranging from microseconds to milliseconds. Our results indicate that: (1) It is feasible to predict short flow sizes with high accuracy, i.e., percentage error in 0-12% range, (2) the low-level traces lead to 10-20% improvement in prediction accuracy compared to using the network and high-level traces.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".