Obtaining Accurate Bandwidth Estimations for the Internet of Things
Bibliographic record
Abstract
Bandwidth estimation is important for many Internet of Things (IoT) applications. Typical bandwidth estimation techniques involve saturating the link and causing temporary congestion; however, this may not be suitable for latencysensitive IoT applications. Packet pairs and trains are alternative bandwidth estimation methods that use packet inter-arrival time. However, these techniques are often noisy [14] and inaccurate. One source of this inaccuracy is caused by kernel interrupt handling and scheduling [22]. In this paper, we propose our PacketBurst bandwidth estimation technique. The main contribution of PacketBurst is to remove inaccuracies in packet train bandwidth estimate calculations that are caused by kernel interrupts. We observe that consecutive packets in packet trains may have exceedingly small differences in their receive timestamps. Using these timestamps in a packet train bandwidth estimate calculation can result in an inaccurate estimate. We identify four cases where kernel interrupts impact the packet train’s receive timestamps, and we show how to address the timestamp inaccuracies of each case using our PacketBurst technique. We show that PacketBurst estimates can reduce measurement errors of packet trains. When using six MTU-sized packets to estimate available bandwidth, the PacketBurst technique experiences an 2.75% error whereas packet trains experience a 21.6% error when the available bandwidth is 20Mbps.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".