A Datagram Extension to DNS Over QUIC: Proven Resource Conservation in Internet of Things
Bibliographic record
Abstract
In this paper, we investigate the Domain Name System (DNS) over QUIC (DoQ) and propose a non-disruptive extension, which can greatly reduce DoQ’s resource consumption. This extension can benefit all DNS clients – especially Internet of Things (IoT) devices. This is important because even resource-constrained IoT devices can generate dozens of DNS requests every hour. DNS is a crucial service that correlates IP addresses and domain names. It is traditionally sent as plain-text, favoring low-latency results over security and privacy. The repercussion of this can be eavesdropping and information leakage about IoT devices. To address these concerns, the newest and most promising solution is DoQ. QUIC offers features similar to TCP and TLS while also supporting early data delivery and stream multiplexing. DoQ’s specification requires that DNS exchanges occur over independent streams in a long-lived QUIC connection. Our hypothesis is that due to DNS’s typically high transaction volume, managing QUIC streams may be overly resource intensive for IoT devices. Therefore, we have designed and implemented a data delivery mode for DoQ using QUIC datagrams, which we believe to be more preferable than stream-based delivery. To test our theory, we analyzed the memory, CPU, signaling, power, and time of each DoQ delivery mode in a setup generating real queries and network traffic. Our novel datagram-based delivery mode proved to be decisively more resource-friendly with little compromise in terms of functionality or performance. Furthermore, our paper is the first to investigate multiple queries over DoQ, to our knowledge.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".