Header length reduction for bloom filter multicast using stochastic overlay networks
Bibliographic record
Abstract
Bloom filter based multicast is a methodology in which bloom filters are used to implement a source routing approach to multicast forwarding, with the goal of addressing forwarding element memory scalability issues in traditional IP multicast deployments. These techniques face their own scalability issues with large multicast group sizes, or networks with a high degree of node interconnectivity, as the necessary bloom filters may be too large to be practically inserted in packet headers. In this work we contribute a technique by which a centralized network controller may leverage stochastically generated overlay networks to reduce the length of in-packet bloom filters used for multicast packet delivery. We evaluate our technique through simulation of false-positive-free filter generation on representative multicast workloads, and find that it achieves a significant reduction in bloom filter lengths (up to 48% in our best case WAN topology scenario, and up to 89% in our best case regular grid topology scenario). Our technique is appropriate for deployment in software defined networks, where the control plane of the network is logically centralized in a network controller, and our technique may be applied with minimal extensions to forwarding hardware.
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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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".