Multicast With Network Coding in Application-Layer Overlay Networks
Bibliographic record
Abstract
All of the advantages of application-layer overlay networks arise from two fundamental properties: 1) the network nodes in an overlay network, as opposed to lower-layer network elements such as routers and switches, are end systems and have capabilities far beyond basic operations of storing and forwarding; 2) the overlay topology, residing above a densely connected Internet protocol-layer wide-area network, can be constructed and manipulated to suit one's purposes. We seek to improve end-to-end throughput significantly in application-layer multicast by taking full advantage of these unique characteristics. This objective is achieved with two novel insights. First, we depart from the conventional view that overlay nodes can only replicate and forward data. Rather, as end systems, these overlay nodes also have the full capability of encoding and decoding data at the message level using efficient linear codes. Second, we depart from traditional wisdom that the multicast topology from source to receivers needs to be a tree, and propose a novel and distributed algorithm to construct a two-redundant multicast graph (a directed acyclic graph) as the multicast topology, on which network coding is applied. We design our algorithm such that the costs of link stress and stretch are explicitly considered as constraints and minimized. We extensively evaluate our algorithm by provable analytical and experimental results, which show that the introduction of two-redundant multicast graph and network coding may indeed bring significant benefits, essentially doubling the end-to-end throughput in most cases.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".