Lightweight reliable overlay multicasting in large-scale P2P networks
Bibliographic record
Abstract
Because of the complexity, cost and limited deployment of multicast capability at the network layer, application layer multicasting between end hosts has become an attractive option for distributing content among a large number of users based on a peer-to-peer architecture. In contrast to network layer multicasting where the tree nodes are fairly static, multicasting in P2P networks has unique characteristics: the large number of network nodes participating in the multicast operation, and the fact that network nodes may drop out, move or join at a significantly higher frequency than in network layer multicasting. These features pose certain challenges for network service designers, in particular regarding how to guarantee continuous multicast service in face of parent node departure or link/node failures, an issue that is referred as service restorability. In this paper we examine a hybrid architecture that contains both a static overlay backbone (i.e. owned by service provider) and dynamic nodes (mostly end hosts). A design for service survivability is examined, and a flexible lightweight approach is proposed that achieves survivability in large-scale networks using minimal resources at individual node.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".