Relay-Based iBGP Multicasting in Software Defined Networks
Bibliographic record
Abstract
In the Internet today, learnt prefixes are forwarded within autonomous systems (ASs) over internal Border Gateway Protocol (iBGP) sessions. Existing schemes for iBGP routing include the full-mesh (FM) solution, route reflection (RR) solution and confederation. Optimal prefix routing and route diversity are the main strength of the FM solution. However, it is rarely employed in a large networks due to its poor scalability. The RR scheme solves the scalability challenge at the cost of FM optimality due to Route Reflector's partial view of the network. The concept of Software Defined Networking (SDN) entails decoupling of the control plane from the forwarding plane such that the control plane is logically centralized benefiting from an overall knowledge of the network for decision making. In this work, we propose a solution based on multicasting which employs relay nodes in the iBGP message dissemination. The relay nodes are elected and act as boundaries of multicast groups, relaying and filtering prefixes into other multicast groups. We evaluate the use of single and multiple relay nodes per multicast group. Our solution brings session management scalability and minimization of duplicate prefix announcement through elimination of peer sessions deemed unnecessary. SDN controller is employed to configure and coordinate the multicast tree.
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 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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".