A subtree-based approach to failure detection and protection for multicast in SDN
Bibliographic record
Abstract
Software-defined networking (SDN) has received tremendous attention from both industry and academia. The centralized control plane in SDN has a global view of the network and can be used to provide more effective solutions for complex problems, such as traffic engineering. This study is motivated by recent advancement in SDN and increasing popularity of multicasting applications. We propose a technique to increase the resiliency of multicasting in SDN based on the subtree protection mechanism. Multicasting is a group communication technology, which uses the network infrastructure efficiently by sending the data only once from one or multiple sources to a group of receivers that share a common path. Multicasting applications, e.g., live video streaming and video conferencing, become popular, but they are delay-sensitive applications. Failures in an ongoing multicast session can cause packet losses and delay, which can significantly affect quality of service (QoS). In this study, we adapt a subtree-based technique to protect a multicast tree constructed for OpenFlow switches in SDN. The proposed algorithm can detect link or node failures from a multicast tree and then determines which part of the multicast tree requires changes in the flow table to recover from the failure. With a centralized controller in SDN, the backup paths can be created much more effectively in comparison to the signaling approach used in traditional multiprotocol label switching (MPLS) networks for backup paths, which makes the subtree-based protection mechanism feasible. We also implement a prototype of the algorithm in the POX controller and measure its performance by emulating failures in different tree topologies in Mininet.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".