Demystifying Failure Recovery for Software-Defined Wireless Mesh Networks
Bibliographic record
Abstract
Software Defined Networking (SDN) brings unprecedented opportunities for facilitating the management of Wireless Mesh Networks (WMNs), particularly when handling network dynamics such as link/node failures. The inflexible destination-based routing and failure recovery of conventional WMN routing protocols are superseded by the efficient and highly flexible per-flow (re)routing in SDN-based networks. Most of the existing works focus on protection by pre-computing backup paths using OpenFlow fast failover groups to reroute the flows in case of channel re-assignment or a single link failure in software-defined WMNs. However, the limitations of failure detection using link monitoring for protection have not been studied. Moreover, the potentials of restoration-based failure recovery to handle single/multiple failure scenarios, have not been fully investigated. In this paper, we implement a prototype for reactive failure recovery in multi-radio multi-channel software-defined WMNs that provides a detailed performance and sensitivity analysis. The acquired results from the SDN-based scenarios outperform the most popular conventional WMN routing protocols in terms of recovery time while preserving the best achievable throughput even when the out-of-band control network is partially impaired. We also demonstrate the limitation of link monitoring, which mainly results from the shared nature of wireless medium, for protection-based mechanisms in software-defined WMNs.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".