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Record W2906795940

Revive: A Reliable Software Defined Data Plane Failure Recovery Scheme

2018· article· en· W2906795940 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueConference on Network and Service Management · 2018
Typearticle
Languageen
FieldComputer Science
TopicSoftware-Defined Networks and 5G
Canadian institutionsDalhousie University
Fundersnot available
KeywordsBackupForwarding planeComputer scienceNetwork topologyScheme (mathematics)Computer networkSoftware-defined networkingController (irrigation)Reliability (semiconductor)Network switchSoftwareDistributed computingOperating systemNetwork packet
DOInot available

Abstract

fetched live from OpenAlex

In Software-Defined Networking (SDN) links and switches (nodes) from data plane suffer from failure and impact network operations. In the presence of such failures, switches can use reactive or proactive recovery scheme. In the reactive scheme, switches contact the controller after detecting a link failure to get a backup route setup; whereas, switches locally redirect the traffic to the backup route without controller's intervention in the case of the proactive scheme. In this paper, we propose a hybrid recovery scheme, called Revive, where the controller proactively installs backup routes only in a subset of the switches between a source-destination pair. In addition, we judicially configure the routes in Revive to meet the application and the reliability demand while efficiently utilize the network resources. Extensive experimental results in Mininet using real topologies illustrate the benefits of Revive compared to its counterparts.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.380
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.249
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it