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Record W2513515172 · doi:10.1145/2967360.2967363

U-HAUL

2016· article· en· W2513515172 on OpenAlexaff
Libin Liu, Hong Xu, Zhixiong Niu, Peng Wang, Dongsu Han

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware-Defined Networks and 5G
Canadian institutionsKootenay Association for Science & Technology
FundersInstitute for Information and Communications Technology PromotionMinistry of Science, ICT and Future PlanningNational Research Foundation of KoreaResearch Grants Council, University Grants CommitteeUniversity Grants Committee
KeywordsComputer scienceSoftware deploymentNetwork Functions VirtualizationOverhead (engineering)Load balancing (electrical power)VirtualizationComputer networkTask (project management)Distributed computingOperating systemCloud computingEngineering

Abstract

fetched live from OpenAlex

Network function virtualization (NFV) enables dynamic scaling of resources to middlebox deployment and management. In NFV, state migration is an important task because operators often need to shift traffic and its associated flow states across NF instances for load balancing. Existing state migration schemes, however, exhibit long delays and high controller overhead.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.936
Threshold uncertainty score0.844

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.009
GPT teacher head0.195
Teacher spread0.187 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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".

Quick stats

Citations23
Published2016
Admission routes1
Has abstractyes

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