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Record W2807331033 · doi:10.5555/3213200.3213211

Simulating link aggregation in private virtual lan using openflow for cloud environment

2018· article· en· W2807331033 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

VenueCommunications and Networking Symposium · 2018
Typearticle
Languageen
FieldComputer Science
TopicSoftware-Defined Networks and 5G
Canadian institutionsConcordia University of Edmonton
Fundersnot available
KeywordsComputer scienceComputer networkCloud computingOpenFlowSoftware-defined networkingLink layerPort (circuit theory)Distributed computingTelecommunications linkIsolation (microbiology)Virtual LANOperating systemEngineering

Abstract

fetched live from OpenAlex

Segregation and isolation of mission critical devices and services are among the main security concerns in cloud computing environments. Private Virtual LAN (PVLAN) offers the ability to efficiently support segregation and isolation among end devices. Link aggregation on PVLAN promiscuous ports reduces the risk of single point of failure for the entire PVLAN network. This research focuses on improving security and availability of nodes within the PVLAN domain and layer three devices by combining multiple PVLAN promiscuous ports as a single logical port using Software Defined Networking protocols. Our approach enables cloud platform to implement PVLAN by incorporating link aggregation to extend and support PVLAN features for optimal load balancing and path selection of inbound and outbound traffic. It also helps to reduce network inefficiencies which might occur from multiple traffic utilizing a single communication uplink. Simulation results show the effectiveness of our approach.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.938
Threshold uncertainty score0.602

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.044
GPT teacher head0.284
Teacher spread0.240 · 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