MétaCan
Menu
Back to cohort
Record W1607439474 · doi:10.1002/9781118937563.ch20

Virtualization, Cloud, SDN, and SDDC in Data Centers

2014· other· en· W1607439474 on OpenAlexaff
Omar Cherkaoui, Ramesh Menon

Bibliographic record

Venuenot available
Typeother
Languageen
FieldComputer Science
TopicCloud Computing and Resource Management
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsCloud computingVirtualizationComputer scienceOperating system

Abstract

fetched live from OpenAlex

Virtualization and Cloud have significantly impacted the data center infrastructures and, in particular, the network infrastructure. There are many additional demands on today's data centers and an increasing number of devices and applications. This chapter first explains the virtualized infrastructure components present in the data center. It then elaborates on the Cloud concepts and the related Cloud Service Offerings focusing mostly on Infrastructure as a Service (IaaS) offering. It also highlights the different issues related to building the IaaS service. The chapter explains the different elements in the design of a network for the new modern data center: what are the different considerations and how to dimension the network. Finally, it presents the Software Defined Networking (SDN) and Software-Defined Data Center (SDDC) concepts required for the network design and implementation for the new modern data centers.

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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.483
Threshold uncertainty score0.577

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 science0.0020.002
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 head0.016
GPT teacher head0.239
Teacher spread0.222 · 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
GenreOther

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

Citations1
Published2014
Admission routes1
Has abstractyes

Explore more

Same topicCloud Computing and Resource ManagementFrench-language works237,207