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Record W4368408252 · doi:10.1145/3578353.3589544

Is Bare-metal I/O Performance with User-defined Storage Drives Inside VMs Possible?: Benchmarking libvfio-user vs. Common Storage Virtualization Configurations

2023· article· en· W4368408252 on OpenAlexaff
Sebastián Rolón, Oana Balmau

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Storage Technologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsOperating systemComputer scienceVirtualizationVirtual machineCloud computingEmbedded systemProcess (computing)BootingPCI ExpressHardware virtualizationFull virtualizationField-programmable gate array

Abstract

fetched live from OpenAlex

It is a critical concern for cloud computing providers to identify and adopt optimal virtual machine (VM) I/O storage paths. In Linux, these involve NVMe hardware, PCIe passthrough with vfio, and userspace drivers (e.g. SPDK). One project tying together these host-side improvements with the guests is vfio-user, a library and protocol for inter-process communication of I/O operations over sockets. QEMU/KVM VMs using vfio-user and SPDK are theoretically expected to attain low latencies due to mostly userspace processing, but this VM datapath configuration is still experimental and performance data is lacking in the literature.

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 categoriesMeta-epidemiology (narrow)
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 score0.697
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0000.004
Open science0.0020.001
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 head0.018
GPT teacher head0.253
Teacher spread0.235 · 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.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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
Published2023
Admission routes1
Has abstractyes

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