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Record W2017139832 · doi:10.1145/944747.944754

A study of iSCSI extensions for RDMA (iSER)

2003· article· en· W2017139832 on OpenAlexaff
M. Chadalapaka, Hemal Shah, Uri Elzur, Patricia Thaler, Michael Ko

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Storage Technologies
Canadian institutionsHewlett-Packard (Canada)
Fundersnot available
KeywordsiSCSIRemote direct memory accessComputer scienceComputer networkInternet protocol suiteOperating systemProtocol (science)SCSIEmbedded systemFibre ChannelComputer data storageThe Internet

Abstract

fetched live from OpenAlex

The iSCSI protocol is the IETF standard that maps the SCSI family of application protocols onto TCP/IP enabling convergence of storage traffic on to standard TCP/IP fabrics. The ability to efficiently transfer and place the data on TCP/IP networks is crucial for this convergence of the storage traffic. The iWARP protocol suite provides Remote Direct Memory Access (RDMA) semantics over TCP/IP networks and enables efficient memory-to-memory data transfers over an IP fabric. This paper studies the design process of iSCSI Extensions for RDMA (iSER), a protocol that maps the iSCSI protocol over the iWARP protocol suite. As part of this study, this paper shows how iSER enables efficient data movement for iSCSI using generic RDMA hardware and then presents a discussion of the iWARP architectural features that were conceived during the iSER design. These features potentially enable highly efficient realizations of other I/O protocols as well.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.046
GPT teacher head0.304
Teacher spread0.258 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations27
Published2003
Admission routes1
Has abstractyes

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