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Record W2245309906 · doi:10.1109/cscloud.2015.33

Tunable Performance and Consistency Tradeoffs for Geographically Replicated Cloud Services (COLOR)

2015· article· en· W2245309906 on OpenAlexaff
Wenbo Zhu, Murray Woodside

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDistributed systems and fault tolerance
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer scienceCloud computingReplication (statistics)Consistency modelLatency (audio)Eventual consistencyDistributed computingConsistency (knowledge bases)CacheCache coherenceCausal consistencyConcurrency controlWeak consistencyReplicaStrong consistencyOverhead (engineering)Computer networkCloud storageData consistencySequential consistencyDatabaseOperating systemCPU cacheCache algorithmsDatabase transaction

Abstract

fetched live from OpenAlex

COLOR (client-oriented layered optimistic replication) is a combination of optimistic and conservative data replication that allows cloud services to be replicated across widely distributed locations without suffering from the latency overhead of strict algorithms, and with quantifiable and controllable tradeoffs between performance and consistency guarantees. The COLOR solution adopts a layered approach to enable optimistic delivery of client messages on top of any existing storage layer that manages the strict replication of the cloud service. When clients may be temporarily exposed to inconsistent states due to replication failures, such inconsistency is made recoverable similar to "optimistic concurrency control" for clients that cache the server state. COLOR supports different numeric parameters to trade the strict consistency for better performance to possibly match Eventual Consistency, while the end-to-end consistency is always guaranteed as the storage layer will never deliver any client messages generated from inconsistent states.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.740
Threshold uncertainty score0.457

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 science0.0010.000
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 head0.022
GPT teacher head0.234
Teacher spread0.212 · 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 designTheoretical or conceptual
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

Citations0
Published2015
Admission routes1
Has abstractyes

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