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Record W2014161917 · doi:10.1109/3pgcic.2012.45

Multi-server MMO Middleware: Unlocked

2012· article· en· W2014161917 on OpenAlexafffund
Christopher Mcknight, C. Stubens, Yvonne Coady, Kin Fun Li

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicPeer-to-Peer Network Technologies
Canadian institutionsUniversity of Victoria
FundersUniversity of Victoria
KeywordsServerComputer scienceOverhead (engineering)Middleware (distributed applications)Consistency (knowledge bases)Distributed computingSynchronization (alternating current)Computer networkClientClient–server modelOperating system

Abstract

fetched live from OpenAlex

Massively multiplayer online (MMO) games consist of huge worlds populated by thousands of clients, far beyond the ability of a single server to maintain. This naturally leads to the division of the game world into many partitions, each controlled by a single server. To provide players with the illusion of a single large world, some MMO systems divide their game world across servers and synchronize all nearby activity between them. To accomplish and synchronize player movement and interaction between servers, a system of inter-server locks are employed. These locks introduce considerable software complexity and network traffic overhead. We discuss the practical problems of such locking systems, and present an alternate approach. A lockless middleware design is described which implements sequential consistency using inter-server remote writes. This approach provides consistency across all clients and servers, allowing for seamless movement of clients within an arbitrarily large game world. We compare the lockless architecture to the traditional locking design, and analyze its performance.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.480
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.268
Teacher spread0.224 · 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 designNot applicable
Domainnot available
GenreMethods

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
Published2012
Admission routes2
Has abstractyes

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