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Record W2504040774

Qos-routing for group communication

2004· article· en· W2504040774 on OpenAlexaff
Hossam S. Hassanein, A. Karaman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Agent-Based Network Management
Canadian institutionsQueen's University
Fundersnot available
KeywordsComputer scienceDistributed computingQuality of serviceRouting (electronic design automation)Span (engineering)Core (optical fiber)Modular designComputational complexity theorySelection (genetic algorithm)Software deploymentDomain (mathematical analysis)Component (thermodynamics)Tree (set theory)Computer networkAlgorithmTelecommunicationsEngineeringMathematicsArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

Quality of Services (QoS) routing for group communications on Internet is mainly concerned with the problem of minimization of total cost of delivery while meeting a given end-to-end delay-bound between source-receiver pairs. The core-based approach in multipoint communication enhances potential solutions in terms of QoS-efficiency and feasibility of the results in inter and intra-domain routing. In this thesis, we first analyze the solution space for constrained multipoint communication problems under the core-based approach. We show that the range of solutions examined by the models proposed to date is restricted to a subset of the entire solution space, which limits the potential efficiency of the results. We propose SPAN, a core-based framework processing on our identified extended solution space for constrained multi-source group applications. SPAN consists of core selection and tree construction as two modular components complimenting one another to achieve more efficient solutions in distributed processing. SPAN is also asymmetric, hence potentially operates in domains in which link weights are not necessarily equal in both directions. We analyze the computational and message complexity of our framework and show its feasibility for distributed deployment. Our evaluations show that SPAN consistently outperforms its counterparts in the literature. We further extended the core selection component of SPAN and present two algorithms for core selection both of which has the potential to enhance the performance of SPAN and further improve the QoS-efficiency of the solutions.

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: Methods · Consensus signal: none
Teacher disagreement score0.730
Threshold uncertainty score0.251

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 head0.018
GPT teacher head0.243
Teacher spread0.225 · 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
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

Citations0
Published2004
Admission routes1
Has abstractyes

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