Analytic performance estimation of client-server systems with multi-threaded clients
Bibliographic record
Abstract
The authors present an analytical performance model named rendezvous network with multi-threaded clients (RNMTC) for performance analysis of client-server systems. RNMTC is able to model systems with multiple clients inter-communicating with multiple servers which may represent either hardware or software system components. Each system client is described by a precedence graph, and may consist of multiple concurrent execution threads whose number can vary due to fork and join operations. The analytic method for RNMTC proposed is based on hierarchical decomposition: at the higher level the system behaviour is represented by a Markov chain (MC) model whose states correspond to all possible combinations of client execution states; at the lower level a stochastic rendezvous network (SRVN) model with simple clients corresponds to each MC slate. SRVN was previously introduced and MVA approximate analytic solutions are known. The RNMTC model has been used with a number of different test cases and the analytic results were found to be in close agreement with simulation results.>
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".