Scalability Analysis of the Hierarchical Architecture for Distributed Virtual Environments
Bibliographic record
Abstract
A distributed virtual environment (DVE) is a shared virtual environment where multiple users at their workstations interact with each other over a network. Some of these systems may support a large number of users, for example, multiplayer online games. An important issue is how well the system scales as the number of users increases. In terms of scalability, a promising system architecture is a two-level hierarchical architecture. At the lower level, multiple servers are deployed; each server interacts with its assigned users. At the higher level, the servers ensure that their copies of the virtual environment are as consistent as possible. Although the two-level architecture is believed to have good properties with respect to scalability, not much is known about its performance characteristics. In this paper, we develop a performance model for the two-level architecture and obtain analytic results on the workload experienced by each server. Our results provide valuable insights into the scalability of the architecture. We also investigate the issue of consistency and develop a novel technique to achieve weak consistency among copies of the virtual environment at the various servers. Simulation results on the consistency/scalability trade-off are presented.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".