Uniform and Non-Uniform Zoning for Load Balancing in Virtual Environments
Bibliographic record
Abstract
Maintaining a stable system for thousands of users is a difficult task. Massively Multiplayer Online Game (MMOG) dealing thousands of players preserves consistency by sharing relevant game states among the interacting parties. The lag of state sharing becomes excessive when the system is overloaded. Current practices supporting a massive number of users generally divide the game world into zones which are managed by servers. However, such zoning restricts cross-zonal interactions and exposes division of the game space. To address these problems, we present load-balancing algorithms for both uniform and non-uniform zonal Peer-to-Peer (P2P) MMOGs. The proposed load-balancing schemes identify a loaded server in terms of either the number of players or packets processed per unit time, and then move the load to other servers considering communication overhead and P2P overlay restructuring. The non-uniform load balancing, named adaptive scheme, uses a bisection procedure that does not adhere to any predefined zone size - zone sizes are flexible and can be determined dynamically. The outlined Multilevel Multiphase Load Balancing (MMLB) method designed for uniform zones reduces load in a step-by-step manner, and avoids problems associated with current load balancing schemes. Our results show that reducing load at any magnitude not necessarily improves performance. By comparison, MMLB performs better than adaptive scheme especially for a series of hotspots.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".