Applying database replication to multi-player online games
Bibliographic record
Abstract
Multi-player Online Games (MOGs) have emerged as popular data intensive applications in recent years. Being used by many players simultaneously, they require a high degree of fault tolerance, scalability and performance. In this paper we analyze how database replication can be used in MOGs to achieve these goals. In data replication, clients can read data from any database replica while updates have to be executed at all available replicas. Thus, reads can be distributed among the replicas leading to reduced response time and scalability. Furthermore, the system is fault-tolerant as long as a replica is available. However, we are not aware of any previous study on the application of database replication to MOG. In this paper, we present a system, MiddleSIR, which provides database replication support. We illustrate different replication protocols implemented in the system along an example, explaining how data consistency and fault tolerance can be achieved. From there, we design a small multi-player typing game to demonstrate how to apply database replication to MOG. We will discuss how different replication protocols affect the semantics of the game. Our experiments show that database replication can provide good scalability and performance in both Local Area Networks (LAN) and Wide Area Networks (WAN).
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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 | 0.001 |
| 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".