Utilizing Minecraft bots to optimize game server performance and deployment
Bibliographic record
Abstract
To simulate a realistic game server environment, we utilized open source software libraries to create automated players (bots) for the globally renowned online game: Minecraft. The fairly simple design of the Minecraft server as well as its massive development and support community facilitates considerable research and analysis prospects. As such, the goal of our investigation was to emulate and then analyze the real-world stress that game-players actively create on hosting servers. We achieved this through creating scripted movements of Minecraft characters that are connected to the Minecraft server(s) hosted within our virtual infrastructure. After this was achieved, we explored altering the methods of running the active Minecraft servers to control CPU load; we primarily explored manually setting the CPU affinity of the Minecraft server thread to run on specific virtual cores. Collecting CPU workload data while the bots were running around on our servers gave us consistent and predictable readings that confirmed the success of our methods we used to control performance. Evidence of this is illustrated through the use of graphs and other experimental data outlined in the body of this document.
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 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 | 0.001 |
| 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".