The Analysis of Time Series Forecasting on Resource Provision of Cloud-based Game Servers
Bibliographic record
Abstract
The server workloads of large-scale online video games are elastic and on-demand. The workload can range from tens to thousands of server instances in short periods. In fact, a cloud-based video game ecosystem can reach a workload of millions of players every week. Given such a large scale, even a small portion of over-provisioning leads to a significant amount of resource idling and a high cost of waste. It is essential to define an effective forecasting model on the game session workloads given a time span. The effectiveness shall be measured by metrics representing Service Level Objectives (SLOs). In this work, we analyze time series forecasting models using ARIMA, Prophet, and LSTM to predict the number of virtual machines in need of cloud resource monitoring data. In addition, we define service-level metrics for measuring effectiveness based on factors of over/under provision and ratio of resource waste. We analyze models with 16 fleets with an average of 2754 game servers over a four-month-long period of time in the production environment. We observe that our LSTM model is the most accurate in forecasting the demand of virtual machines in terms of RMSE and MAE. Further analysis using metrics of SLOs, we observe that the LSTM model leads to more cases of under-provisioning than ARIMA and Prophet do. The LSTM model forecasts the demand of virtual machines with less over-provision ratio than ARIMA and Prophet do for 14 out of 16 fleets. Using the LSTM model, we further evaluate the forecasting effect across different time spans of a single fleet and across multiple fleets within the same time span.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".