Partial Granger causality-based feature selection algorithm for workload prediction in cloud systems
Bibliographic record
Abstract
The development of the cloud technology led to the rising interest in multi/hybrid cloud and emergence of artificial intelligence for IT operations(AIOps). The core elements of AIOps involve predicting how each metric of the data center will change in the future. Compared to general multivariate time-series prediction problems, causal relationships between each variable have a significant impact on cloud metric prediction. This paper focuses on the causality between variables, which partially arises when a specific event occurs in a cloud data center to achieve good predictive performance. The proposed model detects partial causality and sums it up again to extract key variables that explain the target variable well. Through this, variables with higher predictive performance than existing methods were found. We also propose a structure that improves the performance of the prediction model and minimizes inference time through a variable selection technique based on partial causality. By applying this to the actual operating cloud environment, it was proved that it can be effectively applied to the real world.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".