Leveraging machine learning and feature engineering for optimal data-driven scaling decision in serverless computing
Bibliographic record
Abstract
Serverless computing offers scalability and cost-efficiency, but balancing performance and cost remains challenging, particularly in scaling decisions that can lead to cold starts or resource misallocation. This research is motivated by the need to minimize the impact of cold starts and optimize resource utilization in serverless applications by developing intelligent, data-driven scaling decisions. We delve into using machine learning and feature engineering to model and simulate predictions for optimal scaling decisions for Azure Function Apps (AFA). Our focus lies in predicting the ideal timing for provisioning or de-provisioning the Function App’s environment. Using historical invocation data, we applied a sliding window to transform the time-series data into patterns categorized as load or unload classes, considering various target periods. To identify the most effective model, we compared the performance of various baseline models with and without calibration (isotonic and sigmoid) to enhance precision. In addition, we assess multiple feature extraction methods in invocation patterns and explore the use of Principal Component Analysis (PCA) for dimensionality reduction to reduce computation costs. Using the best-identified configurations, we model and simulate the class patterns over time to compare the actual classes with the predicted ones, focusing on memory usage and the costs associated with cold starts. The proposed model is thoroughly evaluated using various metrics under different setups, revealing notable improvements in scaling decisions achieved by applying calibration and feature engineering methods. These findings demonstrate the potential of machine learning for intelligent, data-driven scaling decisions in serverless computing, offering valuable insights for cloud providers to optimize resource allocation and for developers to build more efficient and responsive serverless applications. Specifically, the proposed method can be integrated into serverless platforms to automatically adjust resource provisioning based on predicted workload demands, reducing cold start latency and improving cost-effectiveness.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".