Comparing Auto Scaling Efficiency of Serverless Applications Using AWS Lambda and Azure Functions in E-Commerce Platforms
Bibliographic record
Abstract
Serverless computing emerged as a revolutionary solution in e-commerce processes to create applications with scalable functionality alongside affordable costs and high responsiveness in this dynamic environment. Relevant research determines how auto scaling operations perform between established Function-as-a-Service (FaaS) providers AWS Lambda and Azure Functions when deployed to handle real e-commerce business requirements. The evaluation of both FaaS platforms for core functions like cart management and inventory updates and payment processing features contains identical microservices through tests that determine key measures consisting of warm-up delays and operational expenses and throughput speed and response times under load conditions. Results show detailed strengths together with weaknesses which exist within each individual platform. AWS Lambda maintains low latencies because it has a mature tool set yet Azure Functions achieves superior scalability as well as perfect integration between its products. The specified empirical framework enables system architects to decide between serverless computing platforms by considering particular use-case requirements. This research establishes that organizations must select their cloud platforms according to their operational demands as well as their workload requirements when operating in e-commerce settings.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".