Node.js scalability investigation in the cloud
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Node.js has gained popularity in cloud development due to its asynchronous, non-blocking and event-driven nature. However, scalability issues can limit the number of concurrent requests while achieving an acceptable level of performance. To the best of our knowledge, no cloud-based benchmarks or metrics focusing on Node.js scalability exist. This paper presents the design and implementation of Ibenchjs, a scalability-oriented benchmarking framework, and a set of sample test applications. We deploy Ibenchjs in a local and isolated cloud to collect and report scalability-related measurements and issues of Node.js as well as performance bottlenecks. Our findings include: 1) the scaling performance of the tested Node.js test applications was sub-linear; 2) no improvements were measured when more CPUs were added without modifying the number of Node.js instances; and 3) leveraging cloud scaling solutions significantly outperformed Node.js-module-based scaling.
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.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 it