A Distributed Application-Level IT System Workload Generator
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
Developing a capacity to test distributed systems hinges on being able to generate the workloads that these systems are to process. Appropriate tools must not only generate these workloads in real-time, but must also be able to sweep through a range of possible workload characteristics to support sensitivity and robustness analyses. Currently, the majority of prior work in this area, including Harpoon, ns-2, OpNet, and tcp replay, has focused on the reproduction of workload traces at the network-level. However, for many distributed systems, reproducing application-level workload characteristics is more informative from a testing perspective. This work details such an application-level workload generation tool. The tool itself is distributed and, hence, easily scales to using multiple machines to re-create complex multi-homed workloads. Furthermore, the tool supports the standard abilities to produce both statistically-described workloads, as well as reinstantiating previously-captured workload traces.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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