On the Impact of Reliable Protocols on Run-Time Software System Performance Predictability
Bibliographic record
Abstract
This paper presents the results of OMNet++ based simulations that explore the statistical effects reliable protocols have on software system run-time performance predictability. Real-world relevance is addressed by focusing the behaviors generated via the simulation of a real-world industry-held in-production Large-scale Distributed Software System (LDSS). The impacts of reliable and non-reliable protocols contrasted through simulating the same LDSS under statistically identical service workloads and deployment regimes. In this manner, it is shown that as event recovery actions of reliable protocols are triggered, there is a substantial decrease in the LDSS's runtime performance predictability. This somewhat counter-intuitive result arises due to reliable protocols inherently generating additional workloads to be serviced into LDSS systems already experiencing the onsets of overload conditions. The presented work qualifies these impacts while highlighting easily measurable production indicators of when performance predictability has been lost.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".