Using feedback control to manage QoS for clusters of servers providing service differentiation
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
This paper considers the use of feedback to improve the performance of computing systems that offer differentiated services. The motivation of the work is based on the increasing demand on application servers. It is not always sufficient to buy high-performance software for the server. Multiple servers may be needed. To guarantee that QoS requirements are satisfied, it is possible to statically assign resources for a specific class. This often results in underutilization of resources. This paper describes a novel technique that is based on control theory principles applied to a server cluster that provides differentiated service. The paper shows that feedback can be used to adjust the number of client requests concurrently being processed based on dynamic information such as CPU utilization. The paper also compares the use of the proposed technique with a dynamic non control-theoretic approach that is not based on control theory principles. Results show a dramatic increase in the number of served users using the control theory principles compared with a non control-theoretic approach during the same experiment duration. The improvement provided by the proposed technique exceeded 20%.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 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