Using feedback control to manage QoS for clusters of servers providing service differentiation
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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".