SyRaFa: Synchronous Rate and Frequency Adjustment for Utilization Control in Distributed Real-Time Embedded Systems
Bibliographic record
Abstract
To efficiently utilize the computing resources and provide good quality of service (QoS) to the end-to-end tasks in the distributed real-time systems, we can enforce the utilization bounds on multiple processors. The utilization control is challenging especially when the workload in the system is unpredictable. To handle the workload uncertainties, current research favors feedback control techniques, and recent work combines the task rate adaptation and processor frequency scaling in an asynchronous way for CPU utilization control, where task rates and the processor frequencies are tuned asynchronously in two decoupled control loops for control convenience. Since the two manipulated variables, task rates and processor frequencies, contribute to the CPU utilizations together with strong coupling, adjusting them asynchronously may degrade the utilization control performance. In this paper, we provide a novel scheme to make synchronous rate and frequency adjustment to enforce the utilization setpoint, referred to as SyRaFa scheme. SyRaFa can handle the workload uncertainties by identifying the system model online and can simultaneously adjust the manipulated variables by solving an optimization problem in each sampling period. Extensive evaluation results demonstrate SyRaFa outperforms the existing schemes especially under severe workload uncertainties.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".