Disk access analysis for system performance optimization
Bibliographic record
Abstract
Abstract:- As the gap between processor and disk performance continues to grow in modern computing systems, so too does the need for improvements in disk performance management. In an effort to remove or reduce the performance bottleneck created by disk accesses, new approaches and algorithms for disk scheduling have been developed in recent years. While providing performance improvements, these approaches each have their own strengths and weaknesses that ultimately limit their applicability and usefulness across a wide variety of system workloads. This paper introduces a new method of disk access optimization which focuses primarily on dynamic scheduling algorithm selection and algorithm tuning. Disk activity is continuously collected in real-time and cached for later analysis to discover current system load patterns, while a scoring system is used to detect overall trends by system processes. Once analysis is complete, disk scheduling algorithms are automatically selected and/or tuned based on heuristics or criteria to ensure that the disk scheduling algorithm in use is well suited to the current workload of the system. Experimentation to date has been quite positive, demonstrating this approach has great potential for assisting in the optimization of system performance.
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 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 | 0.002 |
| 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 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".