A comprehensive analytical performance model for disk devices under random workloads
Why this work is in the frame
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Bibliographic record
Abstract
Our goal is to contribute a common theoretical framework for studying the performance of disk-storage devices. Understanding the performance behavior of these devices will allow prediction of the I/O cost in modern applications. Current disk technologies differ in terms of the fundamental modeling characteristics, which include the magnetic/optical nature, angular and linear velocities, storage capacities, and transfer rates. Angular and linear velocities, storage capacities, and transfer rates are made constant or variable in different existing disk products. Related work in this area has studied Constant Angular Velocity (CAV) magnetic disks and Constant Linear Velocity (CLV) optical disks. We present a comprehensive analytical model, validated through simulations, for the random retrieval performance of disk devices which takes into account all the above-mentioned fundamental characteristics and includes, as special cases, all the known disk-storage devices. Such an analytical model can be used, for example, in the query optimizer of large traditional databases as well as in an admission controller of multimedia storage servers. Besides the known models for magnetic CAV and optical CLV disks, our unifying model is also reducible to a model for a more recent disk technology, called zoned disks, the retrieval performance of which has not been modeled in detail before. The model can also be used to study the performance retrieval of possible future technologies which combine a number of the above characteristics and in environments containing different types of disks (e.g., magnetic-disk-based secondary storage and optical-disk-based tertiary storage). Using our model, we contribute an analysis of the performance behavior of zoned disks and we compare it against that for the traditional CAV disks, as well as against that of some possible/future technologies. This allows us to gain insights into the fundamental performance trade-offs.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 | 0.000 |
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