C <scp>o</scp> -A <scp>pproximator</scp> : Enabling Performance Prediction in Colocated Applications.
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
Today’s Internet of Things (IoT) devices can colocate multiple applications on a platform with hardware resource sharing. Such colocations allow for increasing the throughput of contemporary IoT applications, similar to the use of multi-tenancy in clouds. However, avoiding performance interference among colocated applications through virtualized performance isolation is expensive in IoT platforms due to resource limitations. Hence, on the one hand, colocated IoT applications without performance isolation contend for shared limited resources, which makes their performance variance discontinuous and a priori unknown. On the other hand, different combinations of colocated applications make the overall state space exceedingly large. All of these make such colocated routines challenging to predict, making it difficult to plan which applications to colocate on which platform. We propose Co - Approximator , a technique for systematically sampling an exponentially large colocated application state space and efficiently approximating it from only four available complete colocation samples. We demonstrate the performance of Co - Approximator with 17 standard benchmarks and three pipelined data processing applications on different IoT platforms, where on average, Co - Approximator reduces existing techniques’ approximation error from 61% to just 7%.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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