C <scp>o</scp> -A <scp>pproximator</scp> : Enabling Performance Prediction in Colocated Applications.
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%.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".