Modeling the energy cost of applications on portable wireless devices
Bibliographic record
Abstract
Extending the battery life of portable wireless devices has been in the focus of researchers for close to a decade. Several energy management techniques have been investigated at different levels of system design -- starting from silicon at the bottom to application design at the top, with communication protocols and operating system in between. In this paper, we present a model to estimate the energy cost of an application running on a portable wireless device. To develop the cost model, we partition a wireless device into two components, namely, computation and communication. Each component is modeled by a state-transition diagram. Two attributes are associated with each state: an average power cost and a state residence time. The cost of each state of the state-transition diagrams is validated by actual measurements. For a constant voltage supply, the average power cost of a state is denoted by the average current drawn by the component. The state residence times are estimated from the behavior of applications. The cost model has been validated by performing actual measurement of energy cost. We find that the estimated cost and the actual energy cost are within 5-10% of each other. This study will help us in improving the design of energy efficient software for portable devices. Moreover, the energy consumption breakdown into components will be an essential guide for future research in energy management of hardware and software systems.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".