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
Edge Computing (EC) is a promising computing paradigm that can foster a wide spectrum of delay-sensitive and/or data-intensive applications. As opposed to cloud computing, which relies on remote cloud servers, EC brings the computing service closer to the end-users, which can significantly reduce the delay. The concept of EC has recently expanded to include harvesting the computation resources of the Extreme Edge Devices (EEDs), such as smartphones, autonomous vehicles, tablets, etc. However, the cost of recruiting EEDs for resource allocation in such EC environments is mostly overlooked. In this paper, we propose the Price-based Compute Clusters Recruitment (PCCR) scheme. In PCCR, we minimize the cost of recruiting the EEDs required to perform a given set of tasks, where each task is satisfied by the collaborative effort of a group of EEDs forming a compute cluster. PCCR strives to minimize the total recruitment cost while keeping the delay below a certain threshold by forming the optimal set of compute clusters from a pool of heterogeneous EEDs available in a given geographical area. We formulate the optimization problem as a Mixed Integer Quadratically Constrained Quadratic Program (MIQCQP). We then derive an analytical solution using the KKT conditions and Lagrangian analysis. Extensive simulations show that PCCR significantly outperforms a prominent baseline approach in terms of recruitment cost.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.002 |
| 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