Community-Oriented Edge Computing Platform
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
Democratizing the edge by capitalizing the underutilized computational resources of end devices, referred to as Extreme Edge Devices (EEDs), can foster various IoT applications. In this paper, we propose the Community Edge Platform (CEP). CEP is the first platform that exploits business, institutional, and social relationships to build communities of requesters and EEDs to eliminate recruitment costs and preserve privacy in EED-enabled environments. CEP promotes service-for-service exchange and utilizes a hierarchical control paradigm to prioritize the enrollment of nearby devices as workers. CEP also considers the fact that community-imposed constraints can lead to unbalanced work distribution. To alleviate this issue, we propose the Community-Oriented Resource Allocation (CORA) scheme. CORA accounts for community restrictions and strives to minimize the execution time and makespan while retaining a reasonable scheduler runtime. Towards that end, we formulate the resource allocation problem as a Bipartite Graph Matching problem. Comprehensive qualitative evaluations demonstrate the superiority of CEP compared to 12 prominent edge computing platforms in terms of various system architecture and performance features. Additionally, extensive simulations show that CORA outperforms six prominent resource allocation schemes by up to 44% and 7% in terms of makespan and execution time, respectively, while achieving a much faster runtime, outperforming the best of the six baseline resource allocation schemes by a factor of six.
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 | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.008 | 0.009 |
| 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