Community-Oriented Edge Computing Platform
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.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".