Community-Oriented Resource Allocation at the Extreme Edge
Bibliographic record
Abstract
The surging demand for Edge Computing (EC) to cope with the proliferation of latency-critical and data-intensive applications has inspired the notion of recycling ample yet underutilized computational resources of end devices, also referred to as Extreme Edge Devices (EEDs). Maintaining data privacy and cost efficiency remain core challenges for the viability of EED-enabled computing paradigms. In this context, we propose the Community-Oriented Resource Allocation (CORA) scheme. CORA exploits business, institutional, and social relationships to build clusters and communities of requesters and EEDs that can eliminate recruitment costs and preserve privacy. However, community-imposed constraints on resource allocation can lead to unbalanced work distribution. To address this issue, CORA considers community restrictions, minimizes flowtime and makespan for the allocated services, and retains a reasonable scheduler runtime for real-time resource allocation. Towards that end, CORA formulates the resource allocation problem as a Bipartite Graph Matching problem. Furthermore, CORA exposes tuneable parameters that allow prioritizing flowtime or makespan, making it suitable for different scenarios. Extensive simulations show that CORA outperforms six prominent heuristic-based resource allocation schemes by up to 24% in terms of average makespan while sustaining the same level of flowtime and runtime.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".