Predictive Worker Resource Characterization at the Extreme Edge
Bibliographic record
Abstract
Extreme Edge Computing (EEC) promises to enhance the Quality of Service of data-intensive and delay-critical IoT applications. Deploying EEC is challenging due to reliance on user-owned Extreme Edge Devices (EEDs) with heterogeneous computational and communication capabilities and dynamic user behaviors causing resource volatility. This volatility complicates the accurate estimation of EED computational capabilities, a critical step for efcient task allocation in EEC systems. Previous research characterizes EED capabilities simplistically by allocating benchmark tasks without accounting for resource volatility, using insufcient performance indicators, and reactively adjusting EEC task allocation only after volatility impacts system operations. In this thesis, we propose a comprehensive framework for predictively characterizing the computational resources of EEDs according to their dynamic resource usage to address this challenge. The framework consists of four key components that: 1) emulate dynamic user-access behaviors and generate resource usage data, 2) forecast resource usage states efciently over multi-step horizons, 3) allocate benchmark tasks based on predicted states to characterize EED capabilities, and 4) dynamically allocate EEC tasks based on adaptive worker characterization. Extensive experiments on a realistic EED testbed with an interactive monitoring dashboard demonstrate our framework’s efcacy in improving EEC task allocation performance, reducing execution latency, and increasing throughput without overloading device resources, compared to prominent existing schemes.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".