Worker Resource Characterization Under Dynamic Usage in Multi-access Edge Computing
Bibliographic record
Abstract
Multi-access Edge Computing (MEC), also known as Mobile Edge Computing, has gained significant momentum as a key facilitator of the stringent Quality of Service (QoS) requirements associated with delay-sensitive and data-intensive applications. Recently, the advantageous nature of MEC has been further enriched by leveraging the latent yet underused computational resources of Extreme Edge Devices (EEDs), such as smartphones, tablets, and autonomous vehicles. However, EEDs are typically user-owned devices, and thus have dynamic resource usage behavior since users dynamically navigate through various applications on their devices. This, along with the heterogeneity of EEDs, makes it harder to accurately estimate their computational capabilities, drastically affecting task allocation and resource utilization, thus increasing the delay. In this paper, we propose the Usage-based WOrker Resource Characterization (U-WORC) scheme to alleviate this problem and address the issues related to device heterogeneity, resource contention, and network communication delay. U-WORC presents a prediction-based approach to characterize the resources of EEDs (i.e., workers) by clustering the resource usage information and the corresponding execution time while running a benchmark task. Performance evaluation shows that U-WORC yields significant improvements that reach 91.42 % and 38.8 % in terms of characterization accuracy and task execution time, respectively, compared to a prominent scheme that does not consider resource contention and network communication delay.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.004 | 0.010 |
| 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 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".