<i>GAS</i> : DVFS-Driven Energy Efficiency Approach for Latency-Guaranteed Edge Computing Microservices
Why this work is in the frame
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Bibliographic record
Abstract
Edge computing-based microservices (ECM) are pivotal infrastructure components for latency-critical applications such as Virtual Reality/Augmented Reality (VR/AR) and the Internet of Things (IoT). ECM involves strategically deploying microservices at the network’s edge to fulfill the low latency needs of modern applications. However, achieving efficient resource and energy consumption while meeting the latency requirement in the ECM environment remains challenging. Dynamic Voltage and Frequency Scaling (DVFS) is a common technique to address this issue. It adjusts the CPU frequency and voltage to balance energy cost and performance. However, selecting the optimal CPU frequency depends on the nature of the microservice workload (e.g., CPU-bound, memory-bound, or mixed). Moreover, various microservices with different latency requirement can be deployed on the same edge node. This makes the DVFS application extremely challenging, particularly for a chip-wide DVFS implementation for which CPU cores operate at the same frequency and voltage. To this end, we propose GAS, enerGy Aware microServices edge computing framework, which enables CPU frequency scaling to meet diverse microservice latency requirement with the minimum energy cost. Our evaluation indicates that our CPU scaling policy decreases energy consumption by 5% to 23% compared to Linux governors while maintaining latency requirement and significantly contributing to sustainable edge computing.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it