Energy Efficient Scheduling and Management for Large-Scale Services Computing Systems
Why this work is in the frame
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Bibliographic record
Abstract
With the increasing popularity of services published online, energy consumption of services computing systems is growing dramatically. Besides Quality of Service (QoS), energy efficiency has become an important issue and drawn significant attention. However, energy efficient request scheduling and service management for large-scale services computing systems face challenges because of the high dynamics and unpredictability of request arrivals. In this paper, we jointly consider the conflicting metrics of performance, queue congestion and energy consumption. We propose a distributed online scheduling and management algorithm which does not require any priori statistical knowledge of request arrivals. Mathematical analysis is conducted which demonstrates that our algorithm can achieve arbitrary tradeoff between performance and energy efficiency. Numerical and real trace data based experiments are carried out to validate the effectiveness of our algorithm in optimizing energy efficiency while stabilizing the system.
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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 | 0.001 | 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 | 0.001 | 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