Towards software-adaptive green computing based on server power consumption
Bibliographic record
Abstract
With the proliferation of virtualization and cloud comput- ing, optimizing the power usage effectiveness of enterprise data centers has become a laudable goal and a critical re- quirement in IT operations all over the world. While a sig- nificant body of research exists to measure, monitor, and control the greenness level of hardware components, signif- icant research efforts are needed to relate hardware energy consumption to energy consumption due to program exe- cution. In this paper we report on our investigations to characterize power consumption profiles for different types of compute and memory intensive software applications. In particular, we focus on studying the effects of CPU loads on the power consumption of compute servers by monitoring rack power consumption in a data center. We conducted a series of experiments with a variety of processes of differ- ent complexity to understand and characterize the effect on power consumption. Combining processes of varying com- plexity with varying resource allocations produces different energy consumption levels. The challenge is to optimize pro- cess orchestration based on a power consumption framework to accrue energy savings. Our ultimate goal is to develop smart adaptive green computing techniques, such as adap- tive job scheduling and resource provisioning, to reduce over- all power consumption in data centers or clouds.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 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".