PERFORMANCE EVALUATION OF META-HEURSITCS IN ENERGY AWARE REAL-TIME SCHEDULING PROBLEMS
Bibliographic record
Abstract
Energy efficient real-time systems has been a prime concern in the last few years. Techniques on all levels of system design are being developed to reduce energy consumption. On the physical level, new fabrication technologies attempt to minimise overall chipset power. At the system design level, technologies such as Dynamic Voltage and Frequency Scaling (DVFS) and Dynamic Power Management (DPM) allow for changing the processor frequency on-the-fly or go into sleep modes to minimise operational power. At the operating system level, energy-efficient scheduling utilises DVFS and DPM at the task level to achieve further energy savings. Most energy-efficient scheduling research efforts focused on reducing processor power. Recently, system-wide solutions have been investigated. In this work, we extend on the previous work by adapting two evolutionary algorithms for system-wide energy minimisation. We analyse the performance of our algorithms under variable initial conditions. We further show that our meta-heuristics improve on previous work and are three times more likely to reach near-optimal energy savings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".