Optimisation of energy consumption in cloud video surveillance centre based on monitoring and placement of virtual machines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cloud computing is one of the most popular computational models, which requires plenty of physical devices where services are provided based on user demand. A majority of data centres need plenty of energy consumption which has become a challenge in recent years. Regarding cloud video surveillance - as a contemporary research field of cloud computing and big data, the service, due to the high demand for monitoring remote places, continually consumes surplus energy to process the high-volume data. This study considers the importance of energy consumption in cloud video surveillance, and it has been tried to increase the efficiency of servers concerning energy usage. The proposed method employs virtual machine placement in two steps, including monitoring and placement, to reduce energy consumption and increase the efficiency of servers. Implementation results in Cloudsim showed that it reduces energy consumption and increases resource efficiency.
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.
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 | 0.001 | 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 it