A biogeography-based optimization algorithm for energy efficient virtual machine placement
Bibliographic record
Abstract
Recently, high levels of energy consumption in datacenters has become a concern not only due to operational costs, but also due to adverse effects on the environment (i.e., carbon emission, climate change, etc.) Virtualization technology can provide better management of physical servers/machines (PM) and may help reduce power consumption. The purpose of this study is to minimize the total energy consumption through good virtual machine (VM) placement. The VM placement problem has a large search space. Finding an optimal solution of such problems using an exhaustive search is impractical. Heuristic algorithms can provide high-quality solutions with limited computing resources in acceptable time. Evolutionary Algorithms (EAs) can be considered as heuristic tools that can provide high-quality solutions to this type of problems. We propose a Biogeography Based Optimization (BBO) Algorithm for energy-efficient VM placement. We compare the BBO results with the Genetic Algorithm (GA). Overall, simulation results show that BBO outperforms GA.
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 | 0.000 | 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".