Neural Network Based Regression Model for Virtual Machines Migration Method Selection
Bibliographic record
Abstract
Live virtual machines migration has been one of the main strategies in cloud systems management to efficiently utilize data centers resources, reduce power consumption and unutilized resources in data centers, as well as providing the least interruption to customers in the events of migrating virtual machines or their data between different hosts in the same or different data centers. Many migration methods, with different characteristics, have been proposed to migrate virtual machines. Pre-copy and Post-copy are the main classical migration methods that transfer VMs and their memory between different hosts. The main VMs migration performance metrics include the total migration time, downtime, and amount of transmitted data. Machine learning algorithms have been widely used to make systems intelligent. Neural Network is one of the key machine learning algorithms used for classification and regression. In this paper, we propose neural network-based adaptive models that predict the key performance metrics of VM migration for Pre-copy and Post-copy methods, and for different application workload types running over the virtual machine. Based on the prediction, one of the two migration methods can be selected to migrate a specific virtual machine.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".