Analysis and Comparison of Live Virtual Machine Migration Methods
Bibliographic record
Abstract
With the huge development of servers' virtualization technology, the importance of live virtual machines (VM) migration has increased in order to improve hardware utilization, increase the power efficiency of data centers, and to decrease the effect of data centers failures and service downtime during the maintenance period. Live VM migration is the process of copying the CPU state and the memory and disk states of a VM from a hosting physical server to another destination server at the same data center, or to another data center connected to the hosting data center over LAN or WAN networks, with the least service downtime, in order not to affect the Quality of Experience (QoE) of virtualization service users. After the VM is transferred to the destination host and before it resumes running there, network traffic should be redirected to the new VM's location. In this paper, we analyze and compare the three migration methods: the Pre-copy, the Post-copy, and the Hybrid-copy method. We explain the mathematical model of the Pre-copy method as available in the literature. We present our mathematical models for Post-copy and Hybrid-copy methods for the migration downtime, total migration time, and total transferred data during the migration process. We implement the three models using MATLAB and evaluate the performance of these migration methods for different experimental parameters. Based on the performance we propose which method is suitable for live migration of virtual machines.
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 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".