An analysis of live migration in openstack using high speed optical network
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
Virtualisation technology has become a very common trend in modern datacentres as Virtual Machine (VM) migration brings several benefits like improved performance, high manageability, resource consolidation and fault tolerance. Live Migration (LM) of VMs is used for transferring a working VM from one host to another host of a different physical machine without interfering with the existing VMs. However, little research has been done in considering the real time resource consumption and latency of live VM migration that reduces these benefits to much less than their potential. In this paper, we present an analysis of LM in our unique TransAtlantic high speed optical fibre network connecting Northern Ireland, Dublin and Halifax (Canada). We show that the total migration times as well as total network data transfer for post-copy LM are both dominated by specific VM memory patterns using loaded or unloaded VMs. We also found that the downtime for different VM memory patterns is not extremely varied and no severe effect is experienced over our long distance network.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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