VMBackup: an efficient framework for online virtual machine image backup and recovery
Bibliographic record
Abstract
Summary Although deduplication can reduce data volume for backup, it pauses the running system for the purpose of data consistency. This problem becomes severe when the target data are Virtual Machine Image (VMI), the volume of which can scale up to several gigabytes. In this paper, we propose an online framework for VM image backup and recovery, called VMBackup, which comprises three major components: (1) Similarity Retrieval that indexes chunks' fingerprints by its segment id for fast identification, (2) one‐level File‐Index that efficiently tracks file id to its content chunks in a correct order, and (3) Adjacent Storage model that places adjacent chunks of an image in the same disk partition to maximize chunk locality. The experimental results show that (1) the images of one OS serial and the same custom can share high percentage of duplicated contents, (2) variable‐length chunk partitioning is superior to fixed‐length chunk partitioning for deduplication, and (3) VMBackup, in our environment, can provide 8M/s backup throughput and 9.5M/s recovery throughput, which are only 15% and 4% less than storage systems without deduplication. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".