Bibliographic record
Abstract
This paper presents VMFlockMS, a migration service optimized for cross-datacenter transfer and instantiation of groups of virtual machine (VM) images that comprise an application-level solution (e.g., a three-tier web application). We dub these groups of related VM images VMFlocks. VMFlockMS employs two main techniques: first, data deduplication within the VMFlock to be migrated and between the VMFlock and the data already present at the destination datacenter, and, second, accelerated instantiation of the application at the target datacenter after transferring only a partial set of data blocks and prioritization of the remaining data based on previously observed access patterns originating from the running VMs. VMFlockMS is designed to be deployed as a set of virtual appliances which make efficient use of the available cloud resources to locally access and deduplicate the images and data in a distributed fashion with minimal requirements imposed on the cloud API to access the VM image repository. VMFlockMS provides an incrementally scalable and high-performance migration service. Our evaluation shows that VMFlockMS can reduce the data volumes to be transferred over the network to as low as 3% of the original VMFlock size, enables the complete transfer of the VM images belonging to a VMFlock over transcontinental link up to 3.5x faster than alternative approaches, and enables booting these VM images with as little as 5% of the compressed VMFlock data available at the destination.
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 | 0.001 | 0.003 |
| 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 | 0.002 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".