Rocky: Replicating Block Devices for Tamper and Failure Resistant Edge-based Virtualized Desktop Infrastructure
Bibliographic record
Abstract
Recently, edge-based virtual desktop infrastructure (EdgeVDI), which brings the power of virtualized desktop infrastructure to cloudlets closer to users, has been considered as an attractive solution for WAN mobility. However, ransomware and wiper malware are becoming more and more prevalent, which can impose serious cybersecurity threats to EdgeVDI users. Existing tamper-resistant solutions cannot deal with cloudlet failures. In this paper, we propose Rocky, the first distributed replicated block device for EdgeVDI that can recover from tampering attacks and failures. The key enabler is replicating to store a consistent write sequence across cloudlets as an append-only immutable mutation history. In addition, Rocky uses a replication broker to allow heterogenous cloudlets to control replication rates at their pace and reduces both disk space and network bandwidth consumption by coalescing writes for both uplink and downlink. To show the feasibility of Rocky, we implemented Rocky in Java. The experimental results show that Rocky’s write and read throughputs are similar to those of a baseline device with 8.4% and 11.9% additional overheads, respectively. In addition, we could reduce repeated writes by 88.5% and 100% for editing presentation slides and a photo, respectively.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".