Cost-Efficient Remote Backup Services for Enterprise Clouds
Bibliographic record
Abstract
In order to efficiently achieve fault tolerance in cloud computing, large-scale data centers generally leverage remote backups to improve system reliability. Due to long-distance and expensive network transmission, the backups incur heavy communication overheads and potential errors. To address this important problem, we propose an efficient remote communication service, called Neptune. Neptune efficiently transmits massive data between long-distance data centers via a cost-effective filtration scheme. The filtration in Neptune is interpreted as eliminating redundancy and compressing similarity of files, which are generally studied independently in the existing work. In order to bridge their gap, Neptune leverages chunk-level deduplication to eliminate duplicate files and uses approximate delta compression to compress similar files. Moreover, in order to reduce the complexity and overheads, Neptune uses a locality-aware hashing to group similar files and proposes shortcut delta chains for fast remote recovery. We have implemented Neptune between two data centers and their distance is more than 1200 km via a 2 Mb/s network link. We examine the Neptune performance using real-world traces of Los Alamos National Laboratory (LANL), EMC, and Campus collection. Compared with state-of-the-art work, experimental results demonstrate the efficiency and efficacy of Neptune.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".