Using file-grain connectivity to implement a peer-to-peer file system
Bibliographic record
Abstract
Recent work has demonstrated a peer-to-peer storage system that locates data objects using O(logN) messages by placing objects on nodes according to pseudo-randomly chosen IDs. While elegant, this approach constrains system functionality and flexibility: files are immutable, directories and symbolic names are not supported, data location is fixed, and access locality is not exploited. This paper presents Mammoth, a peer-to-peer hierarchical file system that, unlike alternative approaches, supports a traditional file-system API, allows files and directories to be stored on any node, and adapts storage location to exploit locality, balance load, and ensure availability. Our approach handles all coordination at the granularity of files instead of nodes. In effect, the nodes that store a particular file act as its server independently of other nodes in the system. The resulting system is highly available and robust to failure. Our experiments with our prototype have yielded good results, but an important question remains: how the system will perform on a massive scale. We discuss the key issues, some of which we have addressed and others that remain open.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| 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".