Optimization Design of Massive Data Storage System Based on Distributed Computing Model
Bibliographic record
Abstract
With the arrival of the big data era, the demand for massive data storage is growing, and distributed storage systems have become a key technology to solve this problem.The traditional HDFS system has a large storage overhead, this paper in order to improve the storage efficiency of massive data, the introduction of corrective deletion code (RS code) technology, to ensure the reliability of the data at the same time significantly reduce the cost of storage.In order to improve the storage efficiency of massive data, this paper introduces the corrective censoring code (RS code) technology, which ensures the data reliability and significantly reduces the storage cost.In addition, to address the problems of low coding efficiency and high repair overhead in the practical application of RS code, this paper further introduces the local repair code (LRC) technology, which reduces the data repair overhead, and compares and analyzes the application effect of optimization model (RS-LRC-HDFS).The experimental results show that after RS-LRC optimization, the time overhead of the HDFS storage system in the write process and read process is significantly improved by 81.12% and 93.01%, respectively, compared with the pre-optimization period, and the repair time of massive file data is reduced by 87.25%.It can be seen that it provides an efficient and reliable solution for massive data storage.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".