Storage and Rack Sensitive Replica Placement Algorithm for Distributed Platform with Data as Files
Bibliographic record
Abstract
Distributed File System (DFS) is a key component in cloud and data center networking. Frequent hardware failure and network bottlenecks in the underlying infrastructure degrade the performance significantly. Replica technique provides enhanced fault tolerance by storing multiple replicas of a single data block. In the Hadoop platform, the Hadoop Distributed File System (HDFS) handles data storage and provides replica placement services. The Default HDFS replica engine adopts a simple rack aware policy and is designed to improve fault tolerance by storing data blocks in multiple racks. However, the HDFS replica engine does not consider key performance indicators of data center resources such as rack utilization and node storage utilization. Furthermore, in HDFS data is stored as uniformly divided small-sized blocks, which increases traffic flow during the entire file access, therefore degrading the response time. In this research, we propose a Storage and Rack Sensitive (SRS) replica placement algorithm that aims at improving the rack and storage utilization of data center resources. The proposed algorithm also attempts to optimize traffic flow during file access by storing data as original files instead of small uniform blocks. Experimental results of the proposed SRS algorithm are compared against the default HDFS replica distribution and significant improvement on rack-utilization and storage-utilization were observed. Furthermore, latest literature confirms that the “Data as a File” approach indeed decreases the amount of data flow caused by file access traffic.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".