<i>Mist</i>: Efficient Dissemination of Erasure-Coded Data in Data Centers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Data centers store a massive amount of data in a large number of servers built by commodity hardware. To maintain data integrity against server failures, erasure codes have been extensively deployed in modern data centers to provide a higher level of failure tolerance with less storage overhead than replication. Yet, compared to replication, disseminating erasure-coded data from a source server into multiple servers will also take significantly more time. In this paper, we design and implement Mist, a new mechanism for disseminating erasure-coded data efficiently to multiple receiving servers (receivers) in data centers. Mist speeds up the dissemination process by building an efficient topology among the receivers with heterogeneous performance, so that coded data can be received from other receivers in a pipelined fashion, rather than directly from the source. Mist flexibly supports a wide range of erasure codes, without imposing constraints to the range of system parameters, and can be extended for specific erasure codes with better performance by taking advantage of the corresponding erasure code. We have implemented Mist in Python, and our experimental results in Amazon EC2 have demonstrated that the dissemination time can be reduced by up to 96.3 percent with different kinds of erasure codes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 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 it