EC-Store: Bridging the Gap between Storage and Latency in Distributed Erasure Coded Systems
Why this work is in the frame
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Bibliographic record
Abstract
Cloud storage systems typically choose between replicating or erasure encoding data to provide fault tolerance. Replication ensures that data can be accessed from a single site but incurs a much higher storage overhead, which is a costly downside for large-scale storage systems. Erasure coding has a lower storage requirement but relies on encoding/decoding and distributed data retrieval, which can result in straggling requests that increase response times. We propose strategies for data access and data movement within erasure-coded storage systems that significantly reduce data retrieval times. We present EC-Store, a system that incorporates these dynamic strategies for data access and movement based on workload access patterns. Through detailed evaluation using two benchmark workloads, we show that EC-Store incurs significantly less storage overhead than replication while achieving better performance than both replicated and erasure-coded storage systems.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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