SSD: Cache or Tier an Evaluation of SSD Cost and Efficiency using MapReduce
Why this work is in the frame
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Bibliographic record
Abstract
Solid-State Drives (SSDs) play a crucial role in today's storage systems. They are appended into the Hard-Disk Drives (HDDs) storage systems to improve performance. They provide high IO rate and low latency, which makes them a perfect candidate for analytic-based workloads such as MapReduce. Defining an efficient SSD deployment strategies for MapReduce workloads is a challenging task: SSDs are costly and have limited capacity, the workloads have a big process data size, and the platform has a unique workflow nature. The goal of the work is to establish performance and cost relationship between SSD approaches and MapReduce workloads. In our setup, MapReduce workloads were executed with two SSD approaches of tiering and caching each with two setups: compress and uncompress. Our results showed that by using SSD as a tier, MapReduce workload performs better by up to 66% and increased SSD lifespan by around 20% when comparing with cache approach. We also observed that applying compression on the tier approach enhanced the lifespan by 60% but reduced lifespan of cache tier by 50%.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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