HMM Optimized Modeling of SSD Storage for I/O MapReduce Workloads
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Bibliographic record
Abstract
Flash-based SSD draws a considerable interest in big data platforms due to its performance and reliability. However, it still has limited usage as a result of its high cost and limited capacity. Control SSD provisioning on big data platforms reduce storage cost and guarantees performance. The workload is an essential SSD provisioning sources, thus analyzing the characteristics of the workloads would help optimize SSD management design. There is a significant correlation between the workload's IO patterns and the SSD cost and performance. Big data platforms with multi-stage architecture bring challenges into modeling IO patterns where each stage has it is unique IO patterns. Also, big data platforms run on a distributed environment where the workloads are interacting with local and remote storage during the execution. The designed HMM-based IO patterns model considers IO patterns for MapReduce workloads at different stages and different SSD locations. In this paper, we proposed a platform-level SSD, cost-efficiency controller. The controller is responsible for maximizing the SSD lifespan on the Hadoop platform through two phases. First, modeling MapReduce workload's IO patterns by employing the Hidden Markov Model (HMM). Then, defining platform-level SSD allocation policies. The designed allocation policies reduce SSD utilization and improve SSD lifespan on Hadoop by up to %40 compared to static allocation policies.
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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 | 0.000 |
| 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