HMM Optimized Modeling of SSD Storage for I/O MapReduce Workloads
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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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".