Transfer Learning Based Performance Modeling And Effective Storage Management In Big Data Ecosystems
Bibliographic record
Abstract
Big data ecosystems contain a mix of sophisticated hardware storage components to support heterogeneous workloads. Storage components and the workloads interact and affect each other; therefore, their relationship has to consider when modeling workloads or managing storage. Efficient workload modeling guides optimal storage management decisions, and the right decisions help guarantee the workload’s needs. The first part of this thesis focuses on workload modeling efficiency, and the second part focuses on cost-effective storage management. Workload performance modeling is an essential step in management decisions. The standard modeling approach constructs the model based on a historical dataset collected from one set of setups (scenario). The standard modeling approach requires the model to be reconstructed from scratch with every time the setups changes. To address this issue, we propose a cross-scenario modeling approach that improves the workload’s performance classification accuracy by up to 78% through adopting the Transfer Learning (TL). The storage system is the most crucial component of the big data ecosystem, where the workload’s execution process starts by fetching data from it and ends by storing data into it. Thus, the workload’s performance is directly affected by storage capability. To provide a high I/O performance in the ecosystems, Solid State Drive (SSD) are utilized as a tier or as a cache on big data distributed ecosystems. SSDs have a short lifespan that is affected by data size and the number of writing operations. Balancing performance requirements and SSD’s lifespan consumption is never easy, and it’s even harder when interacting with a huge amount of data and with heterogeneous I/O patterns. In this thesis, we analysis big data workloads I/O pattern impacts on SSD’s lifespan when SSD is used as a tier or as a cache. Then, we design a Hidden Markov Model (HMM) based I/O pattern controller that manages workload placement and guarantees cost-effective storage that enhances the workload performance by up to 60%, and improves SSD’s lifespan by up to 40%. The designed transfer learning modeling approach and the storage management solutions improve workload modeling accuracy, and the quality of the storage management policies while the testing setup changes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".