On-Demand Capacity Provisioning in Storage Clusters Through Workload Pattern Modeling
Bibliographic record
Abstract
Internet of Things (IoT), which is the inter-networking of a wide variety of physical devices, is widely used in our daily life. The exponential increase in the number of diverse devices has resulted in a significant increase in the volume, variety, velocity, and veracity of data (i.e., big data). These data present a large requirement on modern storage systems both for capacity and scale, and energy cost has become a critical problem. For storage clusters, much research effort has been invested in alleviating this problem by providing suitable resource capacity (i.e., on-demand providing). However, it is challenging to match the offered resource capacity with the real system workloads, thus resulting in a violation of service level agreement. By considering a storage cluster as a queueing system, this paper proposes a QoS-oriented capacity provisioning mechanism. Based on workload features, the mechanism models the pattern of current workloads as a suitable queueing model. In accordance with the model, our mechanism can well forecast the actual resource capacity demand without violating the service level agreement, and then offer the required resource capacity in terms of the real workloads. Experimental results demonstrate that the proposed mechanism significantly reduces the energy consumption of a typical storage cluster, while meeting the QoS requirements. It also significantly outperforms two classic and two state-of-the-art capacity provisioning mechanisms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".