Cloud Resource Scaling for Time-Bounded and Unbounded Big Data Streaming Applications
Why this work is in the frame
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Bibliographic record
Abstract
Recent advancements in technology have led to a deluge of big data streams that require real-time analysis with strict latency constraints. A major challenge, however, is determining the amount of resources required by applications processing these streams given their high volume, velocity and variety. The majority of research efforts on resource scaling in the cloud are investigated from the cloud provider's perspective with little consideration for multiple resource bottlenecks. We aim at analyzing the resource scaling problem from an application provider's point of view such that efficient scaling decisions can be made. This paper provides two contributions to the study of resource scaling for big data streaming applications in the cloud. First, we present a Layered Multi-dimensional Hidden Markov Model (LMD-HMM) for managing time-bounded streaming applications. Second, to cater to unbounded streaming applications, we propose a framework based on a Layered Multi-dimensional Hidden Semi-Markov Model (LMD-HSMM). The parameters in our models are evaluated using modified Forward and Backward algorithms. Our detailed experimental evaluation results show that LMD-HMM is very effective with respect to cloud resource prediction for bounded streaming applications running for shorter periods while the LMD-HSMM accurately predicts the resource usage for streaming applications running for longer periods.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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