Cloud Resource Scaling for Big Data Streaming Applications Using a Layered Multi-dimensional Hidden Markov Model
Bibliographic record
Abstract
Recent advancements in technology have led to a deluge of data that require real-time analysis with strict latency constraints. A major challenge, however, is determining the amount of resources required by big data stream processing applications in response to heterogeneous data sources, streaming events, unpredictable data volume and velocity changes. Over-provisioning of resources for peak loads can be wasteful while under-provisioning can have a huge impact on the performance of the streaming applications. The majority of research efforts on resource scaling in the cloud are investigated from the cloud provider's perspective, they focus on web applications and do not consider multiple resource bottlenecks. We aim at analyzing the resource scaling problem from a big data streaming application provider's point of view such that efficient scaling decisions can be made for future resource utilization. This paper proposes a Layered Multi-dimensional Hidden Markov Model (LMD-HMM) for facilitating the management of resource auto-scaling for big data streaming applications in the cloud. Our detailed experimental evaluation shows that LMD-HMM performs best with an accuracy of 98%, outperforming the single-layer hidden markov model.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".