Heatmap Visualization for Monitoring Health of a Large-scale Cloud System
Bibliographic record
Abstract
The complex infrastructure, growing scale, and variety of a large-scale Cloud System (LCS) pose many challenges in monitoring the health of its components. Unfortunately, existing advanced monitoring systems often fail to assist the Cloud Operation Team in gaining meaningful insights about their system and its underlying components. In this thesis, we propose a near-real-time interactive visual monitoring tool based on heatmaps that help developers and maintainers of LCS to perform exploratory analysis of LCS health and aid in decision-making regarding resource planning and provisioning, configuration design, and problem identification. We have validated our tool in real-world settings by monitoring IBM Cloud Console (an LCS used by IBM to monitor IBM Cloud). Results show that our heatmaps can provide actionable insights. In particular, the tool has helped the team diagnose anomalous behaviour of the components, determine heavy or low traffic, find latency issues and make critical business decisions. Our tool is of interest to practitioners as it can be used to monitor the health of an arbitrary LCS. Moreover, it can serve as a building block for creating a theory of monitoring complex software systems, which is of interest to academics.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.007 | 0.001 |
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".