A Real-time, Scalable Monitoring and User Analytics Solution for Microservices-based Software Applications
Bibliographic record
Abstract
Monitoring the execution of distributed microservices-based software applications is a complex task. As more and more institutions conduct business in a distributed environment, a large amount of user data and transaction data are generated at an accelerated rate in such an environment. As a result, it becomes a big challenge to carry out user analytics with user interaction and transaction data in real-time. Monitoring is one of the most important approaches to getting instant situations of users and transactions. Ideally, the goal of monitoring is to carry out analysis in real-time and be highly scalable with comprehensive analysis and predictions on user interaction and transaction data to gain deeper and facilitate decision-making for the stakeholders. Therefore, we concentrate on what data can be captured to gain insight into users using the platform and how we can capture such data effectively to learn application usage patterns. To achieve this goal, (1) we present how different events data be captured from execution logs of software applications in distributed microservices, and (2) we utilize machine learning methods to predict usage patterns which are generated from interaction between user and system. For example, we show how error types can be predicted in real-time using machine learning and then enable real-time monitoring. The capability to perform analysis based on the continuously growing data volume is considered to be our solution's scalability characteristic. Microservices are used as containers that are managed by Kubernetes. Event logs generated in each microservice are the important data source for executing monitoring. The experiment shows our system can monitor user actions and transactions in real time and that the model capacity is scalable.
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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 | 0.002 |
| 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 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".