Disambiguating Performance Anomalies from Workload Changes in Cloud-Native Applications
Bibliographic record
Abstract
Modern cloud-native applications are adopting the microservice architecture in which applications are deployed in lightweight containers that run inside a virtual machine (VM). Containers running different services are often co-located inside the same virtual machine. While this enables better resource optimization, it can cause interference among applications. This can lead to performance degradation. Detecting the cause of performance degradation at runtime is crucial to decide the correct remediation action such as, but not limited to, scaling or migrating. We propose a non-intrusive detection technique that differentiates between degradation caused by load and by interference. First, we define an operational zone for the application. Then we define a disambiguation method that uses models to classify interference and normal load. In contrast to previous work, our proposed detection technique does not require intrusive application instrumentation and incurs minimal performance overhead. We demonstrate how we can design effective Machine Learning models that can be generalized to detect interference from different types of applications. We evaluate our technique using realistic microservice benchmarks on AWS EC2. The results show that our approach outperforms existing interference detection techniques in F_1 score by at least 2.75% and at most 53.86%.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 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".