A Kubernetes Controller for Managing the Availability of Elastic\n Microservice Based Stateful Applications
Bibliographic record
Abstract
The architectural style of microservices has been gaining popularity in\nrecent years. In this architectural style, small and loosely coupled modules\nare deployed and scaled independently to compose cloud-native applications.\nCarrier-grade service providers are migrating their legacy applications to a\nmicroservice based architecture running on Kubernetes which is an open source\nplatform for orchestrating containerized microservice based applications.\nHowever, in this migration, service availability remains a concern. Service\navailability is measured as the percentage of time the service is provisioned.\nHigh Availability (HA) is achieved when the service is available at least\n99.999% of the time. In this paper, we identify possible architectures for\ndeploying stateful microservice based applications with Kubernetes and evaluate\nKubernetes from the perspective of availability it provides for its managed\napplications. The results of our experiments show that the repair actions of\nKubernetes cannot satisfy HA requirements, and in some cases cannot guarantee\nservice recovery. Therefore, we propose an HA State Controller which integrates\nwith Kubernetes and allows for application state replication and automatic\nservice redirection to the healthy microservice instances by enabling service\nrecovery in addition to the repair actions of Kubernetes. Based on experiments\nwe evaluate our solution and compare the different architectures from the\nperspective of availability and scaling overhead. The results of our\ninvestigations show that our solution can improve the recovery time of stateful\nmicroservice based applications by 50%.\n
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".