Microservice Based Architecture: Towards High-Availability for Stateful Applications with Kubernetes
Bibliographic record
Abstract
Kubernetes is an open source platform that hides the complexity of orchestrating containerized microservices while managing their availability. Stateless microservices can be executed in a resilient manner with Kubernetes. However, the same is not true for stateful microservices. Containers are characterized by having an ephemeral state and the state aspect of stateful microservices makes orchestration more complex than what the initial Kubernetes controllers were built for. In this paper, we investigate the current Kubernetes support for stateful microservices and identify the problems. We propose a solution to enrich Kubernetes with a State Controller that allows for state replication and automatic service redirection to the healthy entities through the management of secondary labels. We have conducted experiments under the default configuration of Kubernetes as well as under its most responsive one to evaluate our solution and compare the different architectures from an availability perspective. We also perform a comparative evaluation with OpenSAF, which is a proven solution for enabling high-availability. The results of our investigations show that our solution improves the recovery time of stateful microservices by 55% and even up to 99% in certain cases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".