SliceSphere: Agile Service Orchestration and Management Framework for Cloud-Native Application Slices
Bibliographic record
Abstract
We introduce SliceSphere a comprehensive framework to ease the deployment and management of scalable cloud-native application slices. Slicesphere integrates a strong slicing framework with containerized orchestration engines. We present cloud-native application slicing as an architectural framework that integrates application segmentation, platform abstraction, and cloud-native methodologies into a system known as SliceSphere. This approach forms a cohesive, distributed application environment that surpasses traditional infrastructure limits, allowing organizations to deploy, manage, and scale applications across various computing landscapes with flexibility, scalability, and efficiency. In SliceSphere, each slice, termed SliceKube, operates as an independent unit, encompassing specific features and interacting via well-defined APIs. We have incorporated fundamental microservice design principles and offer extensive service integration, covering everything from specialized multi-stack control planes to essential resources such as computing power, storage, and networking. SliceSphere is built to be flexible, ensuring compatibility regardless of the orchestration engine or infrastructure provider in use. It simplifies complex deployments while enabling users to customize the design of control plane and platform service functionalities to their needs. Additionally, SliceSphere includes two marketplaces that offer pre-configured service-oriented applications and multi-cloud shared resources. A practical demonstration of SliceSphere, designed as a Kubernetes plugin and utilizing open-source technologies, has been developed on the multi-tiered SAVI infrastructure, leveraging a variety of resources. We examine this specific use case, SAVI 2.0, in detail to showcase SliceSphere’s ability to address the needs of distributed cloud-native applications across multi-tier and multi-cloud infrastructure.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".