Continuous Integration and Continuous Delivery Framework for SDS
Bibliographic record
Abstract
Fast and efficient development of software drives the high demand for automation techniques, especially for cloud-based systems trying to implement Software Defined Systems (SDS). The emergence of Continuous Integration/Continuous Delivery (CI/CD) provides a set of steps for building, testing, and deployment of new software in an automated fashion. Consequently, many companies integrate CI/CD pipelines into their platform to automate the development and deployment of new software and applications. Software-Defined Perimeter (SDP) is a new approach to cyber security proposed by the Cloud Security Alliance (CSA) to dynamically secure network services. This is reached utilizing the need-to-know concept where authorization is only granted after strict user verification. SDP framework integrates with cloud-based systems seamlessly. However, the installation, configurations, and management of its components are still manual. This will require a lot of time and resources as the number of protected services increases. Therefore, this paper presents the implementation of the Continuous Integration/Continuous Delivery (CI/CD) pipeline for the open SDP project that automates the installation and deployment of its various components. Specifically, the Open SDP components (i.e., SDP controller and gateway) will be used as a use case to show the use of CI/CD and to secure applications hosted on the OpenShift environment. The OpenShift pipeline operator, based on the Tekton project was adopted as the CI/CD pipeline for this project. The Code Ready Container (CRC) was utilized as the OpenShift cluster, which is then hosted on a server running a Windows OS. Furthermore, the challenges, as well as their solutions to the Open SDP CI/CD pipeline, are presented.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".