Constraint-Based Multi-Tenant SaaS Deployment Using Feature Modeling and XML Filtering Techniques.
Bibliographic record
Abstract
Software-as-a-service (SaaS) is becoming more important as a software delivery and service model. However, multi-tenancy, which promises to provide a high degree of resource sharing among a large number of tenants (customers or organizations), can significantly complicate SaaS development, deployment, and management due to potential explosion of co-existing tenant-specific variations. Manually configuring those tenant-specific variations for multi-tenant SaaS systems cannot satisfy scalability and flexibility. In this paper, we propose a novel approach in support of multi-tenant SaaS systems using feature modeling and XML filtering techniques. Feature modeling is used to capture functional, non-functional requirements and constraints. The features of a cloud system and tenant-specific requirements are encoded with XPath feature representations and XML document, respectively. We adopt Yfilter, an established XML filtering technique, and tailor it to match cloud configurations that satisfy tenant-specific requirements and constraints. The experimental results demonstrate that our approach can automatically and correctly identify cloud system configurations that match tenant-specific requirements. In addition, the execution time in our approach is only a small fraction compared to the existing approaches (e.g., Fama) and the configuration space is also smaller.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".