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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".