Comparative Analysis of Genetic Algorithm and XML Filtering Technique for Multi-Tenant SaaS Configuration Management
Bibliographic record
Abstract
The use of Software-as-a-Service (SaaS) cloud model is increasing rapidly. Among different SaaS deployment models, Single Instance Multi-Tenant (SIMT) has multiple advantages, e.g., high scalability and lower cost, but it also has practical concerns for management, as tenants may have distinct requirements or even conflicting preferences, and the number of features and tenants may be large. Offering the best user satisfaction, dealing with tenants' preferences and operational environment changes is crucial but challenging for effective SaaS management. Genetic algorithms (GAs) have been applied to the optimization problem in many fields. Further a GA-based approach, GAFES, specifically adapted to constraint-based feature selection optimization in Software Product Line (SPL) for multi-tenant SaaS has been reported. On the other hand, an XML filter technique, Yfilter, has been applied to various problem domains and recently to SIMT for SaaS. The aim of this paper is to experimentally evaluate the performance of GAFES and Yfilter for SaaS configuration with recourse constraints. We have conducted experiments to evaluate the two very different multi-tenant SaaS configuration techniques. The results show that both approaches can meet the user satisfaction, but Yfilter has much higher performance compared to GAFES.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".