SLA-Aware Tenant Placement and Dynamic Resource Provision in SaaS
Bibliographic record
Abstract
Software as a Service (SaaS) is an increasingly important service delivery model in cloud computing, and multitenancy makes it possible to support large scale customized tenants with only one code base. However, the complexity of multi-tenant architecture may lead to poor performance and low resource utilization. The customized demands may also lead to high operating cost. It is very important to develop an accurate model to predict the performance of the multi-tenant SaaS. To this end, a multi-tenant queueing network model is developed. Based on the model, a balanced SLA-aware tenant placement algorithm is proposed considering that customized tenants may need more resources to be placed together. The algorithm is effective in nearly 90% of the simulations comparing with other heuristic algorithms. Furthermore, the optimization problem on dynamic resource provision to minimize the operating cost is studied. As the original optimization problem is NPhard, a continuous upper bound is used to convert the original optimization problem into a convex optimization which can be solved efficiently in polynomial time. Finally, it is demonstrated that the approximate ratio of the proposed approach is no greater than 1.2 in more than 90% of the simulations.
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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 | 0.001 | 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 | 0.000 | 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".