On Arbitrating the Power-Performance Tradeoff in SaaS Clouds
Bibliographic record
Abstract
In this paper, we present an analytical framework for characterizing and optimizing the power-performance tradeoff in Software-as-a-Service (SaaS) cloud platforms. Our objectives are two-folded: 1) We maximize the operating revenue when serving heterogeneous SaaS applications with unpredictable user requests. 2) We minimize the power consumption when processing the user requests. To achieve these objectives, we construct a unified profit-maximizing objective to jointly consider revenue and cost in an economic view. An offline solution to maximize the supreme bound of the objective is first developed, to 1) justify the validity of our theoretical model, and 2) establish a benchmark to examine the effectiveness of other control solutions. As a highlight of our contributions, we take advantage of the Lyapunov optimization techniques to design and analyze an optimal yet practical control framework, which makes online decisions on request admission control, routing, and virtual machine (VMs) scheduling. Our control framework can accommodate a variety of design choices and operational requirements in a datacenter. Specifically, buffering facilities can be introduced to alleviate the bursty admitted requests and to improve the robustness of the system, and a power budget can be enforced to improve the datacenter performance (dollar) per watt. Our mathematical analyses and simulations have demonstrated both the optimality (in terms of the cost-effective power-performance tradeoff) and stability (in terms of robustness and adaptivity to time-varying and bursty user requests) achieved by our proposed control framework.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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 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".