Service Agreement Trifecta: Backup Resources, Price and Penalty in the Availability-Aware Cloud
Bibliographic record
Abstract
Service Level Agreements (SLA) for cloud services entail complex trade-offs between interrelated variables such as price, penalty, and service availability (uptime) guarantee, with resource management strategies affecting fulfillment of the SLA. In this study, we address three key components of the SLA-based cloud resource management and pricing problem, from the service-provider’s perspective: (1) availability-aware backup resource provisioning; (2) price-penalty schedule determination; and (3) penalty-deferred pricing over two periods. Using the convexity of the provider’s expected total cost over the number of backup resources, we present a dichotomous search algorithm to derive the total cost minimizing number of backup resources for a given level of SLA-specified service availability guarantee. Next, we derive closed-form solutions for the lower bound of the feasible price range, yielding a schedule of breakeven price-penalty combinations, which establishes the baseline required in the economic modeling of the service contracts and related negotiation processes, and may also elicit client preference information. We then model a two-period pricing problem specifically designed to incentivize penalty deferrals in the event of an SLA violation. Detailed experimental studies of the proposed models have been carried out using real-world datacenter log data. The computational study validates the convexity of the probability density function of SLA violations over the number of backup resources. The results demonstrate significant interaction effects between the SLA parameters (price, penalty rate, and provisioning cost) and the backup resource provisioning decisions made by the provider, leading to key practical managerial implications for SLA design and resource deployment in the availability-aware cloud. The online appendix is available at https://doi.org/10.1287/isre.2017.0755 .
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".