Improving Service Guarantees in Computing Utilities Using Volatile Public Resources
Bibliographic record
Abstract
Computing utilities are emerging as an important part of the infrastructure for outsourcing computer services. Fundamental to outsourcing is the notion of quality of service, which is defined by service level agreements (SLAs) between the computing utilities and clients. One of the major objectives of computing utilities is to maximize their net profit while maintaining customer loyalty. To achieve this objective, the computing utilities should meet or exceed their SLA constraints most of the time. Defining the SLAs conservatively might be one way of easily achieving these goals. However, by tuning the SLA parameters conservatively the computing utility might under utilize its resources with a resultant loss of revenue. Therefore, we can see two main issues with SLA management: designing SLAs competitively so that expected revenue for the computing utility is maximized and maintaining the operating conditions such that SLAs are satisfied with very high probability. In this paper, we show that inducting unreliable public resources into a computing utility enables steeper SLAs while maintaining higher levels of run time compliances.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".