Business-Driven Optimization of Policy-Based Management solutions
Bibliographic record
Abstract
We consider whether the off-line compilation of a set of Service Level Agreements (SLAs) into low-level management policies can lead to the runtime maximization of the overall business profit for a service provider. Using a simple Web application hosting SLA template for a utility service provider, we derive low-level QoS management policies and validate their consistency. We show how the default first come first served (FCFS) mechanism for the runtime scheduling of triggered policies fails to deliver an all times maximum business profit for the service provider. To achieve a better business profit, first a penalty/reward model that is derived from the SLA Service Level Objectives (SLOs) is used to assign runtime utility tags to triggered policies. Then three policy scheduling algorithms, which are based on the prediction of the future state of the running SLAs, are used to drive the runtime actions of the Policy Decision Point (PDP). The prediction function per see involved the unsolved problem of predicting in realtime the evolution of the transient state of a variant of an M/M/Ct/Ct queue. A simple approximative solution to the latter problem is provided. Finally, using the VS policy simulator tool, comparative simulation results for the business profit generated by each of the proposed policy scheduling algorithms are presented. VS is a novel tool which we have developed to respond to the increasing need of benchmarking SLA and policy-based management solutions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".