Bibliographic record
Abstract
The principal goal of this work is to support performance management for systems that utilize resources in complex ways. Typically, performance evaluation has been carried out for such systems using simulation tools. However, such tools require expert model builders to create and maintain abstract business process models of the system under study. This can lead to a lack of representativeness, specifically, when many unique scenarios are to be modelled. This thesis presents a new simulation approach, Simulation By Example, which guides the simulation directly using events extracted from traces, i.e., examples. This work demonstrates and evaluates this new approach using three case studies from healthcare systems. These studies establish the advantages of SBE over traditional simulation methods and its ability to support a variety of performance management exercises. Next, this thesis focuses on improving the performance of systems subjected to bursty workloads. Burstiness in resource service demands has previously been shown to have an adverse impact on system performance. This thesis proposes AMIR, an Analytic Method for Improving Responsiveness by reducing burstiness. AMIR considers a system with multiple classes of users and multiple resources that service user sessions in tandem. Batch processing systems, fabrication and manufacturing environments, micro-service systems, and patient operating rooms can be described in this way. Given the service demands distributions placed by all classes for the system's resources and the number of session arrivals for each class, AMIR decides an ordering of sessions that minimizes burstiness and improves system responsiveness metrics including session wait time, and total schedule processing time. A key aspect of the technique is an order O schedule burstiness metric β^O, which represents the mean joint probability that O+1 consecutive sessions in the schedule have resource demands at the bottleneck resource greater than the mean bottleneck resource demand of the schedule. For a given O, AMIR uses integer linear programming to produce schedules that progressively minimize β^i for all i in {1,...O}. Extensive simulation results show that AMIR significantly outperforms baseline policies such as shortest first and random scheduling. The results also provide insights on the conditions under which the technique is most effective.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".