Autonomous Orchestration of Distributed Discrete Event Simulations in the Presence of Resource Uncertainty
Bibliographic record
Abstract
Discrete event simulations model the behavior of complex, real-world systems. Simulating a wide range of events and conditions provides a more nuanced model, but also increases its computational footprint. To manage these processing requirements in a scalable manner, discrete event simulations can be distributed across multiple computing resources. Orchestrating the simulations in a distributed setting involves coping with resource uncertainty. We consider three key aspects of resource uncertainty: resource failures, heterogeneity, and slowdowns. Each of these aspects is managed autonomously, which involves making accurate predictions of future execution times and latencies while also accounting for differences in hardware capabilities and dynamic resource consumption profiles. Further complicating matters, individual tasks within the simulation are stateful and stochastic, requiring inter-task communication and synchronization to produce accurate outcomes. We deal with these challenges through intelligent state collection and migration, active resource monitoring, and empirical evaluation of resource capabilities under changing conditions. To underscore the viability of our solution, we provide benchmarks using a production discrete event simulation that can simultaneously sustain failures, manage resource heterogeneity, and handle slowdowns while being orchestrated by our 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".