A grid-based DEVS approach to dynamic load balancing for large scale distributed simulations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Dynamic load balancing is a key factor in achieving high performance for large scale distributed simulations on grid infrastructures. In a grid environment, the available resources and the simulation's computation and communication behavior may experience run-time critical imbalances. Consequently, an initial static partitioning should be combined with a dynamic load balancing scheme to ensure the high performance of the distributed simulation. Many improved or novel dynamic load balancing designs have been proposed in recent years, which aim to improve the distributed simulation performance. Such designs are in general non-formalized, and the realizations of the designs are highly time-consuming and error-prone practices. In this paper, we propose a formal dynamic load balancing design approach using Discrete Event System Specification (DEVS). We discuss the feasibility of using DEVS and, as an additional step; we consider studying a recently proposed design through a formalized DEVS model system. Our focus is how a DEVS component-based formalized design approach can predict some of the key design factors before the design is realized, or can further validate and consolidate realized dynamic load balancing designs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it