Accelerating Divisible Load Processing Through Machine Learning: A Practical Framework for Large-Scale Workloads
In this paper, we introduce the first machine learning framework for predicting optimal processing times in Single-Level Tree Network (SLTN) architectures for the Divisible Load Theory (DLT) paradigm. Using a feedforward neural network(FNN) with 16 engineered features, we train a model on 100,000 synthetically generated configurations to predict optimal processing times without explicit formulation of DLT equations. The model achieves 97-99% accuracy (R-square factor) with mean absolute percentage error of 1-5%, demonstrating that neural networks can effectively learn complex load distribution relationships. Feature importance analysis reveals that the model implicitly captures DLT mathem...
| Authors | Bharadwaj Veeravalli |
|---|---|
| Primary category | cs.LG |
| Published | 22 May 2026 |
| Last updated at source | 8 June 2026 |
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