Accelerating Divisible Load Processing Through Machine Learning: A Practical Framework for Large-Scale Workloads

2026-05-22

AI Summary

This study addresses the high computational complexity of traditional Divisible Load Theory (DLT) in determining optimal processing times for single-level tree networks, which hinders real-time scheduling and large-scale resource allocation. To overcome this limitation, the work introduces machine learning into DLT optimization for the first time, proposing a feedforward neural network–based prediction framework. The model implicitly learns load conservation and synchronized completion constraints from 16 engineered features derived from 100,000 synthetic data instances, bypassing explicit solution of DLT equations. Evaluated across diverse system configurations, the approach achieves R² accuracies of 97–99% (with MAPE of 1–5%) and sub-millisecond inference latency. This enables speedups of 10–100× over conventional methods while preserving near-optimal accuracy, thereby significantly facilitating real-time scheduling and design space exploration.

Abstract

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 mathematical structure, including load conservation and simultaneous finishing constraints. With inference times under 1 millisecond, the approach provides 10-100x speedup over traditional DLT computation, enabling applications in real-time scheduling, design space exploration, and cloud resource allocation. The method generalizes well across diverse system configurations (n=3 to 20, load size =1 to 100 GB) with consistent accuracy, though performance degrades slightly for very large or highly heterogeneous systems. This work demonstrates the feasibility of using machine learning to accelerate distributed computing optimization while maintaining near-optimal accuracy.

Problem

• Divisible Load Theory
• Optimal Processing Time
• Single-Level Tree Network
• Large-Scale Workloads
• Distributed Computing Optimization

Innovation

• Divisible Load Theory
• Machine Learning Acceleration
• Feedforward Neural Network
• Real-time Scheduling
• Distributed Computing Optimization