Accelerating Divisible Load Processing Through Machine Learning: A Practical Framework for Large-Scale Workloads
arXiv:2605.23247v1 Announce Type: new 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
The increasing computational demands of large-scale AI models and the need for more efficient processing in distributed systems are driving innovation in load optimization techniques.
This development indicates a practical pathway to optimize large-scale computational workloads via ML, potentially reducing processing times and increasing efficiency across various applications.
The explicit formulation of complex DLT equations for optimal processing can be replaced or augmented by ML models, simplifying and accelerating the optimization process for distributed computing.
- · Cloud computing providers
- · High-performance computing (HPC) sectors
- · AI/ML infrastructure developers
- · Researchers in distributed systems
- · Traditional DLT optimization software without ML integration
- · Organizations with inefficient computational resource allocation
Reduced latency and increased throughput for large, divisible computational tasks in distributed environments.
Improved efficiency and cost-effectiveness in sectors heavily reliant on large-scale data processing and AI model training.
Acceleration of complex scientific research and development by making advanced computational resources more accessible and performant.
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