Comparative analysis of convolutional and multilayer perceptron neural networks for resource allocation in distributed computing systems

Abstract

Efficient resource distribution in heterogeneous distributed computing systems requires intelligent node selection mechanisms capable of adapting to dynamic system conditions. This research presents a comparative evaluation of Convolutional Neural Network (CNN) and Multilayer Perceptron (MLP) architectures for binary node suitability classification in distributed task scheduling environments. The study employs five synthetic datasets ranging from 100 to 2000 instances, with each node characterized by four critical attributes: performance, security level, baud rate, and reliability. Experimental results demonstrate that the MLP architecture achieves validation accuracy between 91% and 98.8% with high consistency across dataset sizes, while the CNN architecture shows fluctuating performance ranging from 85% to 94%. The key finding reveals that MLP architectures outperform CNNs for tabular node data due to better alignment with unstructured attribute relationships, as fully connected layers naturally handle unordered features without imposing spatial locality assumptions. This empirical analysis provides practical guidance for architecture selection in AI-based resource schedulers, demonstrating that simpler architectures can outperform more complex ones when appropriately matched to problem structure. The findings contribute evidence-based recommendations for distributed systems practitioners implementing neural network-based scheduling solutions.