Machine Learning for Consistency Violation Faults Analysis

TL;DR

This paper introduces machine learning models to analyze the impact of consistency violation faults in distributed systems, demonstrating promising results and scalability potential with advanced hardware.

Contribution

It presents a novel ML-based approach for quantifying CVF effects in distributed systems using neural networks trained on small graph datasets.

Findings

Neural networks achieved a test loss of 4.39.

Mean absolute error was 1.5.

Distributed training showed limited speedup on CPU.

Abstract

Distributed systems frequently encounter consistency violation faults (cvfs), where nodes operate on outdated or inaccurate data, adversely affecting convergence and overall system performance. This study presents a machine learning-based approach for analyzing the impact of CVFs, using Dijkstra's Token Ring problem as a case study. By computing program transition ranks and their corresponding effects, the proposed method quantifies the influence of cvfs on system behavior. To address the state space explosion encountered in larger graphs, two models are implemented: a Feedforward Neural Network (FNN) and a distributed neural network leveraging TensorFlow's \texttt{tf.distribute} API. These models are trained on datasets generated from smaller graphs (3 to 10 nodes) to predict parameters essential for determining rank effects. Experimental results demonstrate promising performance, with a test loss of 4.39 and a mean absolute error of 1.5. Although distributed training on a CPU did not yield significant speed improvements over a single-device setup, the findings suggest that scalability could be enhanced through the use of advanced hardware accelerators such as GPUs or TPUs.