Automated Reliability Analysis in Fog Computing Environment

This paper introduces the EMA (Expectation Maximization Algorithm) tool, an automated solution designed to fit Hyper-Erlang distributions for reliability analysis, particularly in fog computing environments. Traditional methods for evaluating Time Failure (TTF) distributions are time consuming and do not scale well in complex systems. The EMA tool addresses this limitation by automating the fitting process, significantly reducing computational time. Applying a resampling method across 100 scenarios, the EMA tool captures system variability and efficiently generates key metrics, such as Probability Density Functions (PDFs) and Cumulative Distribution Functions (CDFs). The tool estimates Hyper-Erlang distribution parameters for each scenario and derives critical reliability metrics, including Mean Time to Absorption (MTTA). The fitting process for the 100 scenarios was completed in just 8 minutes and 22 seconds, and subsequent reliability analysis took 24 and 35 seconds. The EMA tool provides essential information on system performance under various conditions, improving the scalability and efficiency of reliability analysis in distributed environments.