Using Fog Computing to Accelerate Metagenomic Data Analysis

Abstract

This article discusses the challenges of processing and analyzing metagenomic data, the volume of which is continuously increasing due to the development of sequencing technologies. Traditional methods such as cloud computing and supercomputing face limitations such as high latency, network dependency, high costs and data security risks. Alternatively, fog computing and hybrid architectures are proposed to distribute the computational load between local devices and cloud systems. This reduces latency, optimizes costs and improves data security. The paper analyzes the advantages of fog computing in metagenomic data analysis, compares it with traditional methods and suggests ways to implement this technology in bioinformatics. The results show that fog computing systems and hybrid systems are promising solutions for applications requiring fast analysis and high data security, such as medical diagnostics and environmental monitoring. The complexity of integrating and managing distributed systems.