A comparative study of cloud-native vs. edge computing architectures for real-time data processing

Abstract

The fast adoption of Internet of Things (IoT) devices, autonomous systems and latency-sensitive applications has increased the need to have effective real-time data processing architectures. The paper will provide a detailed comparative analysis of cloud-native and edge computing systems in processing real-time data with a systematic literature review (SLR) and empirical benchmarking experiments. On the basis of a PRISMA-directed review of 87 papers (22 of which have been ultimately included) found after screening, we evaluate latency, throughput, energy consumption, scalability, fault tolerance, and security profiles of both paradigms. The experimental findings show that edge computing has a mean latency of 8.3 ms compared to cloud-native deployment of 142.7 ms, and the cloud-native architecture has higher availability at 99.95% and is scaled 3.8× times horizontally. It is suggested to use a hybrid framework, combining edge inference with cloud orchestration that is 94.2% times faster and has the same cloud-grade reliability. The ANOVA, regression modelling, and multi-criteria decision analysis (MCDA) data analysis shows that the choice of the optimal architecture is determined by application specific latency tolerance (α), data locality requirements and the budget constraint in the infrastructure. These results are applicable to the system architects operating in such sectors as smart healthcare, industrial IoT, autonomous vehicles, and smart grid management.