Automotive Big Data Pipeline: Disaggregated Hyper-Converged Infrastructure vs Hyper-Converged Infrastructure

Big data disrupts everything it touches, but automotive is probably one of the top industries that enjoy and leverage the benefits. The Automotive Big Data Pipeline (ABDP) is a big data pipeline base on the automotive use case and is required to scale up agile and high performance in real-time or in batch. Nonetheless, there're many alternative infrastructure designs but lack of knowledge, which fits the best for the automotive domain. It leads this paper into a question: What kinds of infrastructure design could provide better performance for the ABDP?In this paper, we introduce two well-known infrastructure designs called Hyper-Converged infrastructure (HCI) and Disaggregated Hyper-Converged infrastructure (DHCI). HCI combines standard data center hardware using locally attached storage resources to create fast, common building blocks. However, does single standard hardware fit all the requirements? DHCI scale independently from compute and storage provides an option. It provides a more cost-efficient and flexible solution; however, there is no comparison from the performance point of view. Therefore, to address it, our objective is to conduct an empirical performance comparison to see which one performs better.The experiment result shows that DHCI performs almost the same as HCI on CPU utilization, memory, and network consumption. However, regarding storage and running time metrics, DHCI performs slightly higher storage throughput, IOPs, and less running time than HCI.