Performance of Raspberry Pi microclusters for Edge Machine Learning in Tourism

While a range of computing equipment has been developed or proposed for use to solve machine learning problems in edge computing, one of the least-explored options is the use of clusters of low-resource devices, such as the Raspberry Pi. Although such hardware configurations have been discussed in the past, their performance for ML tasks remains unexplored. In this paper, we discuss the performance of a Raspberry Pi micro-cluster, configured with industry-standard platforms, using Hadoop for distributed file storage and Spark for machine learning. Using the latest Raspberry Pi 4 model (quad core 1.5GHz, 4Gb RAM), we find encouraging results for use of such micro-clusters both for local training of ML models and execution of ML-based predictions. Our aim is to use such computing resources in a distributed architecture to serve tourism applications through the analysis of big data.