Quantitative cost comparison of on-premise and cloud infrastructure based EEG data processing

High-density, high-sampling rate EEG measurements generate large amounts of measurement data. When coupled with sophisticated processing methods, this presents a storage, computation and system management challenge for research groups and clinical units. Commercial cloud providers offer remote storage and on-demand compute infrastructure services that seem ideal for outsourcing the usually burst-like EEG processing workflow execution. There is little available guidance, however, on whether or when users should migrate to the cloud. The objective of this paper is to investigate the factors that determine the costs of on-premises and cloud execution of EEG workloads, and compare their total costs of ownership. An analytical cost model is developed that can be used for making informed decisions about the long-term costs of on-premises and cloud infrastructures. The model includes the cost-critical factors of the computing systems under evaluation, and expresses the effects of length of usage, system size, computational and storage capacity needs. Detailed cost models are created for on-premises clusters and cloud systems. Using these models, the costs of execution and data storage on clusters and in the cloud are investigated in detail, followed by a break-even analysis to determine when the use of an on-demand cloud infrastructure is preferable to on-premises clusters. The cost models presented in this paper help to characterise the cost-critical infrastructure and execution factors, and can support decision-makers in various scenarios. The analyses showed that cloud-based EEG data processing can reduce execution time considerably and is, in general, more economical when the computational and data storage requirements are relatively low. The cloud becomes competitive even in heavy load case scenarios if expensive, high quality, high-reliability clusters would be used locally. While the paper focuses on EEG processing, the models can be easily applied to CT, MRI, fMRI based neuroimaging workflows as well, which can provide guidance to the wider neuroimaging community for making infrastructure decisions.