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.

高密度，高采样率的脑电图测量会生成大量的测量数据。当加上复杂的处理方法时，这给研究小组和临床部门带来了存储，计算和系统管理方面的挑战。商业云提供商提供远程存储和按需计算基础架构服务，这似乎是外包通常类似突发性EEG处理工作流执行的理想选择。但是，关于用户是否或何时迁移到云的指南很少。本文的目的是调查确定EEG工作负载的本地成本和云执行成本的因素，并比较它们的总拥有成本。开发了一种分析成本模型，该模型可用于就本地和云基础架构的长期成本做出明智的决策。该模型包括评估中的计算系统的关键成本因素，并表达了使用时间，系统大小，计算和存储容量需求的影响。为本地群集和云系统创建了详细的成本模型。使用这些模型，详细研究了群集和云中的执行和数据存储成本，然后进行了收支平衡分析，以确定何时使用按需云基础架构胜过本地群集。本文介绍的成本模型有助于表征关键成本的基础架构和执行因素，并可以在各种情况下为决策者提供支持。分析表明，基于云的EEG数据处理可以显着减少执行时间，并且通常在计算和数据存储需求相对较低时更为经济。如果在本地使用昂贵，高质量，高可靠性的集群，即使在重负载情况下，云也将变得更具竞争力。尽管本文着重于脑电图处理，但是这些模型也可以轻松应用于基于CT，MRI，fMRI的神经影像工作流程，这可以为更广泛的神经影像社区提供基础设施决策的指导。高可靠性群集将在本地使用。尽管本文着重于脑电图处理，但是这些模型也可以轻松应用于基于CT，MRI，fMRI的神经影像工作流程，这可以为更广泛的神经影像社区提供基础设施决策的指导。高可靠性群集将在本地使用。尽管本文着重于脑电图处理，但是这些模型也可以轻松应用于基于CT，MRI，fMRI的神经影像工作流程，这可以为更广泛的神经影像社区提供基础设施决策的指导。