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Title: Microservices Architecture Language for Describing Service View [Microservices Architecture Language for Describing Service View]
Award ID(s):
1854049
PAR ID:
10438977
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
13th International Conference on Cloud Computing and Services Science (CLOSER 2023)
Page Range / eLocation ID:
220 to 227
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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  1. Electrical signatures characteristic of complex neurological activity and neuropsychiatric disease are embedded in electroencephalography (EEG) signal data. To firmly establish new correlations between these brain electrical pulses and cognition, behavior, and disorders, researchers must achieve adequate statistical power to validate and mitigate uncertainties in their findings. This necessitates the usage of extensive studies involving large volumes of raw EEG data files from multiple subjects, data which must be preprocessed before conducting further analysis. While conventional processing and analysis of these raw data have been performed using isolated physical lab environments and stovepiped applications, there is a growing necessity for processing and analysis solutions that enable distributed processing of large data collections. This study presents a novel microservices approach as an alternative and complementary solution for retrieving and preprocessing EEG signal data. The approach leverages serverless technologies to deliver a highly scalable solution for processing massive amounts of EEG data. Deployed within a public cloud environment, we assess the efficacy of this method when employing various container orchestration configurations. This work demonstrates the capability for substantial enhancements in processing speeds, particularly when dealing with extensive EEG datasets. 
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