BRACELET: Edge-Cloud Microservice Infrastructure for Aging Scientific Instruments

Recent advances in cyber-infrastructure have enabled digital data sharing and ubiquitous network connectivity between scientific instruments and cloud-based storage infrastructure for uploading, storing, curating, and correlating of large amounts of materials and semiconductor fabrication data and metadata. However, there is still a significant number of scientific instruments running on old operating systems that are taken offline and cannot connect to the cloud infrastructure, due to security and network performance concerns. In this paper, we propose BRACELET - an edge-cloud infrastructure that augments the existing cloud-based infrastructure with edge devices and helps to tackle the unique performance & security challenges that scientific instruments face when they are connected to the cloud through public network. With BRACELET, we put a networked edge device, called cloudlet, in between the scientific instruments and the cloud as the middle tier of a three-tier hierarchy. The cloudlet will shape and protect the data traffic from scientific instruments to the cloud, and will play a foundational role in keeping the instruments connected throughout its lifetime, and continuously providing the otherwise missing performance and security features for the instrument as its operating system ages.

[1]  Charles H. Ward Materials Genome Initiative for Global Competitiveness , 2012 .

[2]  Liang Tong,et al.  A hierarchical edge cloud architecture for mobile computing , 2016, IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications.

[3]  Indranil Gupta,et al.  4CeeD: Real-Time Data Acquisition and Analysis Framework for Material-Related Cyber-Physical Environments , 2017, 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID).

[4]  Klara Nahrstedt,et al.  BRACELET: Hierarchical Edge-Cloud Microservice Infrastructure for Scientific Instruments’ Lifetime Connectivity , 2018 .

[5]  Adam R Ferguson,et al.  Big data from small data: data-sharing in the 'long tail' of neuroscience , 2014, Nature Neuroscience.

[6]  Klara Nahrstedt,et al.  MONAD: Self-Adaptive Micro-Service Infrastructure for Heterogeneous Scientific Workflows , 2017, 2017 IEEE International Conference on Autonomic Computing (ICAC).