A Novel Architecture of Jupyterhub on Amazon Elastic Kubernetes Service for Open Data Cube Sandbox
                                                                                                        | Title | A Novel Architecture of Jupyterhub on Amazon Elastic Kubernetes Service for Open Data Cube Sandbox | 
| Publication Type | Conference Paper | 
| Year of Publication | 2020 | 
| Authors | Rizvi, Syed R, Lubawy, Andrew, Rattz, John, Cherry, Andrew, Killough, Brian, Gowda, Sanjay | 
| Conference Name | IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium | 
| Date Published | Oct. 2020 | 
| Publisher | IEEE | 
| ISBN Number | 978-1-7281-6374-1 | 
| Keywords | Amazon Web Services, AWS, CEOS, cloud computing, Collaboration, collaboration agreements, composability, Computer architecture, Containers, Earth, Earth Observation, ODC, Open Data, Open Data Cube, policy-based governance, pubcrawl, remote sensing, sandbox, Sandboxing, Satellite, Satellites, Scalability, Servers, Tools | 
| Abstract | The Open Data Cube (ODC) initiative, with support from the Committee on Earth Observation Satellites (CEOS) System Engineering Office (SEO) has developed a state-of-the-art suite of software tools and products to facilitate the analysis of Earth Observation data. This paper presents a short summary of our novel architecture approach in a project related to the Open Data Cube (ODC) community that provides users with their own ODC sandbox environment. Users can have a sandbox environment all to themselves for the purpose of running Jupyter notebooks that leverage the ODC. This novel architecture layout will remove the necessity of hosting multiple users on a single Jupyter notebook server and provides better management tooling for handling resource usage. In this new layout each user will have their own credentials which will give them access to a personal Jupyter notebook server with access to a fully deployed ODC environment enabling exploration of solutions to problems that can be supported by Earth observation data.  |  
| URL | https://ieeexplore.ieee.org/document/9323748 | 
| DOI | 10.1109/IGARSS39084.2020.9323748 | 
| Citation Key | rizvi_novel_2020 | 
