Overview
Cloud computing has emerged as the predominant paradigm, underpinning nearly all industrial applications and a significant portion of academic and research projects. Since its inception and widespread adoption, migrating to cloud computing has posed substantial challenges for numerous organizations and enterprises. Leveraging cloud technologies to process big data near their physical locations represents an ideal use case. These cloud resources provide the necessary infrastructure and tools, especially when combined with high-performance computing (HPC) capabilities. The integration of GPUs and other pervasive technologies—such as application containerization and microservice architecture—across public and private cloud infrastructures further supports computation-intensive AI/ML workloads that used to reside only within HPC environments.
Session Focus
This session focuses on use cases involving both Cloud and HPC computing. The goal is to assess the current landscape and outline the steps needed to facilitate the broader adoption of cloud computing in Earth Observation and Earth Modeling data processing. We invite contributions that explore various cloud computing initiatives within these domains, including but not limited to:
Big Data Infrastructures and Platforms: Case studies, techniques, models, and algorithms for data processing on the cloud.
Cloud Federations and Interoperability: Scalability and interoperability across different domains, multi-provenance data management, security, privacy, and green and sustainable computing practices.
Cloud Applications, Infrastructure, and Platforms: IaaS, PaaS, SaaS, and XaaS solutions.
Cloud-Native AI/ML Frameworks: Tools and frameworks for processing data using AI and ML on the cloud.
Cloud Storage and File Systems: Solutions for big data storage and management.
Operational Systems and Services: Deployment and management of operational systems on the cloud.
Data Lakes and Warehouses: Implementation and management of data lakes and warehouses on cloud platforms.
Convergence of Cloud Computing and HPC: Workload unification for Earth Observation (EO) data processing between cloud and HPC
We encourage researchers, practitioners, and industry experts to share their insights, case studies, and innovative solutions that promote the integration of cloud computing and HPC in Earth Observation and Earth Modeling.
Cloud computing has emerged as the predominant paradigm, underpinning nearly all industrial applications and a significant portion of academic and research projects. Since its inception and widespread adoption, migrating to cloud computing has posed substantial challenges for numerous organizations and enterprises. Leveraging cloud technologies to process big data near their physical locations represents an ideal use case. These cloud resources provide the necessary infrastructure and tools, especially when combined with high-performance computing (HPC) capabilities. The integration of GPUs and other pervasive technologies—such as application containerization and microservice architecture—across public and private cloud infrastructures further supports computation-intensive AI/ML workloads that used to reside only within HPC environments.
Session Focus
This session focuses on use cases involving both Cloud and HPC computing. The goal is to assess the current landscape and outline the steps needed to facilitate the broader adoption of cloud computing in Earth Observation and Earth Modeling data processing. We invite contributions that explore various cloud computing initiatives within these domains, including but not limited to:
Big Data Infrastructures and Platforms: Case studies, techniques, models, and algorithms for data processing on the cloud.
Cloud Federations and Interoperability: Scalability and interoperability across different domains, multi-provenance data management, security, privacy, and green and sustainable computing practices.
Cloud Applications, Infrastructure, and Platforms: IaaS, PaaS, SaaS, and XaaS solutions.
Cloud-Native AI/ML Frameworks: Tools and frameworks for processing data using AI and ML on the cloud.
Cloud Storage and File Systems: Solutions for big data storage and management.
Operational Systems and Services: Deployment and management of operational systems on the cloud.
Data Lakes and Warehouses: Implementation and management of data lakes and warehouses on cloud platforms.
Convergence of Cloud Computing and HPC: Workload unification for Earth Observation (EO) data processing between cloud and HPC
We encourage researchers, practitioners, and industry experts to share their insights, case studies, and innovative solutions that promote the integration of cloud computing and HPC in Earth Observation and Earth Modeling.