Gloire Rubambiza
Research Scientist, IBM Research
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Dr. Gloire Rubambiza conducts research in AI platforms/infrastructure at IBM. Before IBM, he was a PhD student and University Fellow at Cornell, where he led research in hybrid cloud computing for digital agriculture as a member of the Cornell Institute for Digital Agriculture. He has presented at KubeCon EU and NA in 2024, regularly publishes in ACM, USENIX, and IEEE venues, and won the Best Doctoral Presentation at the ACM Richard Tapia Celebration of Diversity in Computing Conference in 2023.
Towards a Cloud-Native, Scalable and Fault-Tolerant Platform for Digital Agriculture
Digital agriculture (DA) broadly is the use of data-driven techniques toward the sustainable intensification of farm yields and efficiency, which can have major financial, environmental, and societal impacts. We present a cloud-native edge computing framework that allows agricultural decision-makers to make sustainable crop management choices in DA. This framework is powered by the KubeStellar open-source project that focuses on addressing configuration management challenges in multi-cluster environments, including edge. The proposed framework is designed with agricultural users in mind and allows researchers to rapidly deploy/manage computational AI models for plant disease detection using NASA imagery without retaining confidential stakeholder information. This system will empower agricultural stakeholders to make well-informed data-driving decisions by granting them access to accurate data on the farm and the latest advances in SI-ML disease detection in a cloud-native environment.
Experience in Designing & Implementing a Cloud Native Framework for Farm Data Analytics
This work is based on 17 months experience managing a digital agriculture platform that has aggregated and processed tens of gigabytes of data on 1500 cows on a commercial dairy farm. Significant challenges surfaced tied to multi-cluster management, fault-tolerance, and privacy as the number of applications and farm management models grew. To bridge this gap, we designed and implemented a cloud native networked system for multi-cluster configuration and management of farm data analytics that leverages KubeStellar and Software-Defined Farm paradigm. Our experience from designing, implementing and deploying this framework showcase how Kubernetes can enable farmers and agribusinesses to leverage the power of containerization and cloud-native computing to optimize workflows and streamline agricultural operations. This work presents progress towards cloud-native, scalable, and fault-tolerant data analytics in digital farming with potential environmental, financial, and societal impacts.
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