Networking Research & Infrastructure
FlyNet: An 'On-the-fly' Deeply Programmable End-to-end Network-Centric Platform for Edge-to-Core Workflows
Description
Unmanned Aerial Vehicles (also known as drones) are becoming popular in the sky. The safe, efficient, and economic operation of such drones poses a variety of challenges that have to be addressed by the science community. This project will provide tools that will allow researchers and drone application developers to address operational drone challenges by using advanced computer and network technologies.
FlyNet will provide an architecture and tools that will enable scientists to include edge computing devices in their computational workflows. This capability is critical for low latency and ultra-low latency applications like drone video analytics and route planning for drones. The proposed work will include four major tasks. First, cutting edge network and compute infrastructure will be integrated into the overall architecture to make them available as part of scientific workflows. Second, in-network processing at the network edge and core will be made available through new programming abstractions. Third, enhanced end-to-end monitoring capabilities will be offered. Finally, the architecture will leverage the Pegasus Workflow Management System to integrate in-network and edge processing capabilities.
Providing best practices and tools that enable the use of advanced cyberinfrastructure for scientific workflows will have a broad impact on society in the long term. The project team will enable access to a rich set of resources for researchers and educators from a diverse set of institutions to further democratize research. In addition, collaboration with the NSF REU (Research Experience for Undergraduates) Site in Consumer Networking will promote participation of under-served/under-represented students in project activities.
RENCI's Role
For the project, Anirban Mandal is the co-PI, Komal Thareja is the distributed systems software engineer, and Cong Wang is the senior computational and network researcher. RENCI is responsible for research and development of resource provisioning tools and algorithms for the edge-to-cloud continuum.
