Pradeep Dogga
I am a software engineer in GGI working on Autonomous Network Operations to improve the operability and reliability of our planet-scale network that powers the AI-era of computing.
Prior to this, I earned my Ph.D. in Computer Science from UCLA. My research interests are broadly in using AI and ML techniques to improve networks and systems. My PhD thesis focus was on assisting end-to-end debugging workflows in production-scale distributed systems. I was advised by George Varghese and Ravi Netravali. Prior to joining UCLA, I received my B.Tech.(Hons.) from the Computer Science and Engineering Department at Indian Institute of Technology, Kharagpur where I was advised by Sandip Chakraborty and Subrata Mitra.
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We propose moving from Software Defined Networks (SDN) to Software Managed Networks (SMN) where all information for managing the life cycle of a network (from deployment to operations to upgrades), across all layers (from Layer 1 through 7) is stored in a central repository. Crucially, a SMN also has a generalized control plane that, unlike SDN, controls all aspects of the cloud including traffic management (e.g., capacity planning) and reliability (e.g., incident routing) at both short (minutes) and large (years) time scales. Just as SDN allows better routing, a SMN improves visibility and enables cross-layer optimizations for faster response to failures and better network planning and operations. Implemented naively, SMN for planetary scale networks requires orders of magnitude larger and more heterogeneous data (e.g., alerts, logs) than SDN. We address this using coarsening - mapping complex data to a more compact abstract representation that has approximately the same effect, and is more scalable, maintainable, and learnable. We show examples including Coarse Bandwidth Logs for capacity planning and Coarse Dependency Graphs for incident routing. Coarse Dependency Graphs improve an incident routing metric from 45% to 78% while for a distributed approach like Scouts the same metric was 22%. We end by discussing how to realize SMN, and suggest cross-layer optimizations and coarsenings for other operational and planning problems in networks.
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