TY - GEN
T1 - Software Managed Networks via Coarsening
AU - Dogga, Pradeep
AU - Singh, Rachee
AU - Nath, Suman
AU - Netravali, Ravi
AU - Palsberg, Jens
AU - Varghese, George
N1 - Publisher Copyright:
© 2025 Copyright held by the owner/author(s).
PY - 2025/11/17
Y1 - 2025/11/17
N2 - 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 sc6ale 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.
AB - 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 sc6ale 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.
KW - AIOps
KW - capacity planning
KW - network management
UR - https://www.scopus.com/pages/publications/105023673652
UR - https://www.scopus.com/pages/publications/105023673652#tab=citedBy
U2 - 10.1145/3772356.3772393
DO - 10.1145/3772356.3772393
M3 - Conference contribution
AN - SCOPUS:105023673652
T3 - HotNets 2025 - Proceedings of the 2025 24th ACM Workshop on Hot Topics in Networks
SP - 201
EP - 209
BT - HotNets 2025 - Proceedings of the 2025 24th ACM Workshop on Hot Topics in Networks
PB - Association for Computing Machinery, Inc
T2 - 24th ACM Workshop on Hot Topics in Networks, HotNets 2025
Y2 - 17 November 2025 through 18 November 2025
ER -