The Limitations of Current AIOps Approaches
The article discusses the limitations of current AIOps platforms, which learn from their own data but fail to leverage knowledge from other networks. It proposes a new architecture called Quadratic Intelligence Swarm (QIS) that enables the transfer of operational intelligence across networks.
Why it matters
The QIS architecture has the potential to significantly improve the performance and efficiency of AIOps platforms by enabling the transfer of operational intelligence across networks, addressing a key limitation of current approaches.
Key Points
- 1Current AIOps platforms are limited by their inability to learn from other networks, leading to repeated issues like BGP convergence problems
- 2Federated learning is not a suitable solution for network telemetry due to the real-time operational requirements and topological heterogeneity of enterprise networks
- 3The QIS architecture enables the routing of structured
- 4 between networks, allowing the transfer of hard-won operational intelligence
- 5QIS leverages a combinatorially larger surface of learning paths compared to centralized AIOps, at a logarithmic compute cost per route
Details
The article highlights the limitations of current AIOps platforms, which ingest telemetry data from thousands of network devices but fail to leverage knowledge from other networks. This leads to repeated issues, such as BGP convergence problems, where a network experiences an issue that was previously solved by another network but the solution is not shared. The article explains why federated learning is not a suitable solution, as network telemetry is a time-series problem with real-time operational requirements, and enterprise network topologies are highly heterogeneous, making it difficult to average gradients across different topologies. The proposed Quadratic Intelligence Swarm (QIS) architecture addresses these limitations by enabling the routing of structured
No comments yet
Be the first to comment