Abstract
In context of distributed monitoring and anomaly detection, when a networking device performs anomaly detection based on local data, such as when a remote controller is not reachable during network convergence or other network issues. Anomaly relevance improves if telemetry data used for anomaly detection comes not only from a local device, but also from the device's immediate surroundings (e.g., physical neighbors, protocol peers, redundancy units, etc.). Presented herein are techniques through which a device can reach its own and nearby telemetry sources in a manner that may follow an effective network topology and configuration. Thus, techniques herein may enable the design of intelligent autonomous agents that can operate beyond the scope of a host (and can integrate nearby information to make smarter assessments) but below the network scale and, hence, are capable of scaling well in order to sample data more quickly and merge data more accurately.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Zacks, Dave; Gao, Jane (Zizhen); Nainar, Nagendra Kumar; Pignataro, Carlos M.; and Goloubew, Dmitry, "AUTONOMOUS COLLECTION DETECTION AND REMEDIATION DECISIONS BASED ON LOCAL MODELS AND LOCALLY SOURCED DATA", Technical Disclosure Commons, (November 28, 2021)
https://www.tdcommons.org/dpubs_series/4747