Cisco Technology, Inc.

Ajay Madhavan

Samer Salam

Ammar Rayes

Samhith Venkatesh

Michael Michaelides

Tarunesh Ahuja

Zachariah Cherian

Ali Mehregani

Silviu Marian Jingoi

Smruti Dilip Lele

Madhavan Kasthurirangan

Ram Prasanna Govind Krishnan

Samer M Salam

Arun Mammen Thomas

Thuan Quang Truong

Arabinda Samantaray

Xixi Luo

Asad Ghayas Ahmed

The present technology addresses a need in the art for an automated tool that allows users to create network-based custom workflows for networks and associated management applications. The users do not need to have in-depth network knowledge to work with the tool or even write any code/script. The tool provides the users with a flexible graphical user interface for automated troubleshooting, network provisioning, and closed-loop automation. Further, the tool uses a domain-independent semantic machine reasoning engine as an underlying engine and a mock data engine to test and validate network-based workflows created by the users.

Advanced workflow based self-serve automation system

Systems, methods, and computer-readable media for identifying a spanning tree loop in a network environment. Spanning tree loop indicators occurring in a network environment that utilizes a spanning tree protocol are identified. The spanning tree loop indicators are correlated to identify correlated spanning tree loop indicators within the network environment. A potential spanning tree loop is recognized from a plurality of the correlated spanning tree loop indicators based on indicator types of the correlated spanning tree loop indicators. The potential spanning tree loop is remedied in the network environment in response to recognizing the potential spanning tree loop in the network environment.

Method and apparatus for automated spanning-tree loop detection in networks

Present technology is directed to preferred processing and the verification of diagnostic signatures for a plurality of network defect. The disclosed optimization process is based on expressing each Diagnostic Signature as a minimal sum of product Boolean function of associated network commands, followed by ranking of each command reference in the product terms of the Boolean expression as well as each Boolean product terms of the SOP Boolean expressions, and constructing a decision tree based on the provided rankings to thereby determine a minimum set of commands along with an preferred command dispatch sequence for evaluating a Diagnostic Signature. Further aspects include the translation of both the optimization computation (interpretation of network conditions associated with a network defect) and the computed workflow (dispatch of the command) into a series of declarative rules that can be processed by a machine reasoning engine to thereby automate the optimization process.

Optimized detection of network defect exposure in network environment

The present technology addresses a need in the art for a closed-loop automation system of a managed network that detects and fixes an issue in the managed network with minimal to no human intervention. The closed-loop automation system employs a domain-independent semantic machine reasoning engine as an underlying engine. After detecting the issue in the managed network, the system searches through a knowledge base to find a match for the detected issue. The knowledge base includes previously collected knowledge from subject matter experts and maintenance teams. The system then proposes a remedial action to a user of the managed network for approval. After approval by the user, the system implements the remedial action.

Closed-loop automation of a managed network

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Milpitas, CA, United States of America

Ajay Madhavan