Cisco Technology, Inc.

Samer M Salam

Salvatore Valenza

Arun G Khanna

Taha Hajar

Nikodin Ristanovic

Matthew Aaron Silverman

Ammar Rayes

Balaji R Venkatraman

James Frank Florwick

Min Se Kim

Carlos M Pignataro

Eric Albin Voit

Pradeep Kumar Kathail

Mark Ammar Rayes

Kasi Nalamalapu

Arman Rezaee

Stefano Ferrari

Ajay Madhavan

Ali Mehregani

Silviu Marian Jingoi

Samhith Venkatesh

Jad Al Aaraj

This disclosure describes techniques for emulating the operations of a computer network. In some cases, a method includes receiving first configuration data associated with a first device and second configuration data associated with a second device in a network; determining a first device representation associated with the first device based on the first configuration data and a second device representation associated with the second device based on the second configuration data; determining, based on the first and the second device representations, a first device cluster, wherein the first device cluster comprises a plurality of clustered devices comprising the first device and the second device; determining a first device model associated with the first device cluster, wherein the first device model represents a first device behavior that is common across the plurality of clustered devices; and generating, based on the first device model, an emulation environment associated with the network.

Computer network emulation using network device clusters

Devices, systems, methods, and processes for managing network devices through generated predictions and associated confidence levels are described herein. Networks within a floorplan can be operated at full capacity all day in an inefficient way when not adjusted due to traffic patterns and seasonality changes. Data related to the topology of the network, along with historical data can be utilized to generate predictions of various network needs. For example, the overall network throughput capacity needs may be predicted for a series of points in the future. An associated confidence level can be generated as well including one or more confidence intervals. These can be utilized to select a future need for the network and generate a corresponding sustainable network configuration for the network devices and/or their transceivers that can provide sufficient network needs while minimizing the overall power used. This can be automated over time once trust has been established.

Client-aware floorplan management with predicted confidence levels

Described herein are devices and methods for calculating a network sustainability index (NSI) and providing specific remediation/action plans to achieve better sustainability in network infrastructures. The NSI is calculated based on one or more of three main parameters: a reporting parameter, an optimization parameter, and/or a device energy efficiency rating. The reporting parameter may evaluate the capability of each device to report its power consumption, considering factors such as granularity, component-level reporting, and reporting frequency. The optimization parameter may assess whether a device participates in network energy optimization. The device energy efficiency rating may evaluate the energy efficiency of each device. A recommendation portal offers actionable insights and remediation plans based on the calculated NSI. The devices and methods allow administrators to list eligible devices for energy optimization, ensuring a comprehensive approach to network sustainability and facilitating targeted measures for improved energy efficiency.

Network sustainability index

The present technology is directed to visualizing a Wi-Fi access point (AP) signal propagation pattern through multiple floors. The present technology can execute a Wi-Fi signal propagation model corresponding to a first AP on a first floor of a building plan and a second AP on a second floor of the building plan. The Wi-Fi signal propagation model calculates a Wi-Fi signal propagation pattern for a plurality of APs including the first AP and the second AP. The present technology can further present a visualization of the Wi-Fi signal propagation pattern for the plurality of APs, wherein the Wi-Fi signal propagation pattern for the first AP on the first floor of the building plan projects onto the second floor of the building plan.

Three-dimensional visualization of Wi-Fi signal propagation through multiple floors

The present technology is directed to providing a 3-D visualization of a Wi-Fi signal propagation pattern based on telemetry data. The present technology can receive telemetry data for a Wi-Fi access point located at a location of a building plan in a Wi-Fi visualization system, store the telemetry data with a timestamp, determine a change in a Wi-Fi coverage for the Wi-Fi access point based on the telemetry data, and present a visualization illustrating the change in the Wi-Fi coverage for the Wi-Fi access point. The present technology can further present an animation of the change in the Wi-Fi coverage for the Wi-Fi access point based on the stored telemetry data.

Three-dimensional visualization of Wi-Fi signal propagation based on telemetry data

The present disclosure is directed to 3-D visualization of wireless signal propagation representing wireless signal strength and interference in 3-D space. The present technology can identify a plurality of access points (APs) in the 3-D space, determine a wireless signal strength for each of the plurality of APs, and determine an interference with the wireless signal strength of each of the plurality of APs, wherein the interference is caused by a neighboring AP of the plurality of APs in the 3-D space. The present technology can further present a 3-D visualization of a wireless signal propagation pattern representing the wireless signal strength from each of the plurality of APs in the 3-D space and the interference from the neighboring AP.

Simulation of Wi-Fi signal propagation in three-dimensional visualization

The present technology is directed to visualizing a Wi-Fi signal propagation in 3-D based on a building plan defined with implicit geometry. The present technology can extract data from a building plan file where a 3-D space and objects in a building plan are defined with implicit geometry, transform the implicit geometry to explicit geometry for the 3-D space and the objects to translate a positioning of the 3-D space and objects from a local coordinate system to a global coordinate system, calculate a RF propagation pattern in the 3-D space based on a RF propagation model for a plurality of Wi-Fi access points located in the 3-D space, and present a 3-D visualization of the RF propagation pattern in the 3-D space.

Three-dimensional visualization of Wi-Fi signal propagation based on building plan with implicit geometry

The present technology is directed to visualizing a Wi-Fi signal propagation in 3-D at various heights and locations. The present technology can calculate a radio frequency (RF) propagation pattern for a Wi-Fi access point (AP) based on a RF propagation model for the Wi-Fi AP and overlay the RF propagation pattern for the Wi-Fi AP over a visualization of the building plan to present a 3-D visualization of the RF propagation pattern of the 3-D space. In particular, the present technology can project a plurality of ray-paths in various directions in a 3-D space originated from the Wi-Fi AP and determine whether the ray-paths interface with objects defined in the building plan. The present technology can segment the respective ray-path into contiguous segments of substantially uniform mediums for each ray-path that interface with the objects and determine a RF signal strength at points along the contiguous segments of the ray-paths.

Three-dimensional visualization of Wi-Fi signal propagation

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

In one embodiment, a first device identifies a region of interest in video in which a light source of a second device is present by: using a current frame of the video and a prior frame of the video to compute a difference frame, performing thresholding on the current frame to form a threshold frame, and performing pixel-wise conjunction operations between the difference frame and the threshold frame, to identify a centroid of the light source of the second device. The first device detects a message within the region of interest transmitted by the second device via its light source. The device provides the message for review by a user.

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Beirut, Lebanon

Samer M Salam