Cisco technology, inc. (20240323112). CROSS-APPLICATION PREDICTIVE ROUTING simplified abstract
Contents
CROSS-APPLICATION PREDICTIVE ROUTING
Organization Name
Inventor(s)
Grégory Mermoud of Venthône (CH)
Grégoire Magendie of Lamorlaye (FR)
Jean-Philippe Vasseur of Combloux (FR)
CROSS-APPLICATION PREDICTIVE ROUTING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240323112 titled 'CROSS-APPLICATION PREDICTIVE ROUTING
The abstract describes a device that predicts quality metrics for different network paths for a variety of applications, then generates a congestion risk prediction model to assign traffic flows to selected paths based on the predicted metrics and congestion risk.
- Predicts quality metrics for network paths for multiple applications
- Generates congestion risk prediction model for traffic flows and paths
- Assigns traffic flows to selected paths based on predicted metrics and congestion risk
- Routes traffic flows in the network via the selected paths
- Utilizes constrained optimization for traffic flow assignment
Potential Applications: - Network traffic management - Quality of Service optimization - Congestion avoidance in networks
Problems Solved: - Efficient routing of traffic flows - Minimization of congestion risks - Optimization of network performance
Benefits: - Improved network efficiency - Enhanced user experience - Reduced congestion and delays
Commercial Applications: Title: "Network Traffic Optimization Technology for Enhanced Performance" This technology can be utilized by telecommunications companies, internet service providers, and network operators to optimize network traffic flow, improve quality of service, and enhance overall network performance. The market implications include increased customer satisfaction, reduced network downtime, and improved operational efficiency.
Prior Art: Researchers in the field of network optimization and traffic management have explored various techniques to improve network performance and reduce congestion risks. Prior studies have focused on traffic engineering, quality of service algorithms, and congestion control mechanisms.
Frequently Updated Research: Ongoing research in this field includes advancements in machine learning algorithms for congestion prediction, development of dynamic routing protocols for adaptive traffic management, and integration of artificial intelligence for real-time network optimization.
Questions about Network Traffic Optimization Technology: 1. How does this technology differ from traditional traffic engineering methods? This technology utilizes predictive models and constrained optimization for traffic flow assignment, improving network efficiency and congestion management compared to traditional methods.
2. What are the key factors considered in predicting congestion risks for traffic flows? The congestion risk prediction model takes into account the type of applications, associated traffic flows, and network paths to assess the likelihood of congestion and optimize traffic routing.
Original Abstract Submitted
in one embodiment, a device predicts, for each of a plurality of applications accessible via a network, quality metrics for different network paths where traffic for that application be routed via one or more paths among the different network paths. the device generates a congestion risk prediction model that predicts a risk of traffic congestion for a particular combination of: applications from among the plurality of applications, traffic flows associated with those applications, and paths among the network paths via which those traffic flows may be routed. the device performs a constrained optimization based on the predicted quality metrics and on the risk of traffic congestion predicted by the model, to assign traffic flows for the applications to a selected subset of the different paths. the device causes the traffic flows to be routed in the network via the selected subset of the different paths to which they are assigned.
