SDWAN Performance Analyzer for Self-Adapting Network Compliance
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Solution Overview
Problem
Current software-defined wide area networks (SDWANs) are limited in their ability to adapt to changing network conditions, as they primarily focus on traffic engineering approaches that fail to account for the holistic health of the network, leading to potential non-compliance with client requirements for quality of service (QoS) and service level agreements (SLA).
Innovation Solution
The implementation of an SDWAN performance analyzer that monitors operational characteristics across the network, generates a network tree illustrating node relationships, and applies machine learning to determine configurable parameters such as routing tables, QoS levels, and node resources to optimize network performance and ensure compliance with client requirements in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traffic engineering approaches are used to manage SDWAN, then routing decisions can be made, but the system fails to account for holistic network health and cannot adapt to changing network conditions
Solution Approach 1:
The SDWAN system performs self-diagnosis and self-configuration by automatically monitoring network conditions, analyzing operational characteristics, and adjusting routing decisions without requiring manual administrator intervention for each change
Solution Approach 2:
The system continuously monitors network operational characteristics, feeds this information back through machine learning algorithms, and uses the learned patterns to dynamically adjust routing decisions and node parameters in real-time
2Productivity
If manual configuration and monitoring is used, then network control can be maintained, but administrator intervention is required for every adjustment and the system cannot ensure real-time compliance
Solution Approach 1:
The system automatically monitors network conditions, analyzes operational characteristics, and adjusts routing decisions without requiring manual administrator intervention for each change
Solution Approach 2:
Manual administrative actions are replaced by machine learning algorithms that automatically analyze network data and generate optimization decisions, substituting human mechanical processes with automated intelligent systems
3Reliability
If traditional network functions are used, then basic routing can be performed, but the system cannot provide self-healing capabilities or ensure compliance with QoS and SLA requirements
Solution Approach 1:
The system continuously monitors network operational characteristics, feeds this information back through machine learning algorithms, and uses the learned patterns to dynamically adjust routing decisions and node parameters in real-time
Solution Approach 2:
The machine learning algorithms continuously analyze network conditions and predict potential issues before they impact service, allowing the system to proactively adjust parameters and prevent QoS/SLA violations before they occur
Data Source
AI summary
A system and method for a self-adapting SDWAN to ensure compliance with client requirements. A SDWAN performance analyzer continuously monitors all of the nodes within an SDWAN, receiving a plurality of operational data regarding operational parameters of each node. Based on the operational data, a machine learning algorithm is applied to develop a tree-structure representative of a desired network configuration, based on the real-time state of the network, to ensure compliance with client requirements. The SDWAN performance analyzer can generate configuration commands to send to one or more of the nodes in the SDWAN to reconfigure the operational parameters of the nodes in line with the desired network configuration.