Predictive Network Topology Analytics for Proactive Remediation
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Solution Overview
Problem
Network providers face challenges in comprehending the relationships between entities in complex cloud networks, leading to difficulties in optimizing and managing these networks effectively.
Innovation Solution
A system that logs network topology information over time, predicts future characteristics, computes a signature based on these characteristics, and determines remediation plans to prevent anomalies and failures by analyzing relationships between entities in the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If network providers use orchestration systems to manage cloud networks, then network deployment and management become automated, but network providers still cannot comprehend the relationships between network entities
Solution Approach 1:
The patent introduces an intermediary system that sits between the orchestration system and network providers. This intermediary automatically discovers, tracks, and visualizes relationships between network entities (services, nodes, pods, containers), transforming raw orchestration data into comprehensible relationship maps that providers can understand without losing the automated management benefits.
Solution Approach 2:
The patent replaces manual network analysis and relationship tracking with automated computational methods. Machine learning algorithms and graph analysis techniques automatically process orchestration data to identify and visualize network relationships, substituting human analytical efforts with automated systems that provide continuous relationship comprehension.
2Loss of information
If network providers manually analyze network relationships, then they can comprehend entity relationships, but network management becomes extremely difficult and time-consuming
Solution Approach 1:
The system performs preliminary automatic discovery and relationship mapping continuously in the background before providers need to analyze the network. Relationship graphs are pre-computed and updated automatically, so when providers need to comprehend network relationships, the analysis is already complete and ready for visualization, eliminating time-consuming manual analysis.
Solution Approach 2:
The network relationship analysis system operates autonomously, automatically discovering entities, tracking relationships, and updating visualizations without human intervention. The system serves itself by continuously monitoring orchestration data and maintaining accurate relationship maps, freeing providers from manual network analysis tasks.
3Ease of operation
If network providers visualize network topologies, then they can manage networks more effectively, but the complexity of large geographically dispersed systems makes visualization difficult
Solution Approach 1:
The patent segments the complex network topology into hierarchical levels and modular components. Instead of displaying all entities simultaneously, the system divides the network into manageable segments (clusters, zones, logical groups) that can be visualized at appropriate scales, making large geographically dispersed systems comprehensible without overwhelming providers with complexity.
Solution Approach 2:
The system adds dimensional organization to network visualization by introducing logical grouping dimensions, hierarchical levels, and spatial metaphors. Complex network relationships are projected into multi-dimensional visual spaces where entities are organized by function, ownership, and relationships rather than physical location alone, enabling effective visualization of large distributed systems.
Data Source
AI summary
Techniques for recommending plans to remediate a network topologies are disclosed. The techniques include logging network topology information identifying relationships between entities in a network topology over a number of time periods. The techniques also include, using the logged network topology information, predicting characteristics of the network at a future time period. The techniques further include computing a signature based on the predicted characteristics and, using the signature, determining whether the predicted characteristics meet remediation criteria. Additionally, the techniques include, in response to determining the predicted characteristics meet the remediation criteria, determining a remediation plan for the current network topology and presenting the plan to a user.