Network Impact Simulation for Outage Risk Prediction
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Solution Overview
Problem
Conventional network management methods lack automated systems for predicting and simulating network responses to scheduled events or disturbances, leading to potential network outages and poor user experience due to manual, risk-based approaches without end-to-end visibility of network health.
Innovation Solution
A network impact prediction and simulation system that monitors key performance indicators, calculates outage probabilities, and provides automated network maintenance prioritization using machine learning and neural networks to minimize outage impact, offering real-time visualization and alternative path simulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual activity planning based on prior human knowledge is used, then network operators can perform maintenance tasks, but network outages and disturbances occur with no proper visibility of pre-activity assessment
Solution Approach 1:
The system performs preliminary simulation of network events before actual maintenance activities. It calculates outage probabilities and simulates network responses to scheduled events in advance, allowing operators to assess potential impacts before executing maintenance tasks, thereby preventing unexpected network disturbances
Solution Approach 2:
The system provides automated feedback through network impact predictions and simulations. It continuously monitors network health metrics and provides visibility into pre-activity assessment results, enabling operators to make informed decisions about maintenance scheduling based on predicted network responses
2Productivity
If networks are over-subscribed for peak traffic rates to maintain reasonable system cost, then resource utilization is optimized, but network performance degrades when traffic load increases
Solution Approach 1:
The system applies partial action by selectively simulating and assessing impacts on specific network elements rather than the entire network. It focuses computational resources on evaluating outage probabilities for critical nodes and links, providing targeted performance assessment without requiring complete network analysis
Solution Approach 2:
The system changes parameters by dynamically calculating outage probabilities based on current network state and traffic conditions. It adjusts performance assessments according to varying traffic loads and network configurations, enabling adaptive maintenance scheduling that accounts for changing network conditions
3Reliability
If automated network impact prediction and simulation is implemented, then network outage risks are reduced through proactive identification, but system complexity and computational requirements increase
Solution Approach 1:
The system segments the network into discrete elements (nodes, links, subnetworks) for individual assessment. It calculates outage probabilities for specific network elements separately and aggregates results to provide overall network impact predictions, making the complex analysis manageable through modular processing
Solution Approach 2:
The system creates simplified computational models (copies) of the network topology and traffic patterns. It simulates network events using these models rather than directly manipulating the actual network, reducing computational complexity while preserving essential network characteristics for accurate impact prediction
Data Source
AI summary
A system and method of simulating an effect on a network in response to a network-related event. The method can include receiving data with respect to a plurality of nodes within a network and generating a network graph with respect to each of the nodes within the network. In addition, the method can include calculating a first value based on network traffic with respect to each of the nodes based on the received data and calculating a second value based on performance with respect to each of the nodes based on the received data. Further, the method can include updating the network graph by applying the calculated first value and second value and identifying, from the updated network graph, one or more nodes from the plurality of nodes that are susceptible to an error or outage within the network.