Distributed Network Impact Prediction via Virtual Simulation
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Solution Overview
Problem
Network designers face challenges in predicting the impact of changes and new implementations in distributed networks, relying on inadequate methods such as back-of-the-envelope calculations and resource-intensive field testing, which can lead to unforeseen drawbacks in system performance, particularly affecting service response time.
Innovation Solution
A method and system for predicting service criteria in distributed computer networks by collecting relevant data, learning dependencies, and preparing an input distribution to evaluate expected network operations based on specified scenarios, using modules for scenario input, relevant feature identification, and data filtering to output predicted results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If field testing is used to evaluate network changes, then prediction accuracy is improved, but time consumption and resource usage increase
Solution Approach 1:
The patent creates a virtual copy of the network environment through simulation. Instead of physically deploying changes in the actual network, the system builds a virtual model that replicates network behavior, allowing accurate prediction of performance impacts without time-consuming field testing. The simulation environment copies network topology, traffic patterns, and device characteristics to enable rapid what-if analysis.
Solution Approach 2:
The system performs preliminary actions by pre-collecting and pre-processing network data before actual changes are implemented. Historical network data is gathered in advance, and the simulation environment is prepared beforehand with configured parameters and models. This allows the actual network changes to be evaluated quickly without requiring time-consuming real-time testing during deployment.
2Measurement precision
If field testing is used to evaluate network changes, then prediction accuracy is improved, but resource consumption increases
Solution Approach 1:
The patent replaces resource-intensive field testing with a virtual simulation copy. The simulation environment replicates network behavior using computational models rather than physical network resources. This eliminates the need to consume actual network bandwidth, processing capacity, and hardware resources during testing, while still achieving accurate predictions through sophisticated virtual modeling.
3Ease of operation
If ad-hoc methods are used to estimate network impact, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The system enables self-service by providing an automated simulation platform that network operators can use independently without requiring expert intervention. The simulation environment automatically executes scenarios, processes data, and generates predictions based on configured parameters. This maintains ease of operation while dramatically improving precision through automated computational analysis rather than manual ad-hoc calculations.
Solution Approach 2:
The simulation system incorporates feedback mechanisms where actual network performance data is continuously fed back into the models to improve prediction accuracy. The system learns from real network behavior patterns and adjusts simulation parameters accordingly, enabling operators to receive accurate predictions while maintaining simple operation through automated feedback-driven model refinement.
Data Source
AI summary
The present invention pertains to specifying, analyzing and evaluating systems such as distributed content distribution networks. Systems and methods are provided to predict how new deployments and changes to existing architectures will impact the networks. A network design may prepare a “what-if” scenario and the impact of this scenario may be predicted. Various tools are provided to determine relevant network variables and collect data about such variables, to learn what dependencies may exist among relevant variables, to prepare an input distribution and to output a predicted impact for the what-if scenario. Thus, a system designer is able to see a predicted impact that a network change or new deployment will have without having to resort to back of the envelope calculation or costly field deployments.