Network Capacity Planning via Simplified Simulation
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Solution Overview
Problem
Current network capacity planning tools face complexity in simulating network communications due to non-linear scaling of network behavior with traffic, requiring extensive computing resources and man-hours, especially during network-wide application deployments or upgrades, and struggle to predict actual performance under increased remote access and shared resource traffic.
Innovation Solution
A simplified system that receives data on application deployment attributes, network topology, and performance attributes using a reduced set of element attributes, allowing for iterative simulation of network performance through a graphical user interface, displaying response times and enabling what-if scenario analysis to predict network and application performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional network simulation methods are used to model network topology and application behavior, then simulation accuracy is improved, but device complexity and computing resources required increase significantly
Solution Approach 1:
The patent extracts and models only the essential performance characteristics of network applications (response times, transaction rates, message sizes) rather than simulating complete application behaviors. This selective extraction maintains simulation accuracy for capacity planning while dramatically reducing model complexity and computational requirements.
Solution Approach 2:
The patent transforms complex application behaviors into simplified performance parameters such as response times, transaction rates, and message sizes. By changing the representation from detailed behavioral models to aggregated performance metrics, the simulation becomes computationally tractable while preserving the essential characteristics needed for capacity planning.
2Reliability
If detailed network models with many elements are created to capture interaction effects, then simulation realism is improved, but loss of time for setup and computation increases
Solution Approach 1:
The patent performs preliminary characterization of applications by collecting performance data (response times, transaction rates) before the simulation. This preliminary action captures essential interaction effects and application behaviors in advance, allowing the actual simulation to proceed quickly without requiring detailed real-time modeling of each interaction.
Solution Approach 2:
Instead of creating detailed models of actual applications, the patent creates simplified copy representations that capture essential performance characteristics. These copied performance profiles can be reused across multiple simulations, dramatically reducing setup time while maintaining realistic simulation outcomes.
3Measurement precision
If network simulations account for non-linear scaling and TCP effects, then prediction accuracy under load is improved, but device complexity and computing resources increase
Solution Approach 1:
The patent incorporates non-linear network behavior and TCP effects by using performance parameters measured under various load conditions rather than attempting to simulate the underlying complex protocols. By changing from protocol-level simulation to performance-metric-based modeling, the system captures non-linear effects accurately while using minimal computational resources.
Data Source
AI summary
Data representing application deployment attributes, network topology, and network performance attributes based on a reduced set of element attributes is utilized to simulate application deployment. The data may be received from a user directly, a program that models a network topology or application behavior, and a wizard that implies the data based on an interview process. The simulation may be based on application deployment attributes including application traffic pattern, application message sizes, network topology, and network performance attributes. The element attributes may be determined from a lookup table of element operating characteristics that may contain element maximum and minimum boundary operating values utilized to interpolate other operating conditions. Application response time may be derived using an iterative analysis based on multiple instances of one or more applications wherein a predetermined number of iterations is used or until a substantially steady state of network performance is achieved.