Network Load Estimation Using Virtual Application Traffic
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Solution Overview
Problem
Existing network capacity planning systems are inefficient and time-consuming, particularly when estimating network load for multiple concurrently operating software applications, as they require extensive manual adjustment and comparison of simulated and real traffic patterns, making it difficult to predict the impact of new clients on network bandwidth requirements.
Innovation Solution
A system comprising a network guidelines estimator (NGE), network load estimator (NLE), and network load analyzer (NLA) that estimates and analyzes network load for each software application in both test and production networks, providing metrics for network load and latency, enabling quick and accurate capacity planning for multiple applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If prior network capacity systems use analytical and/or discreet event simulation tools with limited live application traffic patterns, then network load estimation can be performed, but the process becomes very time consuming, expensive and requires considerable effort to validate models and adjust simulated traffic patterns
Solution Approach 1:
The patent creates virtual copies of production network environments and applications in a test network. These virtual copies replicate traffic patterns, network configurations, and application behaviors without requiring actual production traffic data. The virtual application generates synthetic traffic that mirrors production traffic characteristics, eliminating the need to import and validate trace files from multiple production environments.
Solution Approach 2:
The system performs network load estimation in advance by deploying virtual applications in a test network before actual production deployment. The virtual application pre-generates traffic patterns and performance metrics that can be used to predict production network behavior. This preliminary action eliminates the need for time-consuming post-deployment traffic pattern validation and adjustment.
2Quantity of substance
If trace files are imported to cover all peak hours of traffic activity over several weeks, then comprehensive network load data can be collected, but it becomes very difficult to identify and compare simulated traffic with real production traffic
Solution Approach 1:
Instead of importing and managing multiple trace files from production, the system creates a single virtual application that replicates production traffic patterns. The virtual application continuously generates synthetic traffic data that mirrors production behavior, providing comprehensive coverage without the complexity of managing multiple trace files or comparing simulated traffic against real traffic samples.
Solution Approach 2:
The virtual application self-generates traffic patterns based on configured production network characteristics. It automatically creates realistic traffic flows, protocols, and patterns without requiring manual configuration or import of trace files. The system serves its own data generation needs, eliminating the complexity of external trace file management and validation.
3Measurement precision
If network analysts manually adjust pre-existing simulated traffic patterns to match network load of imported live traffic patterns, then model validation can be achieved, but the effort is challenging and not usually attempted
Solution Approach 1:
The virtual application creates an accurate copy of production network behavior through configurable parameters rather than manual adjustment. By defining production network characteristics (traffic volumes, protocols, patterns, timing), the virtual application automatically generates matching traffic patterns, eliminating the need for iterative manual adjustment and validation.
Solution Approach 2:
The system performs self-validation by comparing virtual application performance metrics against expected production metrics. The virtual application automatically adjusts its traffic generation to match production patterns based on configured parameters, eliminating the need for manual analyst intervention to validate and tune the simulation model.
4Adaptability or versatility
If the system estimates network load for multiple concurrently operating software applications, then comprehensive network capacity planning can be performed, but existing systems become very time consuming and expensive
Solution Approach 1:
The system merges multiple virtual applications into a single test network environment, allowing simultaneous estimation of network load for multiple applications. The virtual applications share the same test network infrastructure, enabling comprehensive multi-application analysis without requiring separate validation processes for each application, thus dramatically reducing time and cost.
Solution Approach 2:
The virtual application framework provides universal support for any number and type of software applications. The same virtualization and traffic generation mechanisms work across different application types and concurrency scenarios, eliminating the need for application-specific validation procedures and enabling scalable multi-application network capacity planning.
Data Source
AI summary
A network guidelines estimator (NGE) estimates a network load for each software application operating in a test network to determine network load metrics for each software application. A network load estimator (NLE) estimates a network load for one or more software applications concurrently operating in a production network responsive to the network load metrics of each of the one or more software applications. A network load analyzer (NLA) analyzes the network load for the one or more software applications concurrently operating in the production network to determine an actual network load for the production network.