Extensible Analytics Engine for Network Traffic Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Network administrators face challenges in efficiently analyzing network traffic data to diagnose issues and improve network performance due to the time-consuming and resource-intensive nature of manual analysis, and the need for customized analysis methods that vary across different industries and business functions.
Innovation Solution
A network traffic analysis system that includes a plug-in network traffic analysis module installed on a network traffic recommendation engine, which collects and analyzes data to output recommendations for adjusting policies and improving network traffic efficiency, allowing for extensibility through third-party modules to support specific analysis needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual network traffic data analysis is performed, then customization and depth of analysis can be adjusted, but time consumption and resource requirements increase significantly
Solution Approach 1:
The system enables self-service analysis by allowing network administrators to define custom analysis parameters, select data types, and configure processing rules without requiring manual intervention for each analysis task. The automated engine executes analyses based on pre-configured parameters, freeing administrators from time-consuming manual processing while maintaining customization capabilities.
Solution Approach 2:
The system performs preliminary configuration of analysis parameters, data collection rules, and processing logic before actual analysis execution. By pre-defining analysis templates and parameters, the system enables rapid execution of customized analyses without requiring administrators to manually configure each detail during analysis time.
2Measurement precision
If comprehensive network traffic data collection is implemented, then analysis depth and diagnostic capability improve, but system complexity and resource consumption increase
Solution Approach 1:
The system segments network traffic data collection and analysis into distinct modular components: data collectors for various traffic types, processors for different analysis functions, and result generators for specific diagnostic outputs. This segmentation allows comprehensive analysis capability while reducing overall system complexity through modular architecture, as each component can be independently configured and managed.
Solution Approach 2:
The system dynamically adjusts data collection scope and analysis depth based on configured parameters and actual network conditions. Administrators can specify which data types to collect, what analysis granularities are needed, and the system adapts its behavior accordingly, maintaining measurement precision while avoiding unnecessary complexity from collecting or processing all possible data uniformly.
3Productivity
If automated analysis recommendations are generated, then administrative workload is reduced, but accuracy and relevance of recommendations may decrease without human expertise
Solution Approach 1:
The system incorporates feedback mechanisms where automated analysis recommendations are presented to network administrators for review and validation. Administrators can confirm, modify, or reject recommendations based on their expertise and contextual understanding. This feedback loop maintains high recommendation accuracy while preserving the productivity benefits of automation, as the system learns from administrator corrections to improve future automated recommendations.
Solution Approach 2:
The system acts as an intermediary between raw network data and administrator decision-making. It processes vast amounts of data through automated analysis, presenting summarized insights and recommendations that administrators can quickly review and act upon. This intermediary function amplifies administrator effectiveness by handling routine data processing while preserving human expertise for critical judgment and exception handling.
4Adaptability or versatility
If multiple analysis modules are integrated, then system versatility and functionality improve, but ease of operation and maintenance decreases
Solution Approach 1:
The system implements a universal framework that supports multiple analysis functions through a common platform. Administrators use a unified interface to configure and execute various analysis types (traffic monitoring, security analysis, performance optimization, etc.) without needing to learn separate systems for each function. This multi-functionality approach improves versatility while maintaining ease of operation through consistent user experience and standardized procedures.
Data Source
AI summary
A method and system for using plug-in analysis modules to analyze network traffic data is disclosed. The network has computing devices coupled to a network traffic appliance that routes data to and from the computing devices. A plug-in network analysis module is installed on a network traffic recommendation engine. The network analysis module is run to obtain selected network traffic data on the network. The selected network traffic data is analyzed via the network analysis module. A recommendation is output based on the selected network traffic data. A policy is adjusted based on the recommendation to improve the efficiency of the network traffic to the computing devices.