Adaptive Session Intelligence Extender for Custom Application Monitoring
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Solution Overview
Problem
Service providers face challenges in monitoring and troubleshooting network applications, especially when proprietary or third-party protocols are used, as existing systems require protocol specifications that may not be available.
Innovation Solution
The implementation of an Adaptive Session Intelligence (ASI) extender and network monitoring system that utilizes ASI data sets to monitor and troubleshoot customized applications at a transactional level, including proprietary protocols, by generating templates and analyzing transactional data for performance metrics and error codes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a network monitoring application requires protocol specifications to monitor performance data, then monitoring accuracy is improved, but the system cannot monitor proprietary or third-party protocols for which specifications are unavailable
Solution Approach 1:
The patent introduces an intermediary component that acts as a bridge between the network monitoring system and proprietary protocols. This intermediary captures raw protocol data packets and uses machine learning models to infer protocol structure and semantics without requiring formal specifications, thereby enabling monitoring of protocols that would otherwise be incompatible with specification-based monitoring systems
Solution Approach 2:
The system dynamically changes its monitoring parameters by adapting to different protocol types through machine learning. Instead of using fixed monitoring parameters based on pre-defined specifications, the system learns and adjusts parameters such as packet structure, field definitions, and performance metrics based on observed traffic patterns, enabling versatile monitoring across proprietary and standard protocols
2Adaptability or versatility
If a monitoring system supports multiple proprietary and third-party protocols without specifications, then protocol compatibility is improved, but the complexity of the monitoring system increases
Solution Approach 1:
The monitoring system performs self-service by automatically discovering and adapting to proprietary protocols through machine learning without requiring manual configuration or external specification inputs. The system autonomously learns protocol structures from captured traffic and configures its own monitoring parameters, eliminating the need for complex manual setup procedures for each new protocol
Solution Approach 2:
The patent implements a universal monitoring architecture that can handle multiple protocol types through a single unified machine learning-based engine. This universal approach replaces the need for separate specialized monitoring components for each protocol, reducing overall system complexity while maintaining broad protocol compatibility through adaptive learning capabilities
3Difficulty of detecting and measuring
If protocol specifications are provided for monitoring, then troubleshooting capability is improved, but proprietary protocols without available specifications cannot be monitored
Solution Approach 1:
The machine learning-based intermediary analyzes raw protocol packets and infers protocol semantics to enable troubleshooting of proprietary protocols. By acting as an intelligent mediator that translates unknown protocol formats into understandable performance metrics and error conditions, the system provides troubleshooting capabilities for protocols without formal specifications
Solution Approach 2:
The system implements feedback mechanisms where machine learning models continuously learn from observed protocol behavior and performance data. This feedback loop enables the system to improve its understanding of proprietary protocols over time, enhancing troubleshooting capability by identifying patterns, anomalies, and root causes based on learned protocol characteristics rather than relying on pre-defined specifications
Data Source
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AI summary
A method for monitoring performance of customized applications at transaction level in a computer network is provided. The method includes receiving, from a user, information related to a customized application. The received information includes at least an application definition and information related to customized application protocol. A template is generated for the customized application based on the received information. Performance of the customized application is monitored at transaction level using the generated template.