Fault Analytics Framework for QoS Services
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Service providers face challenges in identifying and managing faults in Quality of Service (QoS) based services, particularly in large-scale operator networks, as existing methods are not scalable and often fail to accurately determine the root cause of service failures.
Innovation Solution
Implementing a control server that models deterministic communication behavior between devices in the operator network, allowing real-time monitoring and identification of deviations from expected behavior to pinpoint faults and their locations, using state data structures and packet tracing to log and analyze network events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual fault troubleshooting methods are used in large-scale operator networks, then technicians can identify service failures, but the method is not scalable and requires significant manual intervention
Solution Approach 1:
The system implements self-service through automated fault detection and identification. The control server automatically monitors network devices, compares actual behavior against modeled deterministic communication patterns, and identifies faults without requiring manual technician intervention. This automation enables the system to serve itself in detecting and reporting faults, resolving the contradiction between detection accuracy and scalability.
Solution Approach 2:
The patent replaces the mechanical manual troubleshooting process with an automated electronic monitoring system. Instead of technicians physically deploying packet capture devices and manually analyzing logs, the system uses electronic modeling and automated comparison of network traffic against expected behavior patterns. This substitution enables scalable fault detection across large-scale networks while maintaining high detection accuracy.
2Reliability
If packet capture devices are deployed to log network transmissions for fault analysis, then fault records can be obtained, but the process requires recreating the problem and manual log analysis which is time-consuming and may not always identify the root cause
Solution Approach 1:
The system performs preliminary action by pre-modeling the deterministic communication behavior between network devices before faults occur. The control server creates models of expected packet flows, timing, and sequences in advance. When faults occur, the system simply compares actual behavior against these pre-established models, eliminating the need to recreate problems or manually analyze logs. This preliminary modeling enables immediate fault identification upon deviation detection.
Solution Approach 2:
The system implements continuous feedback by monitoring network device behavior in real-time and comparing it against the modeled expected behavior. When deviations are detected, the system immediately identifies the fault location and cause by analyzing which expected communication pattern was violated. This real-time feedback mechanism eliminates delays associated with manual log analysis and problem recreation, providing both high reliability and rapid detection.
3Measurement precision
If technicians manually sift through packet transmission logs to identify faults, then fault location can be determined, but the process is complex and may not always succeed in identifying the root cause
Solution Approach 1:
The patent replaces the complex mechanical process of manual log sifting with automated electronic analysis. The control server electronically compares actual network traffic against pre-modeled expected behavior patterns, automatically identifying deviations and their causes. This substitution transforms the complex manual troubleshooting process into a simple automated comparison operation, maintaining high fault location precision while dramatically reducing process complexity.
Solution Approach 2:
The system changes the parameter of analysis from examining raw packet data to comparing behavioral patterns. Instead of manually sifting through individual packets, the system models the expected parameters of communication (timing, sequence, destinations) and compares actual parameters against these models. This parameter transformation simplifies the analysis process while maintaining or improving fault location precision through systematic pattern matching.
Data Source
AI summary
A device may be configured to determine a current state of each of multiple operator network devices that provide a service via an operator network. The device may determine an allowable event at an operator network device based on the current state of the operator network device and model information that models behavior of the operator network device for the service. The device may monitor events at the operator network devices during a session. The device may detect that an allowable event for the operator network device does not occur during the session. The device may determine that a fault occurred at the operator network device during the session based on the allowable event not being detected at the operator network device. The device may provide fault information that indicates the fault occurred at the operator network device.