CDN Node Fault Detection via Detection End Failure Rate Analysis
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Solution Overview
Problem
Current CDN technologies face challenges in accurately determining whether poor network performance is due to a faulty detection end or a faulty CDN node, leading to inefficient maintenance and performance optimization.
Innovation Solution
A CDN processing method and device that obtain response data from CDN nodes, calculate detection success and failure rates, and compare these rates with target thresholds to determine if the detection end is faulty, thereby distinguishing between detection end and CDN node issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CDN node detection is performed using third-party platforms, then CDN node resources can be detected, but it is difficult to accurately determine whether poor network performance is caused by the detection end or the CDN node
Solution Approach 1:
The patent segments the detection system into multiple independent detection ends, each capable of performing detection tasks. By distributing detection tasks across multiple ends and analyzing their respective performance, the system can isolate whether issues originate from the detection end or the CDN node, thereby improving measurement precision without significantly increasing overall system complexity.
Solution Approach 2:
The patent implements a feedback mechanism where detection results from multiple detection ends are collected and analyzed. The system compares performance data across different detection ends to determine whether poor network performance is caused by detection end faults or actual CDN node issues, enabling accurate identification through feedback analysis.
2Reliability
If multiple detection ends are used to detect CDN nodes, then detection reliability can be improved, but the complexity of determining whether the detection end or CDN node is faulty increases
Solution Approach 1:
The patent divides the detection system into multiple independent detection ends, allowing parallel detection operations. This segmentation improves reliability through redundancy while maintaining manageable complexity by keeping each detection end independent and standardized.
Solution Approach 2:
The patent employs multiple detection ends with identical or similar detection capabilities and protocols. This homogeneity simplifies fault determination because all detection ends operate under the same conditions, making it easier to compare results and identify whether issues are systematic (detection end) or specific to certain CDN nodes.
3Productivity
If CDN node performance is monitored continuously, then network performance can be improved, but it is difficult to distinguish between detection end faults and actual CDN node issues
Solution Approach 1:
The patent implements continuous monitoring with feedback analysis across multiple detection ends. By continuously collecting performance data and comparing results from different detection ends, the system can identify patterns that indicate detection end faults versus actual CDN node performance issues, maintaining high network performance monitoring while improving fault identification accuracy.
Solution Approach 2:
The patent performs preliminary detection using multiple detection ends before making fault determinations. This preliminary action of gathering data from multiple sources allows the system to pre-identify potential detection end issues before they affect the overall assessment of CDN node performance, thereby improving both productivity and measurement precision.
Data Source
AI summary
A content delivery network processing method includes: obtaining response data from at least one CDN node in response to a network request from a detection end, the response data including information indicating that the network request is successful or failed; calculating the quantity of detection successes and the quantity of detection failures of the detection end corresponding to the at least one CDN node in accordance with the response data; calculating a detection failure rate of the detection end in accordance with the quantity of detection successes and the quantity of detection failures; and comparing at least one of the detection failure rate or the quantity of detection failures with a target threshold, and determining whether the detection end is faulty in accordance with a comparison result.