Monitor Agent for Content Delivery Defect Detection
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Solution Overview
Problem
Current content delivery networks lack effective methods to identify and evaluate defective segments of content data, which affects the efficiency of content delivery to users.
Innovation Solution
A system comprising a monitor domain that generates and deploys a monitor agent to simulate user requests, extracts content data from the content delivery domain, generates delivery data, and uses parameter processors to identify defective segments based on performance data, allowing for the evaluation of content delivery efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If content delivery networks distribute content data over the network without monitoring, then content delivery speed is maintained, but defective segments cannot be identified affecting delivery quality
Solution Approach 1:
The patent introduces monitor agents as intermediary components deployed within the content delivery network infrastructure. These agents act as mediators between the content delivery system and the monitoring system, extracting delivery data without disrupting normal content flow. The agents collect performance metrics and defect information while allowing content delivery to proceed uninterrupted, thus improving reliability without proportionally increasing system complexity.
Solution Approach 2:
The monitoring system creates copies of delivery data and performance metrics rather than directly interfering with the original content delivery process. Monitor agents extract and copy delivery information to analyze defective segments, allowing the system to identify quality issues without altering the primary content delivery pathway. This copying approach enables reliable defect detection while maintaining operational simplicity.
2Measurement precision
If monitor agents are deployed to extract and analyze delivery data, then defective segments can be identified, but system complexity and resource usage increase
Solution Approach 1:
The monitor agents implement partial action by selectively monitoring only critical delivery parameters and segments that are likely to contain defects. Rather than analyzing every byte of delivered content, the system focuses computational resources on identifying potential defective segments based on performance data patterns. This approach achieves high measurement precision for defect detection while avoiding the excessive computational resource consumption that would result from comprehensive analysis of all delivered content.
3Measurement precision
If comprehensive delivery data is collected and analyzed, then accurate identification of defective segments is achieved, but processing time and complexity increase
Solution Approach 1:
The system performs preliminary actions by having monitor agents continuously collect and pre-process delivery data in the background during normal content delivery operations. Performance metrics and delivery information are gathered and organized before formal defect analysis is initiated. This preliminary data collection and preparation enables rapid and accurate defect identification when needed, without requiring time-consuming data gathering during the actual defect detection process.
Data Source
AI summary
Technologies are generally described for systems and methods to identify defective segments of content data. The methods may comprise generating a monitor agent effective to generate a request for the content data. The methods may comprise deploying the monitor agent to an address. The monitor agent may be effective to extract the content data and to generate delivery data associated with the content data. The methods may comprise distributing the delivery data to a parameter processor. The methods may comprise generating parameter data. The parameter data may be effective to indicate a parameter level associated with a parameter of the content data. The methods may comprise generating performance data based on the parameter data. The performance data may be effective to indicate a number of defects among the content data. The methods may comprise identifying a number of defective segments among the content data based on the performance data.