Online Service Performance Monitoring Through Cross-Service Analysis
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Solution Overview
Problem
Existing methods for monitoring and optimizing the performance of online services on centralized hosting platforms are resource-intensive and inefficient, particularly in identifying the causes of performance changes resulting from service modifications, such as plugin installations or theme changes, due to the complexity and cost of continuous monitoring and rollback processes.
Innovation Solution
A method that leverages cross-service data to identify similar online services and compares performance impacts of modification events, flagging unanticipated changes and recommending or automatically executing corrective actions, using historical data and similarity criteria to determine the cause of performance deviations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous monitoring and rollback processes are implemented to identify causes of performance changes, then performance optimization reliability is improved, but resource consumption and system complexity increase
Solution Approach 1:
The system performs preliminary actions by proactively identifying similar services and pre-analyzing their modification histories before performance issues occur. When a service modification is detected, the system immediately queries pre-identified similar services for comparable modification events, enabling rapid root cause analysis without extensive continuous monitoring or rollback processes.
Solution Approach 2:
The patent introduces similar services as intermediaries to transfer diagnostic information. Instead of directly monitoring and analyzing every performance change in isolation, the system uses similar services as mediators to provide comparative data, enabling indirect inference of performance change causes through cross-service pattern matching.
2Measurement precision
If cross-service data comparison is implemented to identify performance change causes, then diagnostic accuracy is improved, but data processing time increases
Solution Approach 1:
The system applies local quality by focusing data collection and comparison efforts only on specific, relevant attributes of similar services. Instead of comprehensively analyzing all service data, the system selectively queries modification histories, performance metrics, and configuration details that are locally relevant to the detected performance change, reducing unnecessary data processing while maintaining diagnostic accuracy.
Solution Approach 2:
The patent implements partial action by performing selective comparisons with similar services rather than exhaustive analysis of all available data. The system identifies and compares only the necessary modification events and performance parameters that are directly relevant to the detected issue, achieving sufficient diagnostic accuracy without the time cost of complete data processing.
3Productivity
If similar services are identified and compared to detect performance issues, then performance optimization efficiency is improved, but computational resources are consumed
Solution Approach 1:
The system performs preliminary identification of similar services based on service types, configurations, and operational characteristics before performance issues occur. This pre-segmentation enables rapid querying of relevant comparison data when performance changes are detected, avoiding the need to compute similarity metrics across all services during incident response and reducing computational resource consumption.
Data Source
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AI summary
A computer-implemented method is disclosed. The method includes: obtaining, by a server, a value for at least one performance metric associated with a first online service; detecting a service modification event for the first online service; identifying, by the server, a second online service; obtaining historical service modification events data for the second online service; comparing changes in the value for the at least one performance metric resulting from the service modification event for the first online service to changes in performance metric values resulting from historical service modification events for the second online service; determining that the service modification event is associated with an unanticipated performance change for the first online service based on the comparing; and providing an indication identifying the service modification event as being associated with the unanticipated performance change for the first online service.