Asynchronous Tag Latency Monitoring for Web Page Performance
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Solution Overview
Problem
Existing web page loading technologies struggle to effectively manage third-party tags, leading to slow page loads or failures due to unresponsive third-party services, as they lack proactive monitoring and adaptive suspension mechanisms to handle latency issues.
Innovation Solution
Implementing a system that employs multiple tag states (NORMAL, FLAGGED, SUSPENSION_A, SUSPENSION_B) and uses asynchronous monitoring to proactively manage tag latency, changing states based on latency thresholds to prevent premature blocking or enabling of tags, ensuring optimal web page loading performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If third-party services are loaded into web pages, then functionality and content are enhanced, but page load time increases and reliability decreases when services are slow or unresponsive
Solution Approach 1:
The system performs preliminary actions by proactively monitoring tag latency before tags cause page loading failures. The asynchronous monitoring process continuously checks tag performance metrics and predicts potential failures, allowing the system to take preventive measures by suspending tags before they actually impact page loading, thus maintaining reliability while preserving functionality.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring tag latency metrics and using this information to dynamically adjust tag deployment decisions. The monitoring system provides real-time feedback on tag performance, and this feedback loop enables the system to adaptively suspend or resume tags based on their actual performance, resolving the contradiction between maintaining functionality and ensuring reliability.
2Adaptability or versatility
If third-party services are loaded into web pages, then functionality is enhanced, but page load speed decreases
Solution Approach 1:
The system performs preliminary monitoring and assessment of tag performance characteristics before tags are deployed. By establishing baseline latency metrics and performance thresholds in advance, the system can quickly determine whether to suspend or allow tag loading, thereby optimizing page load speed without sacrificing necessary functionality.
Solution Approach 2:
The system applies partial action by selectively suspending only those tags that exhibit poor performance characteristics while allowing well-performing tags to continue loading. This partial suspension approach maintains the necessary functionality provided by good tags while eliminating the speed penalty caused by problematic tags, thus resolving the contradiction between functionality and load speed.
3Reliability
If tag monitoring and suspension mechanisms are implemented, then page loading reliability is improved, but system complexity increases
Solution Approach 1:
The system implements self-service by enabling tags to effectively monitor and evaluate their own performance characteristics. The asynchronous monitoring process allows tags to self-assess their latency metrics and automatically determine when suspension is necessary, reducing the need for complex external management systems while maintaining high reliability.
Solution Approach 2:
The system manages complexity through parameter changes by focusing monitoring and control efforts on key performance parameters such as tag latency and load time thresholds. By establishing clear parameter-based decision rules for tag suspension, the system achieves reliable tag management without requiring overly complex control mechanisms, thus resolving the contradiction between reliability and system complexity.
4Reliability
If proactive tag latency monitoring is implemented, then premature tag suspension is prevented, but measurement and detection difficulty increases
Solution Approach 1:
The monitoring system implements self-service by having tags independently report their own latency metrics and performance data. This self-reporting mechanism simplifies the measurement process compared to external probing methods, as tags provide accurate first-hand information about their loading performance, thereby maintaining high suspension accuracy while reducing measurement complexity.
Solution Approach 2:
The system uses feedback from actual tag loading operations to continuously refine latency measurements and performance thresholds. By collecting real-world performance data from deployed tags and using this feedback to adjust monitoring parameters, the system achieves accurate tag suspension decisions without requiring overly complex measurement methodologies, thus resolving the contradiction between suspension accuracy and measurement difficulty.
Data Source
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AI summary
Embodiments are directed towards employing a plurality of tag states to control tag suspension based on an asynchronous process that proactively monitors tag performance, response times, and latency. Tags may be in one of multiple states. Tags in a NORMAL state or a FLAGGED state may be enabled for deployment, and tags in a SUSPENSION A state or a SUSPENSION B state may be blocked from deployment. A tag's state may change based on monitoring the tag's latency to determine if latent events occur. Tag latency may be asynchronously monitored independent of web page requests. If latent events occur, then the tag's state may change from NORMAL to FLAGGED, from FLAGGED to SUSPENSION A, or from SUSPENSION B to SUSPENSION_A. If non-latent events occur, then the tag's state may change from SUSPENSION B to FLAGGED or from FLAGGED to NORMAL, while a tag's state may change from SUSPENSION_A to SUSPENSION_B after a predetermined time period.