Edge Analytics Collectors for CDN Content Control
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Solution Overview
Problem
Content providers in content delivery networks lack control over content distribution and access to internal analytics, leading to inefficiencies in content delivery and user experience due to separate entities managing content and network infrastructure.
Innovation Solution
Implementing a scalable content and network analytics collection system within the content delivery network, utilizing edge servers to collect and aggregate data, applying collection rules, and processing analytics in parallel to provide insights on content and network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If content providers use external content delivery networks for distribution, then content reach and delivery speed are improved, but control over distribution and access to analytics are lost
Solution Approach 1:
The patent introduces an intermediary analytics collection system that sits between the content delivery network infrastructure and content providers. This intermediary layer (comprising collectors, aggregators, and analyzers deployed within the CDN) enables content providers to retrieve analytics data without directly controlling the distribution infrastructure, thus maintaining delivery speed while restoring analytical control.
Solution Approach 2:
The analytics collection system is segmented into multiple independent components: collectors at edge servers, aggregators at regional hubs, and analyzers at central locations. This segmentation allows the system to operate independently from the content delivery infrastructure, enabling content providers to access analytics without interfering with or requiring control over the distribution mechanism.
2Loss of information
If centralized analytics collection is implemented, then comprehensive data access is improved, but processing time and system complexity increase
Solution Approach 1:
The analytics collection architecture is divided into hierarchical segments: edge-level collectors gather data locally, regional aggregators consolidate data from multiple collectors, and central analyzers process aggregated data. This segmentation enables parallel processing across multiple nodes, significantly reducing total processing time compared to a single centralized collection point.
Solution Approach 2:
Analytics data is collected and pre-processed at edge servers and regional hubs before being sent to central analyzers. Collection rules are applied locally to filter and aggregate data in advance, so that when data reaches the central analytics system, it is already organized and ready for analysis, reducing processing time and computational overhead.
3Measurement precision
If fine-grained analytics collection is implemented at edge servers, then measurement precision is improved, but device complexity and data volume increase
Solution Approach 1:
The analytics collection function is segmented from the content delivery function at edge servers. Dedicated collector components handle analytics data collection independently from content delivery processes, minimizing the impact on edge server complexity while enabling fine-grained measurement of content access patterns, user behavior, and performance metrics.
Solution Approach 2:
Collection rules are configured to gather only the specific analytics data needed for particular analysis objectives, rather than collecting all possible data. This selective collection approach maintains measurement precision for relevant metrics while limiting the volume of data that increases system complexity and processing requirements.
Data Source
AI summary
Example embodiments herein include a system having one or more edge servers disposed in an edge site of a content delivery network (CDN). The system can include a collector for collecting analytics associated with requests for content in the CDN. One or more additional collectors can be instantiated in the system, for example, in response to an increase in recordable events detected in the CDN. The system can include an aggregator for aggregating the collected analytics with analytics collected from other edge stages of the CDN. The system can also include a data store that stores the aggregated analytics according to a configurable data model.