Diagnosis Point Tracker for Network Performance Anomaly Detection
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Solution Overview
Problem
Modern cellular networks are volatile and diverse, making it challenging to predict and manage network performance due to variable latency, jitter, and throughput, which affects data delivery and user experience, as existing techniques like compression and caching are inadequate in addressing these issues.
Innovation Solution
A data-driven approach is implemented using a diagnosis point tracker that measures and analyzes data attribute values across various network transactions, generating insights into network performance anomalies and recommending strategies to optimize data delivery based on device and network conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If compression or right-sizing content techniques are used, then data size is reduced, but network volatility and diversity issues remain unresolved
Solution Approach 1:
The patent introduces an intermediary system that sits between the content source and the network, actively monitoring network conditions and dynamically adjusting content delivery parameters. This intermediary analyzes real-time network metrics (latency, jitter, packet loss) and adapts the content delivery strategy accordingly, resolving the contradiction by mediating between fixed content and volatile network conditions.
Solution Approach 2:
The system transitions from static content delivery to dynamic adaptation by continuously monitoring network conditions and adjusting delivery parameters in real-time. The patent implements dynamic content segmentation, adaptive bitrate streaming, and real-time rebuffering based on observed network volatility, making the delivery system responsive to changing network conditions rather than relying on fixed compression ratios.
2Measurement precision
If multiple metrics are monitored across the network, then diagnosis capability is improved, but data complexity and analysis difficulty increase
Solution Approach 1:
The patent segments the network monitoring system into distinct functional components: data collection agents at network points, a central aggregation server, and analysis modules. Each segment handles specific metrics (latency, jitter, throughput, packet loss) independently, then combines results. This segmentation reduces analysis complexity by localizing processing and enabling modular analysis of individual metric types rather than analyzing all metrics simultaneously.
Solution Approach 2:
The patent introduces an intermediary analysis layer that sits between raw metric collection and final diagnosis. This intermediary normalizes diverse metrics from multiple sources, correlates them temporally and spatially, and presents synthesized insights to users. The intermediary translates complex multi-dimensional data into actionable diagnostic information, reducing the perceived complexity for end users while maintaining high measurement precision.
3Reliability
If network performance is monitored at multiple points, then diagnostic coverage is improved, but system complexity increases
Solution Approach 1:
The patent implements a nested monitoring architecture where lightweight agent components are embedded at multiple network points (routers, switches, servers), each collecting local metrics. These agents nest within a larger monitoring system that aggregates data from multiple nested levels. The nested structure enables comprehensive diagnostic coverage across the entire network while keeping individual monitoring points simple and manageable.
Solution Approach 2:
The patent creates a universal monitoring framework where a single standardized agent design can be deployed across diverse network equipment types. The universal agent collects multiple metric types (latency, throughput, error rates) using the same interface and protocol, enabling multi-point monitoring without proportionally increasing system complexity. The standardized universal approach allows the same monitoring infrastructure to serve multiple diagnostic purposes.
Data Source
AI summary
A data-driven approach to network performance diagnosis and root-cause analysis is presented. By collecting and aggregating data attribute values across multiple components of a content delivery system and comparing against baselines for points of inspection, network performance diagnosis and root-cause analysis may be prioritized based on impact on content delivery. Recommended courses of action may be determined and provided based on the tracked network performance analysis at diagnosis points.