Network Latency Detection via Data Center Timestamp Analysis
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Solution Overview
Problem
Current systems for detecting latency in communication networks are costly, complex, and limited in identifying and predicting network issues until they occur, making them ineffective for real-time monitoring and adaptive route selection.
Innovation Solution
A method and system that monitor connectivity by processing queries from subscriber devices, determining processing and total times to assess latency, and selecting alternate routes when latency exceeds a threshold, while categorizing subscribers by geographic location or data centers to identify latency sources and adaptively reroute messages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If agents are installed at both endpoints of data transmission for latency detection, then latency can be detected, but cost and complexity increase
Solution Approach 1:
The patent extracts the latency detection function from the subscriber endpoint and relocates it entirely to the data center. The data center determines total time by comparing the timestamp of received queries with the timestamp of received acknowledgments, eliminating the need for agents at subscriber endpoints while maintaining detection capability.
Solution Approach 2:
The data center performs self-service latency detection by utilizing existing query and acknowledgment messages in the message delivery process. No separate detection mechanism or additional agents are required - the system uses its own operational traffic to measure latency, reducing complexity while maintaining measurement precision.
2Measurement precision
If agents are installed at both endpoints for latency detection, then latency issues can be identified, but the system is limited to identifying issues after they appear and cannot predict them
Solution Approach 1:
The data center continuously monitors total time for message delivery across multiple subscribers and maintains historical latency data. This continuous preliminary monitoring enables the system to detect latency trends and select alternate routes before critical failures occur, rather than waiting for issues to manifest.
Solution Approach 2:
The system implements feedback by continuously measuring total time for each message delivery and using this information to dynamically select alternate data centers or routes. The feedback loop enables proactive route selection based on real-time latency conditions, allowing the system to adapt and prevent performance degradation.
3Measurement precision
If the system continuously monitors latency for multiple subscribers, then network performance can be assessed, but processing time and computational resources increase
Solution Approach 1:
The data center performs multiple functions using the same query and acknowledgment messages: message delivery, acknowledgment transmission, and latency measurement. By making the measurement process universal and integrating it into existing operational traffic, the system avoids the need for separate detection mechanisms that would consume additional processing resources.
Solution Approach 2:
The latency measurement process is self-service, utilizing the existing message flow between data center and subscribers. The data center determines total time by comparing timestamps from its own operational messages, eliminating the need for separate detection infrastructure and reducing overall processing resource requirements while maintaining continuous monitoring capability.
Data Source
AI summary
A method of detecting latency in message delivery in a communication network is disclosed. One or more data centers receives messages that are to be published via the communication network. The system receives queries from subscribers, processes the queries and transmits messages to the subscribers in response to the queries. The subscribers acknowledge the messages. The system uses its own processing time and the acknowledgement time to assess latency in message delivery. The system also may use the information to determine which data center or geographic location is experiencing relatively higher latency than other data centers or locations.