Network Brownout Detection via DNS Query Thresholds
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Solution Overview
Problem
Network failures, or 'brownouts,' occur due to various issues such as server failures, overloaded servers, fiber cuts, and misconfigured ISP policies, making it challenging to ensure reliable communication over the Internet, which is complex and distributed across multiple organizations.
Innovation Solution
A method for detecting network brownouts by comparing the number of client requests to an alternative network location against threshold values, including median values, exponentially weighted moving averages, and standard deviations, to identify potential failures and generate alarms, with the option to redirect users to alternative DNS servers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network monitoring and failure detection systems are implemented, then network reliability is improved, but device complexity increases
Solution Approach 1:
The patent introduces DNS servers as intermediary components that passively monitor network health by analyzing DNS query patterns. Instead of implementing complex active monitoring systems, the invention uses the existing DNS infrastructure to detect network brownouts by observing unusual patterns in DNS requests, thereby improving reliability without significantly increasing device complexity
Solution Approach 2:
The system implements feedback mechanisms where DNS servers continuously analyze query patterns and generate alerts when anomalies are detected. This feedback loop enables automatic detection and notification of network failures, allowing the system to maintain high reliability through continuous monitoring while keeping the implementation relatively simple by leveraging existing DNS protocols
2Measurement precision
If multiple threshold values and statistical methods are used for detection, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The detection system performs self-service by automatically calculating multiple statistical thresholds (median, mean, standard deviation) from the collected DNS query data without requiring external configuration or complex rule sets. The system adapts to normal traffic patterns and automatically identifies deviations, achieving high detection precision while maintaining relatively simple implementation through automated statistical analysis
3Loss of time
If real-time monitoring and alerting are implemented, then response time is improved, but use of energy increases
Solution Approach 1:
The system implements periodic monitoring by sampling DNS query patterns at regular intervals rather than continuously analyzing every single query in real-time. This periodic approach enables timely detection of network brownouts while significantly reducing energy consumption compared to continuous real-time analysis, achieving a balance between response time and energy efficiency
Data Source
AI summary
A number of requests for an alternative network location are identified, where the requests for the alternative network location are generated after failure of one or more previous requests to reach a first, previous network location as a result of a network brownout. It is determined if the number of requests for the alternative network location exceed a threshold value for a period of time, and if so, an alarm is generated.