Automated DNS Steering for Dynamic Network Latency Optimization
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Solution Overview
Problem
Modern heterogeneous networks face challenges due to their volatility and diversity, leading to inconsistent data delivery performance, as existing techniques like caching and compression are ineffective in addressing the dynamic and personalized nature of traffic in these environments.
Innovation Solution
The implementation of a cognitive analysis system that measures latency between autonomous systems and user devices, using a DNS controller to automate DNS steering by aggregating RTT values and configuring DNS to route traffic to the optimal data center based on expected latency, thereby adapting to the dynamic conditions of wireless networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If DNS steering is implemented to optimize data delivery, then data delivery speed is improved, but system complexity increases due to the need to measure latency, aggregate RTT values, and configure DNS routing dynamically
Solution Approach 1:
The system automatically measures latency, aggregates RTT values, and configures DNS routing without manual intervention. The DNS controller autonomously performs cognitive analysis of network performance data and makes routing decisions based on expected latency, eliminating the need for manual configuration and reducing operational complexity despite the advanced functionality.
Solution Approach 2:
The system performs preliminary latency measurements and RTT value aggregations before actual data delivery occurs. By pre-calculating expected latency for different data centers and autonomously configuring DNS routing in advance, the system optimizes data delivery paths before traffic flows, reducing real-time decision complexity and improving speed.
2Adaptability or versatility
If automated DNS steering is implemented to adapt to dynamic network conditions, then adaptability is improved, but measurement and detection difficulty increases due to the need to monitor latency, jitter, throughput, and losses in real-time
Solution Approach 1:
The system continuously measures network performance parameters (latency, jitter, throughput, losses) and uses this feedback to update latency expectations and optimize DNS routing decisions. The cognitive analysis system processes real-time network performance data and adjusts data center selections dynamically, improving adaptability while managing measurement complexity through automated feedback loops.
Solution Approach 2:
The system replaces complex manual network monitoring and analysis with automated cognitive analysis mechanisms. By using programmed algorithms to aggregate RTT values, calculate expected latency, and configure DNS routing automatically, the system reduces the operational difficulty of measuring and responding to dynamic network conditions while maintaining high adaptability.
Data Source
AI summary
Network performance data, such as routing trip time between autonomous systems and data centers, is gathered and aggregated to determine optimal mappings of autonomous systems and data centers. Autonomous system based DNS steering may be automated by repeating a life cycle of determining the optimal mappings. Data delivery strategies are applied to a portion of a network to deliver content using the optimal mappings.