Multi-factor Routing Optimization via Intermediary Service
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Solution Overview
Problem
Current network communication systems do not efficiently optimize routing to minimize costs while maintaining user experience, as they often rely on traditional methods that do not account for varying network conditions and costs across different geographic regions and carriers.
Innovation Solution
Implement a multi-factor optimized routing system that uses performance monitoring and cost databases to dynamically select the best route and server for network communications, considering factors like cost, load balancing, responsiveness, and geo-political issues, and updates routing tables based on real-time data to ensure efficient and cost-effective communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional routing methods are used that rely on shortest physical distance, then user experience is maintained with simple routing logic, but network costs increase due to lack of cost optimization
Solution Approach 1:
The patent introduces a routing optimization service as an intermediary component that sits between the DNS server and the traditional routing system. This service receives routing requests, queries multiple data sources (cost databases, performance monitoring systems, geo-political databases), and returns optimized routing decisions. By making the system modular and introducing this intermediary layer, the patent achieves cost optimization without requiring complete redesign of the existing routing infrastructure, thus managing complexity while improving network cost efficiency.
Solution Approach 2:
The patent implements feedback mechanisms by continuously monitoring network performance metrics (latency, packet loss, throughput) and using this information to dynamically adjust routing decisions. The performance monitoring system provides real-time feedback about the state of network paths, which is fed back into the routing optimization service to make informed decisions about which routes to prefer, thereby optimizing network costs while maintaining performance.
2Loss of energy
If routing decisions are made based on multiple factors (cost, performance, geo-political), then network cost optimization improves, but routing determination complexity increases
Solution Approach 1:
The patent segments the routing determination process into distinct functional modules: a cost database module that evaluates financial aspects, a performance monitoring module that assesses network conditions, a geo-political database module that checks regulatory compliance, and a decision-making module that synthesizes all inputs. By dividing the complex multi-factor evaluation into separate, specialized components, each module can focus on its specific aspect without being overwhelmed by the overall complexity, making the system more manageable and maintainable.
Solution Approach 2:
The routing optimization service acts as an intermediary that coordinates between multiple data sources and the routing system. It standardizes the interfaces to various databases and monitoring systems, normalizing their different data formats and evaluation criteria into a unified decision framework. This intermediary layer shields the complexity of integrating multiple factors from the core routing logic, making the system easier to implement and maintain.
3Productivity
If dynamic routing updates are performed based on real-time data, then network performance optimization improves, but system operational complexity increases
Solution Approach 1:
The patent implements periodic routing updates by having the routing optimization service check for changes in network conditions, cost data, and performance metrics at scheduled intervals rather than continuously. This periodic action allows the system to maintain optimized routing decisions while avoiding the operational overhead of constant real-time updates. The service can be configured to update routing tables at appropriate frequencies based on the volatility of network conditions and the requirements of the network operations team.
Solution Approach 2:
The routing optimization service is designed to automatically monitor network conditions, query databases, evaluate routing options, and update routing tables without requiring manual intervention. This self-service capability reduces operational complexity by eliminating the need for network administrators to manually manage dynamic routing updates. The system autonomously adapts to changing network conditions while maintaining performance optimization, thereby improving network efficiency without proportionally increasing operational complexity.
Data Source
AI summary
A multi-factor optimized routing can select a computing device, from among multiple computing devices associated with a domain, to receive communications and thereby optimize one or more factors associated with such communications. Such multi-factor optimized routing can likewise specify a particular route, such as through the specification of one or more sub-networks. A performance monitoring system, comprising a performance monitoring framework that can support service-specific performance monitors can collect performance data to be used in the selection of a multi-factor optimized route. Additional data can be collected from other sources and the multi-factor optimized routing can be provided to a name resolution and routing system to ultimately route communications in an optimized manner.