Dynamic Low Latency Routing for Network Service Flows
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Solution Overview
Problem
Current network infrastructure struggles to identify and prioritize low latency traffic effectively for critical applications, leading to potential delays in high-priority situations without requiring extensive costs or personnel resources.
Innovation Solution
A network method involving a low latency controller and optimizing agent that identifies and routes low latency traffic by modifying existing service flows or creating new ones based on prioritization notifications from client devices, ensuring optimal service flow for applications like IoT devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing service flows are used for all traffic, then network infrastructure complexity is reduced, but low latency traffic cannot be prioritized effectively
Solution Approach 1:
The patent implements dynamic service flow selection where the network device adapts between using existing service flows and creating new optimized service flows based on real-time traffic characteristics. The optimizing agent continuously monitors traffic patterns and dynamically determines whether to route low latency traffic through existing flows or establish new dedicated flows, making the system flexible rather than static.
Solution Approach 2:
The system changes service flow parameters dynamically by modifying existing service flows or creating new ones with specific low latency parameters when needed. The optimizing agent adjusts service flow characteristics such as priority levels, bandwidth allocation, and routing paths based on traffic requirements, transforming the network from a fixed configuration to an adaptive parameter-based system.
2Reliability
If new optimized service flows are created for low latency traffic, then service quality is improved, but network device complexity and resource allocation increase
Solution Approach 1:
The optimizing agent implements self-service by automatically monitoring traffic patterns, identifying low latency requirements, and creating or modifying service flows without manual intervention. The system autonomously manages the entire process from detection to service flow establishment, reducing the need for complex manual configuration and ongoing management overhead.
Solution Approach 2:
The system incorporates feedback mechanisms where the optimizing agent continuously monitors traffic performance and service flow effectiveness. Based on this feedback, the agent dynamically adjusts service flow configurations, creates new flows when existing ones are insufficient, and optimizes resource allocation, forming a closed-loop control system that adapts to changing network conditions.
3Reliability
If manual configuration of service flows is used, then service quality can be optimized, but personnel resources and operational costs increase
Solution Approach 1:
The optimizing agent enables the network system to serve itself by automatically detecting low latency traffic patterns, analyzing service flow performance, and creating or modifying service flows without human intervention. This automation eliminates the need for manual configuration and reduces operational complexity while maintaining optimized service quality.
Solution Approach 2:
The patent replaces manual mechanical configuration processes with automated electronic monitoring and control mechanisms. The optimizing agent uses software-based traffic analysis and automated service flow management to substitute human operators, reducing personnel requirements and operational costs while improving response time and consistency.
Data Source
AI summary
An optimizing agent of a network device that does not support low latency DOCSIS can identify traffic or packets associated with a client resource for an optimization service flow. For example, the optimizing agent can receive a priority notification associated with a client resource from a low latency controller that is indicative of a low latency requirement associated with the client resource. The optimizing agent identifies the traffic for the optimized service flow based on the priority notification. The identifying can require modifying one or more parameters of an existing service flow, creating a new service flow, or selecting an existing service flow with low latency. The identified traffic can be routed to the optimized service flow to achieve low latency or high QoS.