Time-Based Traffic Engineering for Seasonal Network Flows
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Solution Overview
Problem
Deterministic networks face challenges in managing seasonal traffic flows, particularly in ensuring service level agreements (SLAs) are met due to varying latency and jitter, especially in low power and lossy networks like IoT, where traditional methods struggle to adapt to seasonal changes.
Innovation Solution
A device in the network identifies seasonal traffic flows using machine learning techniques and determines if the SLA is met; if not, it provisions a time-based traffic engineered path, either by computing a new 6TiSCH track or using source routing to ensure compliance with SLA requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional networking methods are used in low power and lossy networks, then device complexity and energy consumption are reduced, but service level agreement compliance deteriorates due to uncontrolled latency and jitter
Solution Approach 1:
The system performs preliminary identification of seasonal traffic flows using machine learning techniques and proactively provisions time-based traffic engineered paths before SLA violations occur. This advance preparation allows the network to preemptively adjust routing and resource allocation for identified seasonal patterns, ensuring SLA compliance without reactive complexity.
Solution Approach 2:
The system dynamically provisions time-based paths that adapt to seasonal traffic patterns while maintaining deterministic networking requirements. The traffic engineered paths are flexible in their temporal allocation but rigid in meeting SLA constraints, allowing the network to optimize for seasonal flows without permanently increasing device complexity.
2Manufacturing precision
If time-based traffic engineered paths are provisioned for seasonal flows, then deterministic packet delivery is improved, but network device complexity increases
Solution Approach 1:
The system employs machine learning techniques that enable the network to automatically identify seasonal traffic flows and self-provision appropriate time-based paths without extensive manual configuration. This self-service capability reduces the operational complexity of implementing deterministic networking while maintaining precise packet delivery for seasonal flows.
3Measurement precision
If machine learning techniques are used to identify seasonal traffic flows, then traffic flow identification accuracy is improved, but processing requirements and device complexity increase
Solution Approach 1:
The system applies machine learning techniques selectively to identify seasonal traffic flows rather than processing all network traffic uniformly. By focusing computational resources only on detecting seasonal patterns rather than analyzing every packet, the system achieves high identification accuracy while minimizing energy consumption and processing overhead in resource-constrained IoT environments.
Data Source
AI summary
In one embodiment, a device in a network receives information regarding one or more traffic flows in the network. The device identifies a particular one of the one or more traffic flows as a seasonal traffic flow based on the information regarding the one or more traffic flows. The device determines whether a service level agreement associated with the seasonal traffic flow is met. The device causes a time-based path for the seasonal traffic flow to be provisioned, in response to a determination that the service level agreement associated with the seasonal traffic flow is not met.