Scalable Wireless Data Plane Architecture for Dynamic Traffic Scaling
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Solution Overview
Problem
Existing 5G data plane architectures are inefficient in scaling to meet dynamic processing needs for both high throughput and low latency data transfers, leading to suboptimal resource usage and increased power consumption.
Innovation Solution
The implementation of a dynamically scalable data plane architecture that includes multiple self-contained downlink and uplink clusters, each with a full data stack, which can be activated or deactivated based on CPU load, throughput, and latency thresholds, allowing for adaptive scaling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed data plane architecture is used, then device complexity is reduced, but adaptability to dynamic traffic conditions deteriorates
Solution Approach 1:
The data plane architecture is segmented into multiple independent processing clusters, each capable of handling specific data layers. These clusters can be individually activated or deactivated based on traffic conditions, enabling adaptive scaling without requiring a complete architectural redesign. The segmentation allows the system to adjust processing capacity dynamically while maintaining manageable complexity through modular design.
Solution Approach 2:
The architecture implements dynamic cluster activation and deactivation mechanisms that respond to real-time traffic conditions. Controllers monitor throughput and latency metrics, then dynamically adjust the number of active processing clusters. This dynamic behavior enables the system to adapt its capacity to match actual demand, resolving the contradiction between fixed architecture and adaptive performance.
2Productivity
If more processing clusters are activated, then throughput increases, but power consumption increases
Solution Approach 1:
Instead of maintaining all processing clusters active at all times, the system activates only the necessary number of clusters based on current traffic demands. When throughput requirements are low, fewer clusters are activated, reducing power consumption. When high throughput is needed, additional clusters are activated. This partial action approach ensures that processing resources are used efficiently without unnecessary energy waste.
Solution Approach 2:
The system implements feedback mechanisms where controllers continuously monitor throughput performance and adjust cluster activation states accordingly. When throughput falls below required thresholds, additional clusters are activated. When throughput exceeds requirements, clusters are deactivated to save power. This closed-loop feedback control optimizes the balance between productivity and energy consumption in real-time.
3Adaptability or versatility
If cluster scaling is implemented, then adaptability improves, but control complexity increases
Solution Approach 1:
The controllers are designed with multi-functionality, handling both monitoring and cluster management tasks. The same control infrastructure that monitors traffic conditions also executes the scaling decisions, eliminating the need for separate control systems. This universal approach to control reduces overall system complexity while maintaining sophisticated scaling capabilities.
Solution Approach 2:
The system implements self-service mechanisms where controllers automatically adjust cluster activation based on pre-defined performance thresholds and traffic patterns. The scaling decisions are made autonomously by the control system without requiring external intervention or complex manual configuration. This self-service approach simplifies control complexity by enabling automatic adaptation to changing conditions.
Data Source
AI summary
Embodiments of apparatus and method for data plane management are disclosed. In one example, an apparatus for communication both uplink and downlink can include a plurality of downlink clusters, each downlink cluster including a downlink cluster processor configured to process three or more downlink data layers. The apparatus can also include a plurality of uplink clusters, each uplink cluster including an uplink cluster processor configured to process three or more uplink data layers. The apparatus can further include a controller configured to scale the plurality of downlink clusters and configured to scale the plurality of uplink clusters. Scaling the plurality of downlink clusters and the plurality of uplink clusters can include activating or deactivating one or more clusters of the plurality of downlink clusters, the plurality of uplink clusters, or both the plurality of downlink clusters and the plurality of uplink clusters.