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Subscriber-Aware Load Balancing Enhances Network Operations Efficiency

Source Decision Insights Coverage Published November 20, 2025 Companies 3
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3 companies named across 9 categories, one of 275 articles referencing Nvidia. Previous coverage: Nokia announces strategy shift towards AI, cloud infrastructure, data center networking, and 6G (Nov 2025).

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Subscriber-Aware Load Balancing (SALB) allows mobile operators to effectively align control- and user-plane traffic per subscriber. This capability enhances operational efficiency and resource utilization by providing real-time packet correlation, all while minimizing Central Processing Unit (CPU) overhead.

Overview of SALB

SALB features within the Aviz Service Node, enabling the correlation of control and user-plane packets for each subscriber. This design promotes optimized analytics and supports scalable performance without increasing CPU demands.

Technical Implementation of SALB

The implementation of SALB is facilitated through the NVIDIA BlueField-3 Data Processing Unit (DPU). This architecture processes control-plane packets on Arm cores while steering user-plane traffic via a P4 pipeline, achieving line-rate throughput.

Operational Mechanics:

  • Control-plane packets: Handled on the BlueField-3 DPU's Arm cores.
  • User-plane packets: Context is extracted, enabling effective flow management within the DPU's infrastructure.

This configuration allows mobile operators to maintain coherence between traffic types and optimize the use of monitoring tools.

Architectural Insights

The architecture for SALB involves configuring the BlueField-3 in DPU mode, ensuring that control packets are efficiently processed while user flows are managed through the P4 pipeline. This method enables proactive rule installation through the NVIDIA P4 Software Development Kit (SDK), supporting fast processing requirements.

Performance Advancements with DPU

Recent tests indicate that the BlueField-3 DPU can achieve 100 Gbps at line-rate using one Arm core in contrast to around 20 x86 CPU cores needed for similar throughput, demonstrating efficiency gains.

Validation Results

Feature / MetricBlueField-3 DPUx86 Baseline
HardwareBlueField-3 DPU (Arm + P4)Dual-Socket Xeon (2.3 GHz)
Network InterfaceDual-port 100 GbE (200 Gbps)Intel E810, 2×100 GbE
Traffic TypeGTP User and Control PlaneGTP User and Control Plane
Throughput100 Gbps (1 Arm Core + P4 Datapath)~20 x86 CPU cores

This delivery model confirms that one DPU port can manage substantial traffic, substituting for multiple x86 cores.

Cost and Power Efficiency

Utilizing the BlueField-3 DPU effectively reduces total ownership costs and decreases power consumption, providing core efficiency improvements. For instance, the DPU can replace approximately 20 CPU cores while saving considerable power.

Collaborative Validation by Aviz and NVIDIA

The joint efforts of Aviz and NVIDIA have validated the operational effectiveness of SALB on the BlueField-3 DPU, confirming its applicability for deep network observability with minimal overhead.

Feedback from the Industry

Chid Perumal, CTO of Aviz Networks, stated, “With Subscriber-Aware Load Balancing on BlueField-3, we correlate control- and user-plane flows per subscriber and steer them at line rate, allowing operators to maintain capacity and scalability without excessive CPU demands.”

Advantages for Mobile Operators

The implementation of SALB offers several benefits:

  • Coherent Analytics: Both traffic types for a subscriber are coherently managed to the same analytical tools.
  • Efficient Tool Performance: Probes retain full visibility without being overloaded.
  • Efficiency per Power: Improved throughput supports greater operational efficiency compared to traditional CPU configurations.

Final Remarks

In conclusion, SALB provides mobile operators with necessary mechanisms to manage control and user plane traffic for subscriber analysis effectively. The system ensures coherent load balancing while optimizing resource utilization at line rate. Consequently, operators achieve reduced overhead in computing while maintaining visibility within their networks.

Blog post, originally published by Aviz Team at Aviz Networks.

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