The rapid evolution of next-generation networks demands high performance, scalability, and security. However, conventional CPU-based systems face fundamental bottlenecks in terms of throughput, latency, and energy efficiency, which limit their ability to support 5G networking, HPC, and cybersecurity workloads. This thesis aims to evaluate the role of programmable Data Processing Units (DPUs) as a unifying hardware platform for accelerating workloads across 5G data networks, high-performance computing (HPC), and network security. The research investigates three complementary directions. For 5G, user-plane functions such as GTP encapsulation, QoS enforcement, and DDoS mitigation are offloaded to NVIDIA BlueField DPUs using the DOCA framework. For HPC, a comparative study of BlueField-2 and BlueField-3 is conducted using RDMA micro-benchmarks to assess bandwidth, latency, and message-rate performance. For security, the thesis introduces IDS-NGNN, a nested graph neural network for intrusion detection, and proposes PUF-based authentication schemes for autonomous swarm systems. The findings show that DPUs can sustain line-rate packet processing up to 400 Gbps in 5G scenarios while reducing latency and improving scalability compared to software-only UPFs. In HPC contexts, comparative benchmarking highlights clear trade-offs between BlueField-2 and BlueField-3 in terms of throughput and latency, underscoring the suitability of DPUs as data movement accelerators. In security, the combination of IDS-NGNN and PUF-based mechanisms demonstrates that AI and hardware primitives can provide robust, low-latency protection against evolving cyber threats. This thesis concludes that DPUs are a cornerstone technology for the 6G era and beyond, enabling infrastructures that are not only faster and more efficient but also inherently more secure and resilient. By bridging advances in 5G networking, HPC, and cybersecurity, the work establishes DPUs as critical enablers of future high-performance, secure digital ecosystems.

Data Processing Units for Next-Generation Networks: Evaluation across User Plane Function, High-Performance Computing, and Cybersecurity Workload

ABU BAKAR, RANA
2026

Abstract

The rapid evolution of next-generation networks demands high performance, scalability, and security. However, conventional CPU-based systems face fundamental bottlenecks in terms of throughput, latency, and energy efficiency, which limit their ability to support 5G networking, HPC, and cybersecurity workloads. This thesis aims to evaluate the role of programmable Data Processing Units (DPUs) as a unifying hardware platform for accelerating workloads across 5G data networks, high-performance computing (HPC), and network security. The research investigates three complementary directions. For 5G, user-plane functions such as GTP encapsulation, QoS enforcement, and DDoS mitigation are offloaded to NVIDIA BlueField DPUs using the DOCA framework. For HPC, a comparative study of BlueField-2 and BlueField-3 is conducted using RDMA micro-benchmarks to assess bandwidth, latency, and message-rate performance. For security, the thesis introduces IDS-NGNN, a nested graph neural network for intrusion detection, and proposes PUF-based authentication schemes for autonomous swarm systems. The findings show that DPUs can sustain line-rate packet processing up to 400 Gbps in 5G scenarios while reducing latency and improving scalability compared to software-only UPFs. In HPC contexts, comparative benchmarking highlights clear trade-offs between BlueField-2 and BlueField-3 in terms of throughput and latency, underscoring the suitability of DPUs as data movement accelerators. In security, the combination of IDS-NGNN and PUF-based mechanisms demonstrates that AI and hardware primitives can provide robust, low-latency protection against evolving cyber threats. This thesis concludes that DPUs are a cornerstone technology for the 6G era and beyond, enabling infrastructures that are not only faster and more efficient but also inherently more secure and resilient. By bridging advances in 5G networking, HPC, and cybersecurity, the work establishes DPUs as critical enablers of future high-performance, secure digital ecosystems.
2-feb-2026
Italiano
SmartNIC
DPU
Hardware
Offload
Machine Learning
AI
Wirespeed AI
Networks
GNN
DPI
DOCA Flow
Flow Tracking
DDoS Detection
CASTOLDI, PIERO
CUGINI, FILIPPO
DOMENICO SIRACUSA
GREGORIO PROCISSI
KYRIAKOS VLACHOS
SAMBO, NICOLA
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14242/357851
Il codice NBN di questa tesi è URN:NBN:IT:SSSUP-357851