A Novel Real-Time Data Stream Transfer System in Edge Computing of Smart Logistics

Smart logistics systems generate massive amounts of data, such as images and videos, requiring real-time processing in edge clusters. However, the edge cluster systems face performance bottlenecks in reception and forwarding high-concurrency data streams from numerous smart terminals, resulting in degraded processing efficiency. To address this issue, a novel high-performance data stream model called CBPS-DPDK is proposed. CBPS-DPDK integrates the DPDK framework from Intel corporations with a content-based publish/subscribe model enhanced by semantic filtering. This model adopts a three-tier optimization architecture. First, the user-space data plane is restructured using DPDK to avoid kernel context switch overhead via zero-copy and polling. Second, semantic enhancement is introduced into the publish/subscribe model to reduce the coupling between data producers and consumers through subscription matching and priority queuing. Finally, a hierarchical load balancing strategy ensures reliable data transmission under high concurrency. Experimental results show that CBPS-DPDK significantly outperforms two baselines—OSKT (kernel-based data forwarding) and DPDK-only (DPDK). Relative to the OSKT baseline, DPDK-only achieves improvements of 37.5% in latency, 11.1% in throughput, and 9.1% in VMAF; CBPS-DPDK further increases these to 51.8%, 18.3%, and 11.2%, respectively. In addition, compared with the traditional publish–subscribe system NATS, CBPS-DPDK maintains lower delay, higher throughput, and more balanced CPU and memory utilization under saturated workloads, demonstrating its effectiveness for real-time, high-concurrency edge scenarios.