Flexible Network Modal Packet Processing Pipeline Construction Mechanism for Cloud-Network Convergence Environment

ZHU Jun; XU Qi; ZHANG Fujun; WANG Yongjie; ZOU Tao; LONG Keping

doi:10.11999/JEIT250806

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ZHU Jun, XU Qi, ZHANG Fujun, WANG Yongjie, ZOU Tao, LONG Keping. Flexible Network Modal Packet Processing Pipeline Construction Mechanism for Cloud-Network Convergence Environment[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250806

Citation:

ZHU Jun, XU Qi, ZHANG Fujun, WANG Yongjie, ZOU Tao, LONG Keping. Flexible Network Modal Packet Processing Pipeline Construction Mechanism for Cloud-Network Convergence Environment[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250806

Citation:

ZHU Jun, XU Qi, ZHANG Fujun, WANG Yongjie, ZOU Tao, LONG Keping. Flexible Network Modal Packet Processing Pipeline Construction Mechanism for Cloud-Network Convergence Environment[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250806

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Flexible Network Modal Packet Processing Pipeline Construction Mechanism for Cloud-Network Convergence Environment

doi: 10.11999/JEIT250806 cstr: 32379.14.JEIT250806

Research Center for High Efficiency Computing Facilities, Zhejiang Lab, Hangzhou 311121, China

Funds: The National Natural Science Foundation of China (U22A2005), Key R&D Program of Zhejiang (2024SSYS0001)

Received Date: 2025-08-27
Accepted Date: 2025-11-03
Rev Recd Date: 2025-11-03

Available Online: 2025-11-11

Abstract

Abstract

Objective With the deep integration of information network technologies and vertical application domains, the demand for cloud–network convergence infrastructure becomes increasingly significant, and the boundaries between cloud computing and network technologies are gradually fading. The advancement of cloud–network convergence technologies gives rise to diverse network service requirements, creating new challenges for the flexible processing of multimodal network packets. The device-level network modal packet processing flexible pipeline construction mechanism is essential for realizing an integrated environment that supports multiple network technologies. This mechanism establishes a flexible protocol packet processing pipeline architecture that customizes a sequence of operations such as packet parsing, packet editing, and packet forwarding according to different network modalities and service demands. By enabling dynamic configuration and adjustment of the processing flow, the proposed design enhances network adaptability and meets both functional and performance requirements across heterogeneous transmission scenarios. Methods Constructing a device-level flexible pipeline faces two primary challenges: (1) it must flexibly process diverse network modal packet protocols across polymorphic network element devices. This requires coordination of heterogeneous resources to enable rapid identification, accurate parsing, and correct handling of packets in various formats; (2) the pipeline construction must remain flexible, offering a mechanism to dynamically generate and configure pipeline structures that can adjust not only the number of stages but also the specific functions of each stage. To address these challenges, this study proposes a polymorphic network element abstraction model that integrates heterogeneous resources. The model adopts a hyper-converged hardware architecture that combines high-performance switching ASIC chips with more programmable but less computationally powerful FPGA and CPU devices. The coordinated operation of hardware and software ensures unified and flexible support for custom network protocols. Building upon the abstraction model, a protocol packet flexible processing compilation mechanism is designed to construct a configurable pipeline architecture that meets diverse network service transmission requirements. This mechanism adopts a three-stage compilation structure consisting of front-end, mid-end, and back-end processes. In response to adaptation issues between heterogeneous resources and differentiated network modal demands, a flexible pipeline technology based on Intermediate Representation (IR) slicing is further proposed. This technology decomposes and reconstructs the integrated IR of multiple network modalities into several IR subsets according to specific optimization methods, preserving original functionality and semantics. By applying the IR slicing algorithm, the mechanism decomposes and maps the hybrid processing logic of multimodal networks onto heterogeneous hardware resources, including ASICs, FPGAs, and CPUs. This process enables flexible customization of network modal processing pipelines and supports adaptive pipeline construction for different transmission scenarios. Results and Discussions To demonstrate the construction effectiveness of the proposed flexible pipeline, a prototype verification system for polymorphic network elements is developed. As shown in Fig. 6, the system is equipped with Centec CTC8180 switch chips, multiple domestic FPGA chips, and domestic multi-core CPU chips. On this polymorphic network element prototype platform, protocol processing pipelines for IPv4, GEO, and MF network modalities are constructed, compiled, and deployed. As illustrated in Fig. 7, packet capture tests verify that different network modalities operate through distinct packet processing pipelines. To further validate the core mechanism of network modal flexible pipeline construction, the IR code size before and after slicing is compared across the three network modalities and allocation strategies described in Section 6.2. The integrated P4 code for the three modalities, after front-end compilation, produces an unsliced intermediate code containing 32,717 lines. During middle-end compilation, slicing is performed according to the modal allocation scheme, generating IR subsets for ASIC, CPU, and FPGA with code sizes of 23,164, 23,282, and 22,772 lines, respectively. The performance of multimodal protocol packet processing is then assessed, focusing on the effects of different traffic allocation strategies on network protocol processing performance. As shown in Fig. 9, the average packet processing delay in Scheme 1 is significantly higher than in the other schemes, reaching 4.237 milliseconds. In contrast, the average forwarding delays in Schemes 2, 3, and 4 decrease to 54.16 microseconds, 32.63 microseconds, and 15.48 microseconds, respectively. These results demonstrate that adjusting the traffic allocation strategy, particularly the distribution of CPU resources for GEO and MF modalities, effectively mitigates processing bottlenecks and markedly improves the efficiency of multimodal network communication. Conclusions Experimental evaluations verify the superiority of the proposed flexible pipeline in construction effectiveness and functional capability. The results indicate that the method effectively addresses complex network environments and diverse service demands, demonstrating stable and high performance. Future work focuses on further optimizing the architecture and expanding its applicability to provide more robust and flexible technical support for protocol packet processing in hyper-converged cloud–network environments.
- Polymorphic networks,
- Protocol packet processing,
- Network programmability,
- Network resource allocation and orchestration

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References(14)

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