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Monte: SFCs Migration Scheme in the Distributed programmable data plane

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2025年第4期36卷 633-644页

作者： Zhang, Xiaoquan Cui, Lin Tso, Fung Po Deng, Yuhui Li, Zhetao Jia, Weijia Jinan Univ Coll Informat Sci & Technol Natl & Local Joint Engn Res Ctr Network Secur Dete Guangdong Prov Key Lab Data Secur & Privacy Protec Guangzhou 510632 Peoples R China Loughborough Univ Dept Comp Sci Loughborough LE11 3TU England Beijing Normal Univ BNU Zhuhai BNU UIC Inst Artificial Intelligence & Future Netw Zhuhai 100875 Peoples R China BNU HKBU United Int Coll Zhuhai 100875 Peoples R China

Service function chains (SFCs) are sequences of network functions that provide specific services to meet operators' needs in today's ISPs and datacenter networks. To improve the performance of SFCs, programmable data planes are used to leverage their low latency and high performance packet processing. However, SFCs need to be adaptable to dynamics such as changes in requirements and attributes. Therefore, the ability to migrate SFCs is essential. Unfortunately, migrating SFCs in distributed programmable data planes is challenging due to the risk of degraded performance and failure to meet SFCs requirements and resource constraints in switches. In this paper, we propose Monte, which provides an effective SFCs migration scheme in distributed programmable data planes. We build a novel integer programming model to represent the migration process with constraints on resource limitations of switches and SFCs attributes in the distributed data plane. Additionally, an SFCs migration algorithm is designed to optimize the migration cost by deeply analyzing resource allocation in the switch pipeline. Monte has been implemented on both P4 software switches (Bmv2) and hardware switches (Intel Tofino ASIC). Extensive evaluation results show that the migration cost in Monte is 94.03% lower on average than the state-of-the-art deployment scheme, and Monte can effectively save pipeline resources.

关键词： Pipelines Costs Substrates Random access memory Switches Logic Hardware Computer architecture Throughput Distributed databases Network function migration P4 service function chain (SFC) programmable data plane

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Flow-Aware Service Function Embedding Algorithm in programmable data plane

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IEEE ACCESS 2021年 9卷 6113-6121页

作者： Lee, Jaewook Ko, Haneul Lee, Hochan Pack, Sangheon Korea Univ Sch Elect Engn Seoul 02841 South Korea Korea Univ Dept Comp Convergence Software Sejong 30019 South Korea

Service function chaining (SFC) is an indispensable technique for Internet service providers to efficiently manage their networks. However, SFC poses requirements of additional processing time of service functions (SFs) and increased routing time owing to detoured paths. In this paper, we introduce the use of a programmable data plane (PDP) to reduce the additional processing and routing times in SFC. We first classify the existing PDP-empowered SFC schemes and analyze their pros and cons. An optimization problem, to find the optimal SF embedding strategy minimizing the SFC completion time while efficiently utilizing the PDP switch resources, is formulated and a flow-aware SF embedding (FASE) algorithm that complementarily combines the redundant SF and re-circulation approaches is devised. FASE is implemented over a commercial PDP switch and experimental results demonstrate that FASE can reduce the SFC completion time by up to 33% compared with conventional approaches while utilizing the switch resources efficiently.

关键词： Service function chaining programmable data plane in-network computing

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Synchronizing DDoS detection and mitigation based graph learning with programmable data plane, SDN

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2024年 154卷 206-218页

作者： Ma, Jie Su, Wei Li, Yikun Peng, Yihua Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China

The availability of SD-IoT is now under complex and serious cyber threats, especially distributed denialof-service attacks. However, traditional defense schemes suffer from coarse-grained centralized sampling approaches, low accuracy of detection models, and inefficient mitigation methods. In this paper, a novel DDoS defense scheme is proposed, which consists of a high-accuracy detection mechanism based on a Graph Convolutional Neural Network learning model and a mitigation mechanism based on fast traffic migration. In the detection stage, a fine-grained INT sampling approach is utilized to obtain multidimensional network topology and status information. The Graph Convolutional Neural Network learning model detects switches containing DDoS attack traffic with high accuracy because the detection model not only extracts and utilizes multiple temporal and spatial features of the collected information, but also has a better learning and representation capability. In the mitigation stage, the enhanced whitelist with dynamic threshold-based values is automatically adapted to the real -time state of the network environment for enhanced mitigation flexibility. The fast programmable segment rerouting strategy can block attack traffic in time and ensure the continuity of network services. The results of several comparison experiments show that the proposed scheme can detect DDoS attacks more accurately and mitigate them more effectively than traditional schemes.

关键词： Software defined network programmable data plane Distributed denial of service In-band network telemetry Graph convolutional neural network

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A Survey on In-Network Computing: programmable data plane and Technology Specific Applications

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IEEE COMMUNICATIONS SURVEYS AND TUTORIALS 2023年第1期25卷 701-761页

作者： Kianpisheh, Somayeh Taleb, Tarik Univ Oulu Ctr Wireless Commun Oulu 019098 Finland

In comparison with cloud computing, edge computing offers processing at locations closer to end devices and reduces the user experienced latency. The new recent paradigm of in-network computing employs programmable network elements to compute on the path and prior to traffic reaching the edge or cloud servers. It advances common edge/cloud server based computing through proposing line rate processing capabilities at closer locations to the end devices. This paper discusses use cases, enabler technologies and protocols for in-network computing. According to our study, considering programmable data plane as an enabler technology, potential in-network computing applications are in-network analytics, in-network caching, in-network security, and in-network coordination. There are also technology specific applications of in-network computing in the scopes of cloud computing, edge computing, 5G/6G, and NFV. In this survey, the state of the art, in the framework of the proposed categorization, is reviewed. Furthermore, comparisons are provided in terms of a set of proposed criteria which assess the methods from the aspects of methodology, main results, as well as application-specific criteria. Finally, we discuss lessons learned and highlight some potential research directions.

关键词： In-network computing programmable data plane software defined networking cloud computing edge computing 6G and network function virtualization

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Compiling Service Function Chains via Fine-Grained Composition in the programmable data plane

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2023年第4期16卷 2490-2502页

作者： Zhang, Xiaoquan Cui, Lin Tso, Fung Po Jia, Weijia Jinan Univ Dept Comp Sci Guangdong Prov Key Lab Data Secur & Privacy Protec Guangzhou 510632 Peoples R China Loughborough Univ Dept Comp Sci Loughborough LE11 3TU England Beijing Normal Univ BNU Zhuhai BNU UIC Inst Artificial Intelligence & Future Netw Zhuhai 519088 Peoples R China BNU HKBU United Int Coll Zhuhai 519088 Peoples R China

Service function chains (SFCs) are fundamental services in today's datacenters and ISP networks. Explosive volume of network traffic creates high demands for low latency and high performance. The emergence of programmable data planes has offered a new way to overcome the problem. However, limited by pipeline constraints in hardware architecture, implementing multiple network functions on programmable data planes is challenging. Besides, considering various types of network functions, e.g., stateful network functions, a general model is essential for abstracting distinct network functions. In this article, we propose pSFC which provides a fine-grained SFCs deployment scheme in programmable data planes. Control flow graph (CFG) is proposed to abstract and analyze various network functions. Then we model pipeline constraints in the hardware architecture using an ILP (Integer Linear Programming), and model the SFCs deployment in the substrate network as a one big switch (OBS) problem. To reduce deployment cost, pSFC first composes multiple SFCs to a compound CFG for eliminating redundant logics within SFCs, further decomposes the compound CFG based on the resource limitation per stage, and finally maps the OBS into the substrate network. We have implemented pSFC in both bmv2 software switch and P4 hardware switch (i.e., Intel Tofino ASIC). Evaluation results show that pSFC reduces switch costs by 45.7% and decreases average latency by 22% without compromising throughput.

关键词： P4 service function chains programmable data plane one big switch

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Dapper: Deploying Service Function Chains in the programmable data plane Via Deep Reinforcement Learning

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2023年第4期16卷 2532-2544页

作者： Zhang, Xiaoquan Cui, Lin Tso, Fung Po Li, Zhetao Jia, Weijia Jinan Univ Coll Informat Sci & Technol Guangdong Prov Key Lab Data Secur & Privacy Protec Network Secur Detect & Protect TechnolNatl & Loca Guangzhou 510632 Peoples R China Loughborough Univ Dept Comp Sci Loughborough LE11 3TU England Beijing Normal Univ BNU Zhuhai BNU UIC Inst Artificial Intelligence & Future Netw Zhuhai 100875 Peoples R China BNU HKBU United Int Coll Zhuhai 100875 Peoples R China

Network functions perform specific packet processing on network traffic. To meet operators' needs, forming service function chains (SFCs) is a fundamental technique used in today's ISPs and datacenter networks. Implementing SFCs in the programmable data plane with high throughput and low latency is a new approach to satisfy demands of ever-growing network traffic. Previous works have proposed different solutions to solve the problem, but they all inevitably have to make trade-offs between running time and performance. For example, an ILP (Integer Linear Programming) can optimize cost but suffers from long running time in large-scale network topologies. Heuristic algorithms depend strongly on manual designs and usually have a performance gap with the optimal solution. In this paper, we propose Dapper, a framework for deploying SFCs in the programmable data plane using DRL (Deep Reinforcement Learning) with graph convolutional network. In order to expand the searching space to prevent the optimal value from being missed, Dapper allows the RL (Reinforcement Learning) agent to simultaneously extract features from both the substrate network and the hardware pipeline, and exploit a graph convolutional network to enhance performance. Moreover, a mask mechanism is also designed to accelerate Dapper and improve its scalability. Dapper has been implemented and extensively evaluated on both P4 hardware switches (equipped with Intel Tofino ASIC) and software switches (i.e., bmv2). Experimental results show that Dapper can automatically generate deployment solutions in a few seconds of running time after training. They also demonstrate that Dapper reduces hardware stage usage and the latency of SFCs by up to 17.8% and 50 similar to 73% respectively on average when compared with heuristics.

关键词： Deep reinforcement learning p4 programmable data plane service function chain

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Passive In-Band Network Telemetry Systems: The Potential of programmable data plane on Network-Wide Telemetry

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IEEE ACCESS 2021年 9卷 20391-20409页

作者： Manzanares-Lopez, Pilar Pedro Munoz-Gea, Juan Malgosa-Sanahuja, Josemaria Univ Politecn Cartagena Dept Informat Technol & Commun Cartagena 30202 Spain

In the last few years, the emergence of programmable data planes and the appearance of programming protocol-independent languages such as P4 have offered powerful tools to define new network protocols, as well as to redesign existing network applications and systems. Network telemetry is one of the main areas of interest identified by the P4 Application Working Group. The collection of network-wide, fine-grained network information in real-time is a critical requirement for the design of useful and adequate monitoring tools that can be integrated into complex Operations, Administration & Maintenance applications. Recent research has focused on the definition and implementation of in-band monitoring systems, where specifically dedicated monitoring packets are not required. Even though the In-Band Network Telemetry specification proposed by the P4 Language Consortium is the starting point of many of the in-band monitoring systems, this is not the only alternative. Therefore, in this work, we will describe and compare other P4-based in-band passive telemetry proposals.

关键词： Telemetry Computer architecture Monitoring Protocols Programming Software Proposals In-band network telemetry network monitoring P4 passive monitoring programmable data plane remote network management

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pHeavy: Predicting Heavy Flows in the programmable data plane

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2021年第4期18卷 4353-4364页

作者： Zhang, Xiaoquan Cui, Lin Tso, Fung Po Jia, Weijia Jinan Univ Dept Comp Sci Guangzhou 510632 Peoples R China Loughborough Univ Dept Comp Sci Loughborough LE11 3TU Leics England Beijing Normal Univ BNU UIC Inst Artificial Intelligence & Future Net Zhuhai 519087 Peoples R China BNU HKBU United Int Coll Guangdong Key Lab AI & Multimodal Data Proc Zhuhai 519087 Peoples R China

Since heavy flows account for a significant fraction of network traffic, being able to predict heavy flows has benefited many network management applications for mitigating link congestion, scheduling of network capacity, exposing network attacks and so on. Existing machine learning based predictors are largely implemented on the control plane of Software Defined Networking (SDN) paradigm. As a result, frequent communication between the control and data planes can cause unnecessary overhead and additional delay in decision making. In this paper, we present pHeavy, a machine learning based scheme for predicting heavy flows directly on the programmable data plane, thus eliminating network overhead and latency to SDN controller. Considering the scarce memory and limited computation capability in the programmable data plane, pHeavy includes a packet processing pipeline which deploys pre-trained decision tree models for in-network prediction. We have implemented pHeavy in both bmv2 software switch and P4 hardware switch (i.e., Barefoot Tofino). Evaluation results demonstrate that pHeavy has achieved 85% and 98% accuracy after receiving the first 5 and 20 packets of a flow respectively, while being able to reduce the size of decision tree by 5.4x on average. More importantly, pHeavy can predict heavy flows at line rate on the P4 hardware switch.

关键词： Machine learning Switches Predictive models Machine learning algorithms Decision trees Training Computational modeling Heavy flow decision tree programmable data plane P4

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DCI-NACC: flow scheduling and congestion control based on programmable data plane in high-performance data center networks

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2022年第1期122卷 51-63页

作者： Geng, Junjie Commun Univ China State Key Lab Media Convergence & Commun Beijing Peoples R China

PFC (Priority-based Flow Control), which is used to give full play to the performance advantages of RDMA, can cause problems such as head-of-line (HoL) blocking. PFC-based method cannot eliminate the congestion fundamentally. At the same time, another approach to deal with congestion in the data center networks, commonly known as "injection throttling" mechanism, may result in slower effective response due to the delay between congestion detection and feedback to the sources. In this article, we have optimized the two types of algorithms (DCI and NACC) and used them in combination (DCI-NACC). We propose a novel scheduling strategy of combining PFC-based algorithm and "injection throttling"-based algorithm using programmable data plane device for lossless data center networks. Specifically, we design a dynamic congestion isolation (DCI) method to optimize PFC. DCI ensures lossless transmission in the data center networks and eliminates the HoL blocking problem caused by PFC. We also propose a network-assisted congestion control (NACC) algorithm in which the feedback information is directly sent from the switch where congestion occurs in the network. NACC increases the efficiency of network congestion perception and reduces the delay that feedback take effect. Experimental results show that, compared with PFC and ECN algorithm, DCI-NACC algorithm has significant improvement in network performance (normalized throughput, network latency, and flow completion time). Especially, simulation experiments show that NACC can greatly reduce the number of times that PFC PAUSE is triggered. The simulation experiments confirm that DCI-NACC algorithm can effectively improve the performance of the data center networks on the basis of lossless transmission.

关键词： Congestion control Lossless transmission programmable data plane Traffic scheduling High-performance data centers

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P-SCOR: Integration of Constraint Programming Orchestration and programmable data plane

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2021年第1期18卷 402-414页

作者： Melis, Andrea Layeghy, Siamak Berardi, Davide Portmann, Marius Prandini, Marco Callegati, Franco Univ Bologna Dept Comp Sci & Engn I-47521 Cesena Italy Univ Queensland Sch ITEE Brisbane Qld 4072 Australia

In this manuscript we present an original implementation of network management functions in the context of Software Defined Networking. We demonstrate a full integration of an artificial intelligence driven management, an SDN control plane, and a programmable data plane. Constraint Programming is used to implement a management operating system that accepts high level specifications, via a northbound interface, in terms of operational objective and directives. These are translated in technology-specific constraints and directives for the SDN control plane, leveraging the programmable data plane, which is enriched with functionalities suited to feed data that enable the most effective operation of the "intelligent" control plane, by exploiting the P4 language.

关键词： SDN constraint programming P4 programmable data plane security

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