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学者姓名：张栋
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Network Function Virtualization (NFV) decouples network functions from dedicated hardware devices into Virtual Network Functions (VNFs). These VNFs are chained in order as a Service Function Chain (SFC) to provision flexible and efficient services. When service requests dynamically increase, the intensive workloads often lead to node overloads and further impact the Quality of Service (QoS). Existing works address this problem by migrating VNFs from overload nodes to other low-load nodes, known as VNF migration. However, when a VNF is shared by multiple SFCs, migrating the VNF will change the mapping relationships between these SFCs and the physical network (nodes and links). That may make some SFCs traverse more links and increase their propagation latency. That violates the demand of users for low-latency services. In this paper, to minimize the impact of VNF migration on SFC latency, we propose partial VNF migration. It migrates only partial VNFs within these SFCs to minimize the overall SFC latency while reducing migration costs. As such, we leverage partial VNF migration for efficient latency minimization with the formulation of an integer linear programming (ILP) model. Given the NP-hard nature of the problem, we propose a dynamic latency-aware partial VNF migration algorithm to reduce node overloads and minimize SFC latency. Evaluation indicates that the proposed approach has 12.7%-21.8% lower average SFC latency and 12.5%-48.5% less migration cost than state -of -the -art VNF migration algorithms. And it demonstrates about 90% shorter execution time with similar minimization performance, compared to other SFC reconfiguration algorithms.
Keyword ：
Multi-access edge computing Multi-access edge computing Network function virtualization Network function virtualization Request dynamics Request dynamics Service function chain Service function chain VNF migration VNF migration
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| GB/T 7714 | Liu, Dongquan , Zhou, Zhengyan , Zhang, Dong et al. Efficient service reconfiguration with partial virtual network function migration [J]. | COMPUTER NETWORKS , 2024 , 241 . |
| MLA | Liu, Dongquan et al. "Efficient service reconfiguration with partial virtual network function migration" . | COMPUTER NETWORKS 241 (2024) . |
| APA | Liu, Dongquan , Zhou, Zhengyan , Zhang, Dong , Guo, Kaiwei , Wu, Yanni , Wu, Chunming . Efficient service reconfiguration with partial virtual network function migration . | COMPUTER NETWORKS , 2024 , 241 . |
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Radio access network (RAN) enables large-scale collection of sensitive data. Privacy-preserving techniques aim to learn knowledge from sensitive data to improve services without compromising privacy. However, as the data scale increases, enforcing privacy-preserving techniques on sensitive data may consume a considerable amount of system resources and impose performance penalties. To reduce system resource consumption, we present NetDP, an in-network architecture for privacy-preserving techniques by leveraging programmable switches to improve resource efficiency (i.e., CPU cycles, network bandwidth, and privacy budgets). The key idea of NetDP is to accommodate and exploit cryptographic operators to reduce resource consumption rather than repetitively and exhaustively suppressing the impact of these techniques. To the best of our knowledge, this is the first time that privacy-preserving techniques in a large-scale data processing system have been enforced on programmable switches. Our experiments based on Tofino switches indicate that NetDP significantly reduces computation latency (e.g., 40.2%-55.8% latency in computations) without impacting fidelity.
Keyword ：
Computer architecture Computer architecture Data processing Data processing differential privacy differential privacy Differential privacy Differential privacy In-network computing In-network computing Noise Noise Pipelines Pipelines Privacy Privacy Sensitivity Sensitivity
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| GB/T 7714 | Zhou, Zhengyan , Chen, Hanze , Chen, Lingfei et al. NetDP: In-Network Differential Privacy for Large-Scale Data Processing [J]. | IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING , 2024 , 8 (3) : 1076-1089 . |
| MLA | Zhou, Zhengyan et al. "NetDP: In-Network Differential Privacy for Large-Scale Data Processing" . | IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING 8 . 3 (2024) : 1076-1089 . |
| APA | Zhou, Zhengyan , Chen, Hanze , Chen, Lingfei , Zhang, Dong , Wu, Chunming , Liu, Xuan et al. NetDP: In-Network Differential Privacy for Large-Scale Data Processing . | IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING , 2024 , 8 (3) , 1076-1089 . |
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Modern artificial intelligence (AI) technologies, led by machine learning (ML), have gained unprecedented momentum over the past decade. Following this wave of "AI summer," the network research community has also embraced AI/ML algorithms to address many problems related to network operations and management. However, compared to their counterparts in other domains, most ML-based solutions have yet to receive largescale deployment due to insufficient maturity for production settings. This article concentrates on the practical issues of developing and operating ML-based solutions in real networks. Specifically, we enumerate the key factors hindering the integration of AI/ML in real networks, and review existing solutions to uncover the missing components. Further, we highlight a promising direction, that is, machine learning operations (MLOps), that can close the gap. We believe this article spotlights the system-related considerations on implementing and maintaining ML-based solutions, and invigorates their full adoption in future networks.
Keyword ：
Artificial intelligence Artificial intelligence Costs Costs Data collection Data collection Data models Data models Feature extraction Feature extraction Optimization Optimization Production Production
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| GB/T 7714 | Liu, Qiong , Zhang, Tianzhu , Hemmatpour, Masoud et al. Operationalizing AI/ML in Future Networks: A Bird's Eye View from the System Perspective [J]. | IEEE COMMUNICATIONS MAGAZINE , 2024 , 63 (4) : 176-182 . |
| MLA | Liu, Qiong et al. "Operationalizing AI/ML in Future Networks: A Bird's Eye View from the System Perspective" . | IEEE COMMUNICATIONS MAGAZINE 63 . 4 (2024) : 176-182 . |
| APA | Liu, Qiong , Zhang, Tianzhu , Hemmatpour, Masoud , Qiu, Han , Zhang, Dong , Chen, Chung Shue et al. Operationalizing AI/ML in Future Networks: A Bird's Eye View from the System Perspective . | IEEE COMMUNICATIONS MAGAZINE , 2024 , 63 (4) , 176-182 . |
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Tuple space search(TSS)-based packet classification is the keystone of network system. Previous studies accelerate TSS by partitioning tuples, combining trees and tuples, and merging tuples. However, they do not scale with the number of rules, resulting in a high memory footprint or update time. In this paper, we propose TupleRadar, a framework for accelerating TSS while ensuring low memory footprint and fast rule updates. Our key idea is to construct learned indexes for tuples, which inherently improve the lookup speed but ensure the advantages of TSS. Specifically, TupleRadar builds orderly hash table-based tuples and then constructs the updatable learned index. It provides a bounded memory footprint of the index structure as well. We have evaluated TupleRadar on multiple scales rule-sets. Experimental results show that TupleRadar outperforms previous solutions, reducing 46.66% lookup time and 61.53% memory footprint on average, by up to 86.70% and 88.95%. It also performs a competitive rule update speed.
Keyword ：
learned index learned index packet classification packet classification software-defined networking software-defined networking tuple space search tuple space search
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| GB/T 7714 | Zhu, Longlong , Yu, Jiashuo , Huang, Long et al. TupleRadar: Accelerating Tuple Space Search in Packet Classification by Learned Index [J]. | ACM 32ND INTERNATIONAL SYMPOSIUM ON QUALITY OF SERVICE, IWQOS , 2024 . |
| MLA | Zhu, Longlong et al. "TupleRadar: Accelerating Tuple Space Search in Packet Classification by Learned Index" . | ACM 32ND INTERNATIONAL SYMPOSIUM ON QUALITY OF SERVICE, IWQOS (2024) . |
| APA | Zhu, Longlong , Yu, Jiashuo , Huang, Long , Lin, Hang , Huang, Kaiwei , Zhou, Zhengyan et al. TupleRadar: Accelerating Tuple Space Search in Packet Classification by Learned Index . | ACM 32ND INTERNATIONAL SYMPOSIUM ON QUALITY OF SERVICE, IWQOS , 2024 . |
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The normal operation of data center network management tasks relies on accurate measurement of the network status. In-band Network Telemetry (INT) leverages programmable data planes to provide fine-grained and accurate network status. However, existing INT-related works have not considered the telemetry data required for dynamic adjustments of INT under uninterrupted conditions, including additions, deletions, and modifications. To address this issue, this paper proposes OpenINT, a lightweight and flexible In-band Network Telemetry system. The key innovation of OpenINT lies in decoupling telemetry operations in the data plane, using three generic sub-modules to achieve lightweight telemetry. Meanwhile, the control plane utilizes heuristic algorithms for dynamic planning to achieve near-optimal telemetry paths. Additionally, OpenINT provides primitives for defining network measurement tasks, which abstract the underlying telemetry architecture's details, enabling network operator to conveniently access network status. A prototype of OpenINT is implemented on a programmable switch equipped with the Tofino chip. Experimental results demonstrate that OpenINT achieves highly flexible dynamic telemetry and significantly reduces network overhead.
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| GB/T 7714 | Cai, Jiayi , Lin, Hang , Sun, Tingxin et al. OpenINT: Dynamic In-band Network Telemetry with Lightweight Deployment and Flexible Planning [J]. | IEEE INFOCOM 2024-IEEE CONFERENCE ON COMPUTER COMMUNICATIONS , 2024 : 2488-2497 . |
| MLA | Cai, Jiayi et al. "OpenINT: Dynamic In-band Network Telemetry with Lightweight Deployment and Flexible Planning" . | IEEE INFOCOM 2024-IEEE CONFERENCE ON COMPUTER COMMUNICATIONS (2024) : 2488-2497 . |
| APA | Cai, Jiayi , Lin, Hang , Sun, Tingxin , Zhou, Zhengyan , Zhu, Longlong , Chen, Haodong et al. OpenINT: Dynamic In-band Network Telemetry with Lightweight Deployment and Flexible Planning . | IEEE INFOCOM 2024-IEEE CONFERENCE ON COMPUTER COMMUNICATIONS , 2024 , 2488-2497 . |
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The flow table is a critical component of Software-Defined Networking (SDN). However, flow tables' limited capacity makes them highly vulnerable to flow table overflow attacks (FTOAs). Due to the low attack cost and highly flexible attack forms, it is hard to eradicate FTOAs. This paper addresses three unsolved problems for table security and proposes a robust defense accordingly. First, we reveal that the existing defenses with fixed defense speeds will cause severe packet loss when handling diverse traffic. We prove that deleting multiple rules can efficiently solve this problem and give a rigorous derivation to calculate the suitable deletion number according to the environment. Second, we illustrate that abnormal table occupancy squeezing is a constant characteristic of FTOAs regardless of attack forms. It can be used to identify attacked ports accurately in different scenarios. Third, we mathematically prove that random deletion can guarantee the continuous decrease of malicious flow rules after confirming attacked ports. It achieves fast speed and robust effectiveness in different environments. Based on these findings, we design rDefender, a robust and lightweight defense prototype. We evaluate its effect by designing diverse, powerful attacks and using real-world datasets and topology. The results demonstrate that it achieves the best overall performance compared to six existing mainstream defenses, providing stable security for switch flow tables.
Keyword ：
security security Software-defined networking Software-defined networking table overflow attacks table overflow attacks
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| GB/T 7714 | Kong, Dezhang , Chen, Xiang , Wu, Chunming et al. rDefender: A Lightweight and Robust Defense Against Flow Table Overflow Attacks in SDN [J]. | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY , 2024 , 19 : 9436-9451 . |
| MLA | Kong, Dezhang et al. "rDefender: A Lightweight and Robust Defense Against Flow Table Overflow Attacks in SDN" . | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 19 (2024) : 9436-9451 . |
| APA | Kong, Dezhang , Chen, Xiang , Wu, Chunming , Shen, Yi , Zhou, Zhengyan , Cheng, Qiumei et al. rDefender: A Lightweight and Robust Defense Against Flow Table Overflow Attacks in SDN . | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY , 2024 , 19 , 9436-9451 . |
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The programmable switch offers a limited capacity of packet header vector (PHV) words that store packet header fields and metadata fields defined by network functions. However, existing switch compilers employ inefficient strategies of encoding fields on PHV words. Their encoding wastes scarce PHV words and may result in failures when deploying network functions. In this paper, we propose, a new framework that reuses PHV words for as many fields as possible to achieve resource-efficient PHV encoding. offers a field analyzer and an optimization framework. The analyzer identifies which fields can reuse PHV words while preserving the original packet processing logic. The framework integrates analysis results into its encoding to offer the resource-optimal decisions. Also, to achieve timeliness at runtime, it provides a Greedy-based heuristic, which quickly solves PHV encoding and returns near-optimal results. We evaluate with production-scale network functions. Our results show that reduces the consumption of PHV words by up to 85%.
Keyword ：
Encoding Encoding Metadata Metadata Optimization Optimization Packet header vector Packet header vector Production Production programmable switch programmable switch Program processors Program processors Runtime Runtime Vectors Vectors
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| GB/T 7714 | Chen, Xiang , Zhang, Wenbin , Sun, Xi et al. Resource-Efficient and Timely Packet Header Vector (PHV) Encoding on Programmable Switches [J]. | IEEE-ACM TRANSACTIONS ON NETWORKING , 2024 , 32 (5) : 4191-4206 . |
| MLA | Chen, Xiang et al. "Resource-Efficient and Timely Packet Header Vector (PHV) Encoding on Programmable Switches" . | IEEE-ACM TRANSACTIONS ON NETWORKING 32 . 5 (2024) : 4191-4206 . |
| APA | Chen, Xiang , Zhang, Wenbin , Sun, Xi , Liu, Hongyan , Zhang, Jianshan , Huang, Qun et al. Resource-Efficient and Timely Packet Header Vector (PHV) Encoding on Programmable Switches . | IEEE-ACM TRANSACTIONS ON NETWORKING , 2024 , 32 (5) , 4191-4206 . |
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In network measurement, data plane switches measure traffic and report events (e.g., heavy hitters) to the control plane via control channels. The control plane makes decisions to process events. However, current network measurement suffers from two problems. First, when traffic bursts occur, massive events are reported in a short time so that the control channels may be overloaded due to limited bandwidth capacity. Second, only a few events are reported in normal cases, making control channels underloaded and wasting network resources. In this paper, we propose Escala to provide the elastic scaling of control channels at runtime. The key idea is to dynamically migrate event streams among control channels to regulate the loads of these channels. Escala offers two components, including an Escala monitor that detects scaling situations based on realtime network statistics, and an optimization framework that makes scaling decisions to eliminate overload and underload situations. We have implemented a prototype of Escala on Tofino-based switches. Extensive experiments show that Escala achieves timely elastic scaling while preserving high application-level accuracy.
Keyword ：
Accuracy Accuracy Atmospheric measurements Atmospheric measurements Bandwidth Bandwidth control channel scaling control channel scaling Control systems Control systems Monitoring Monitoring network event collection network event collection Network measurement Network measurement Optimization Optimization Particle measurements Particle measurements programmable networks programmable networks Runtime Runtime Servers Servers Time measurement Time measurement
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| GB/T 7714 | Liu, Hongyan , Chen, Xiang , Huang, Qun et al. Elastically Scaling Control Channels in Network Measurement With Escala [J]. | IEEE-ACM TRANSACTIONS ON NETWORKING , 2024 , 33 (2) : 777-792 . |
| MLA | Liu, Hongyan et al. "Elastically Scaling Control Channels in Network Measurement With Escala" . | IEEE-ACM TRANSACTIONS ON NETWORKING 33 . 2 (2024) : 777-792 . |
| APA | Liu, Hongyan , Chen, Xiang , Huang, Qun , Kong, Dezhang , Zhang, Dong , Wu, Chunming et al. Elastically Scaling Control Channels in Network Measurement With Escala . | IEEE-ACM TRANSACTIONS ON NETWORKING , 2024 , 33 (2) , 777-792 . |
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In modern data center networks (DCNs), network-stack processing denotes a large portion of the end-to-end latency of TCP flows. So profiling network-stack latency anomalies has been considered as a crucial part in DCN performance diagnosis and troubleshooting. In particular, such profiling requires full coverage (i.e., profiling every TCP packet) and low overhead (i.e., profiling should avoid high CPU consumption in end-hosts). However, existing solutions rely on system calls or tracepoints in end-hosts to implement network-stack latency profiling, leading to either low coverage or high overhead. We propose Torp, a framework that offers full-coverage and low-overhead profiling of network-stack latency. Our key idea is to offload as much of the profiling from costly system calls or tracepoints to the Torp agent built on eBPF modules, and further to include a Torp handler on the ToR switch to accelerate the remaining profiling operations. Torp efficiently coordinates the ToR switch and the Torp agent on end-hosts to jointly execute the entire latency profiling task. We have implemented Torp on $32\times 100$ Gbps Tofino switches. Testbed experiments indicate that Torp achieves full coverage and orders of magnitude lower host-side overhead compared to other solutions.
Keyword ：
Computer science Computer science Hardware-software coordination Hardware-software coordination Hash functions Hash functions Instruments Instruments Kernel Kernel latency profiling latency profiling Monitoring Monitoring Network function virtualization Network function virtualization programmable switches programmable switches Real-time systems Real-time systems
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| GB/T 7714 | Chen, Xiang , Liu, Hongyan , Zhang, Wenbin et al. Toward Full-Coverage and Low-Overhead Profiling of Network-Stack Latency [J]. | IEEE-ACM TRANSACTIONS ON NETWORKING , 2024 , 32 (5) : 4441-4455 . |
| MLA | Chen, Xiang et al. "Toward Full-Coverage and Low-Overhead Profiling of Network-Stack Latency" . | IEEE-ACM TRANSACTIONS ON NETWORKING 32 . 5 (2024) : 4441-4455 . |
| APA | Chen, Xiang , Liu, Hongyan , Zhang, Wenbin , Huang, Qun , Zhang, Dong , Zhou, Haifeng et al. Toward Full-Coverage and Low-Overhead Profiling of Network-Stack Latency . | IEEE-ACM TRANSACTIONS ON NETWORKING , 2024 , 32 (5) , 4441-4455 . |
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区块链存在网络动态性强和其管理困难等问题，使得区块链普遍存在DDoS 攻击和账户接管等异常现象. 现有区块链异常检测方法多从所有区块链账户中提取历史交易信息和交易频率等特征加以分析以甄别异常. 然而，随着区块链数据规模的不断扩大，现有方法在提取特征时面临内存消耗高、检测精度低的挑战. 为此，提出了一种高检测精度、低内存开销的区块链异常检测机制，该机制采用近似测量算法将区块链异常检测转化为异常交易账户检测，包括区块内异常账户和跨区块异常账户. 对于区块内异常账户，即仅存在于单个区块内的异常账户，使用Sketch算法进行账户识别，精度高. 而对于存在于多个区块且难以通过单个区块信息检测到的跨区块异常账户，则通过聚合和分析多区块信息进行账户的准确检测. 使用包含88 847个区块的真实区块链数据评估上述机制. 实验结果表明，与现有代表性方法对比，所提出的机制将区块链异常检测的召回率最高提升了6.3倍，F1分数最高提升了4.4倍. 因此，提出的高精度区块链异常检测机制对于规范区块链交易行为、维护系统安全性具有意义.
Keyword ：
Sketch Sketch 区块链 区块链 安全 安全 异常检测 异常检测 近似算法 近似算法
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| GB/T 7714 | 朱龙隆 , 陈翔 , 陈浩东 et al. 基于近似测量算法Sketch的高精度区块链异常检测机制 [J]. | 计算机研究与发展 , 2024 , 61 (10) : 2526-2539 . |
| MLA | 朱龙隆 et al. "基于近似测量算法Sketch的高精度区块链异常检测机制" . | 计算机研究与发展 61 . 10 (2024) : 2526-2539 . |
| APA | 朱龙隆 , 陈翔 , 陈浩东 , 牛继堂 , 刘雯靓 , 林声睿 et al. 基于近似测量算法Sketch的高精度区块链异常检测机制 . | 计算机研究与发展 , 2024 , 61 (10) , 2526-2539 . |
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