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An Autonomous Deployment Mechanism for AI Security Services

IEEE ACCESS 2024年 12卷 4048-4062页

作者： Wang, Weilin Zhou, Huachun Li, Man Yan, Jingfu Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China

Future network architectures are expected to be autonomous, intelligent, and service-based, posing new security challenges. To address these challenges, the Artificial Intelligence (AI) security service emerges as a promising solution. However, the complex service configurations and performance guarantees hinder the autonomous deployment of the AI security service. This paper proposes an autonomous deployment mechanism in Software-Defined networking/network function virtualization (SDN/NFV) enabled networks. First, our mechanism introduces user and decision planes on top of the control plane, enabling hierarchical intent expression and translation from user security intent to security policies. Then, we analyze the embedding problem of the AI-based Security function Chain (AISFC) during security policy generation. We formulate the AISFC embedding problem as an Integer Linear Programming (ILP) task to minimize the total response delay. By decomposing it into AISF placement and routing, we design a heuristic algorithm with polynomial time complexity. Finally, we validate the proposed mechanism through a prototype system and numerical simulations, demonstrating its ability to autonomously translate, implement, and guarantee the user security intent. Comparative analysis shows that our approach considering the relationship between available computing resources and delay achieves smaller response delays than the baseline. Furthermore, our algorithm achieves a gap from optimality approximately 28.57% smaller than the greedy algorithm and supports networks that are 4.34 times larger in scale than the exact solution within a 2-second execution time.

关键词： Security Artificial intelligence Delays Data models Analytical models Task analysis Routing network function virtualization Security management network function virtualization service function chain intent-based network security management

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On-Policy Versus Off-Policy Reinforcement Learning for Multi-Domain SFC Embedding in SDN/NFV-Enabled networks

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IEEE ACCESS 2024年 12卷 123049-123070页

作者： Zhao, Donghao Shi, Wei Lu, Yu Li, Xi Liu, Yicen Army Engn Univ Shijiazhuang Campus Shijiazhuang 050003 Peoples R China China Coast Guard Acad Ningbo 315801 Peoples R China Natl Key Lab Blind Signal Proc Chengdu 610000 Peoples R China

In the software defined network (SDN)/network function virtualization (NFV)-enabled networks, service function chains (SFCs) should typically be allocated to deploy these services, which not only entails meeting the service's Quality of Service (QoS) requirements, but also considering the infrastructure's limitations. Although this issue has received much attention in the literature, the dynamics, intricacy, complexity and unpredictability of the issue provide several difficulties for researchers and engineers. The traditional methods (e.g., exact, heuristic, meta-heuristic, and game, etc.) are subjected to the complexity of multi-domain cloud network scenarios with dynamic network states, high-speed computational requirements, and enormous service requests. Recent studies have shown that reinforcement learning (RL) is a promising way to deal with the limitations of the traditional methods. On-policy and off-policy are two key categories in the field of RL models, and they both have promising advantages in deal with dynamic resource allocation problems. This paper contains two innovative points at two levels. Firstly, in order to deal with SFC embedding problem in dynamic multi-domain networks, a mixed Markov model combining Markov decision process (MDP) and hidden Markov model (HMM) is constructed, and the corresponding RL model-solving algorithms are proposed. Secondly, in order to distinguish the appropriate model in a given network scenario, the on-policy RL based multiple domain SFC embedding algorithm is compared with the off-policy one. The obtained simulation results show that the proposed RL algorithms can outperform the current baselines in terms of delay, load balancing and response time. Furthermore, we also point out that the off-policy based algorithm is more suitable for small-scale dynamic network scenarios, while the on-policy based algorithm is more suitable for medium to large-scale network scenarios with high convergence requirements.

关键词： Hidden Markov models Heuristic algorithms Business Computer architecture Resource management Computational modeling Cloud computing Software defined networking network function virtualization Reinforcement learning Service function chain (SFC) software defined network (SDN) network function virtualization (NFV) reinforcement learning (RL) Markov decision process (MDP) hidden Markov model (HMM)

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Service function Chain Deployment Algorithm Based on Multi-Agent Deep Reinforcement Learning

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Computers, Materials & Continua 2024年第9期80卷 4875-4893页

作者： Wanwei Huang Qiancheng Zhang Tao Liu YaoliXu Dalei Zhang College of Software Engineering Zhengzhou University of Light IndustryZhengzhou450007China Henan Jiuyu Tenglong Information engineering Co. Ltd.Zhengzhou450052China Henan Xin’an Communication Technology Co. Ltd.Zhengzhou450007China

Aiming at the rapid growth of network services,which leads to the problems of long service request processing time and high deployment cost in the deployment of network function virtualization service function chain(SFC)under 5G networks,this paper proposes a multi-agent deep deterministic policy gradient optimization algorithm for SFC deployment(MADDPG-SD).Initially,an optimization model is devised to enhance the request acceptance rate,minimizing the latency and deploying the cost SFC is constructed for the network resource-constrained ***,we model the dynamic problem as a Markov decision process(MDP),facilitating adaptation to the evolving states of network ***,by allocating SFCs to different agents and adopting a collaborative deployment strategy,each agent aims to maximize the request acceptance rate or minimize latency and *** agents learn strategies from historical data of virtual network functions in SFCs to guide server node selection,and achieve approximately optimal SFC deployment strategies through a cooperative framework of centralized training and distributed *** simulation results indicate that the proposed method,while simultaneously meeting performance requirements and resource capacity constraints,has effectively increased the acceptance rate of requests compared to the comparative algorithms,reducing the end-to-end latency by 4.942%and the deployment cost by 8.045%.

关键词： network function virtualization service function chain Markov decision process multi-agent reinforcement learning

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Robust Deployment Model for Parallelized Service function Chains Against Uncertain Traffic Arrival Rates

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IEEE TRANSACTIONS ON network AND SERVICE MANAGEMENT 2025年第2期22卷 2156-2180页

作者： Zhang, Chenlu Sato, Takehiro Oki, Eiji Kyoto Univ Grad Sch Informat Kyoto 6068501 Japan

In network function virtualization, a network service is provided by a service function chain (SFC), which consists of a chain of virtual network functions (VNFs) within a specific order. SFC parallelism allows parallel processing among VNFs to reduce the end-to-end service delay. Existing works handle the service delay without considering traffic uncertainty, which leads to degraded performance on parallel structure balancing and deployment cost saving in the parallelized SFC deployment problem. This paper proposes a robust deployment model for parallelized SFCs against traffic uncertainty that satisfies the requirement of balanced parallel structures and minimizes the deployment cost. We define a traffic uncertainty set that handles both the variation of service traffic arrival rates and the fluctuation of parallel structures. We apply VNF sharing to improve the efficiency of resource allocation. We formulate the proposed model as a mixed integer second-order cone programming (MISOCP) problem. We introduce a heuristic algorithm to handle larger-size problems, where the MISOCP approach is intractable to obtain a solution in a practical time. Numerical results show the advantages of the proposed model in terms of deployment cost over the baseline models.

关键词： network function virtualization service deploy-ment queueing systems robust optimization robust optimization robust optimization

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A Fastformer Assisted DRL Method on Energy Efficient and Interference Aware Service Provisioning

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IEEE TRANSACTIONS ON network AND SERVICE MANAGEMENT 2025年第2期22卷 1801-1811页

作者： Ren, Cheng Gao, Jinsong Wang, Yu Li, Yaxin Southwest Petr Univ Sch Elect Engn & Informat Chengdu 610054 Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 610054 Peoples R China

network function virtualization (NFV) empowered by virtualization technology can achieve flexible virtual network function (VNF) placement. To improve resource utilization and energy efficiency, different VNFs tend to be co-located on common servers, which inevitably intrigues VNF performance degradation induced by hardware resource competition. The problem of energy-efficient and interference-aware service function chain (SFC) provisioning is considered in this paper and envisioned to yield minimum activated servers and maximum average throughput. It is formulated as a mixed integer linear programming (MILP) model to achieve optimal solutions. Then, a gale-shapley based offline approximation algorithm is designed through bipartite matching, to yield an SFC allocation decision in one go with proved competitive ratio. In online scenario, Transformer and its efficient model Fastformer, combined with Graph Attention network (GAT) respectively, are introduced into deep reinforcement learning (DRL) structure for the first time to quickly and accurately abstract features of substrate network and SFC. A DRL-based Fastformer-assisted energy efficient and interference aware SFC provisioning (DRL-EI) algorithm is proposed with an elaborately designed reward function to balance energy consumption and VNF interference. Simulations indicate the gap between DRL-EI and MILP is marginal. DRL-EI outperforms state-of-art work in terms of energy consumption, VNF normalized throughput and acceptance rate.

关键词： Interference Throughput Servers Energy consumption Resource management Adaptation models Transformers Logic gates Upper bound Training network function virtualization deep reinforcement learning fastformer virtual network function interference

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Toward Fine-Gained Services: NFV-Assisted Tracking and Positioning Using Micro-Services for Multi-Robot Cooperation

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IEEE network 2024年第3期38卷 8-16页

作者： Yi, Bo Qiu, Lin Lv, Jianhui Nian, Yingpu Wang, Xingwei Das, Sajal K. Northeastern Univ Coll Comp Sci & Engn Shenyang 110819 Peoples R China Pengcheng Lab Shenzhen 518066 Peoples R China Missouri Univ Sci & Technol Dept Comp Sci Rolla MO 65409 USA

Robotics as a Service (RaaS) emerges as a new paradigm to motivate diversified potential of the "remote-controlled economy" for flexible and efficient service provision with the help of cloud computing. The multi-robot cooperation (MRC) technology has been widely used in various intelligent logistics scenarios, such as warehouses, factories, airports and subway stations, benefiting from the advantages of high operational efficiency and low labor cost. While promising, the corresponding challenge is that the service functions deployed on logistics robots (LRs) are more prone to failures such as resource exhaustion and error configuration in the multi-robot system (MRS). In this way, it becomes extremely important to discover and locate abnormal services as soon as possible so as to ensure the stable and secure operation of MRS, further reducing or even avoiding economic loss. Due to the flexibility, scalability and resilience of network function virtualization (NFV), this paper aims at proposing a NFV based complete service chain tracking and positioning process with a more fine-grained level of LRs. Specifically, a micro-service-based system framework for high-accurate service tracking and fault function positioning is constructed, in which two main micro-service functions are designed for service chain tracking and positioning to maintain the stability and reliability of the MRS. On one hand, the tracking micro-service proposes an improved Hopcroft-Karp algorithm to determine the optimal probing and tracking path for MRC. On the other hand, the positioning micro-service proposes a delay-aware dichotomy probing algorithm to minimize the number of probe packets. Experimental results indicate that the proposed system framework and mechanisms outperform the state-of-the-art methods in terms of tracking and positioning accuracy in the MRS.

关键词： Robots Logistics Target tracking Fault location Scalability Robot sensing systems network function virtualization Resilience Reliability engineering Public transportation Multi-robot systems Remote control RaaS Multi-robot Cooperation Logistics Robots NFV Tracking and Positioning

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Multi-objective optimization of SFC deployment using service aggregation and computing offload

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COMPUTER COMMUNICATIONS 2024年 224卷 60-71页

作者： Xiao, Junbi Zheng, Jiaqi Wen, Wu Guizani, Mohsen Zhang, Peiying Tan, Lizhuang China Univ Petr East China Qingdao Inst Software Coll Comp Sci & Technol Qingdao 266580 Peoples R China Guangzhou Univ Sch Comp Sci & Cyber Engn Guangzhou 510006 Peoples R China Mohamed Bin Zayed Univ Artificial Intelligence MBZ Machine Learning Dept Abu Dhabi 999041 U Arab Emirates Qilu Univ Technol Minist Educ Natl Supercomp Ctr Jinan Key Lab Comp Power Network & Informat SecurShando Jinan 250014 Peoples R China

New technologies such as virtualization and Software Defined networking (SDN) have given traditional networks unprecedented flexibility. A Service function Chain (SFC) formed by concatenating multiple Virtual network functions (VNFs) expands network functions to meet the growing personalized network requirements of applications. However, due to the decentralization of VNF instances and the tight cloud load, how to ensure Quality of Service (Qos) while deploying the SFC brings new challenges. Hence, a SFC deployment strategy based on multi -objective optimization, named MO-SACO is proposed, which regards delay optimization, reliability assurance and cost reduction as the main objectives. Specifically, we first design a VNF aggregation rule to handle raw SFC requests, which effectively reduces the SFC path latency and improves reliability. Then, VNF placement and traffic routing is modeled as a computation offloading decision based on cloudfog -edge collaboration with the goal of target optimization according to functional, location, and resource constraints. Finally, we also take into account the performance changes of deployed SFCs and handle noncompliant requests in a timely manner. We conduct extensive simulations in networks of varying sizes and the results demonstrate that the proposed MO-SACO strategy not only achieves lower latency and cost, higher reliability compared with the state-of-the-art methods, but also has unexpected performance in terms of the deployment success rate and node load.

关键词： network function virtualization Service aggregation Computing offload Service function chain Cloud-fog-edge computing

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Spotlighter: Backup Age-Guaranteed Immersive Virtual Vehicle Service Provisioning in Edge-Enabled Vehicular Metaverse

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第12期23卷 13375-13391页

作者： Qiu, Yu Chen, Min Huang, Hebin Liang, Weifa Liang, Junbin Hao, Yixue Niyato, Dusit South China Univ Technol Sch Comp Sci & Engn Guangzhou 510640 Peoples R China Pazhou Lab Guangzhou 510640 Peoples R China Guangxi Univ Sch Comp Elect & Informat Guangxi Key Lab Multimedia Commun & Network Techno Nanning 118 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 1037 Peoples R China Nanyang Technol Univ Coll Comp Sci & Engn Singapore 639798 Singapore

Edge-enabled Vehicular Metaverse (EVM) is a new paradise supported by various compute-intensive Virtual Vehicle Services (VVSs), where users can immerse and enjoy their spiritual world. User immersion is critical during VVS provisioning in the EVM, yet it can be weakened or curtailed by a sense of disengagement caused by unknown failures. Providing redundant backups VVSs (BVVSs) and keeping the Age of Backup Information (AoBI) could effectively resist and avoid this disengagement when failures occur. However, the trajectories of mobile vehicles are unknown and dynamic, which makes it challenging to optimally migrate VVSs and BVVSs or adjust the update frequency of backup information in real-time, so as to ensure service reliability and AoBI while minimizing the cost of accepting VVS-based metaverse services. In this paper, the above long-term issue is first decomposed into discrete single-slot sub-problems that are modeled as integer linear programming problems. Then, a comprehensive resource explorer named spotlighter is designed, where the first and second parts are a metaverse service home prediction algorithm based on deep learning and a VVS migration algorithm based on randomized rounding, respectively. By tracking the dynamical locations of service homes based on current and historical information, the former can help the latter to adaptively minimize migration costs on VVS re-instantiation and traffic transmission among services and moving vehicles. Finally, a cost-adaptive AoBI guarantee algorithm is merged in spotlighter to ensure the freshness of backup status, by trading-off synchronization cost on BVVS migration, backup update, and backup synchronization. Theoretical analyses and experiments based on real databases show that our algorithms are promising compared with baseline algorithms.

关键词： Metaverse Costs Reliability Synchronization Heuristic algorithms Accuracy Task analysis Age of information Internet of Vehicles metaverse mobile edge cloud network function virtualization reliability

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AdaptNF: Adaptive service chain scheduling with stateless migration and NF consolidation

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Digital Communications and networks 2023年第2期9卷 462-472页

作者： Qing Li He Huang Yong Jiang Jingpu Duan Peng Cheng Laboratory Shenzhen518000China Tsinghua Shenzhen International Graduate School Shenzhen10003China Southern University of Science and Technology Shenzhen14325China

The combination of network function virtualization and software-defined networking allows various network functions to process flows according to their characteristics and *** to the highly dynamic nature of the workload,the network infrastructure needs to properly schedule the underlying resources in order to respond to workload changes in a timely ***,the existing NFV platform lacks a comprehensive solution for how to scale under workload variation,which may seriously hurt the overall system *** improve the scalability of the NFV platform and ensure consistent high performance under dynamic workloads,we propose AdaptNF,a novel NFV platform designed to support a combination of course-grained and fine-grained resource scheduling *** deal with resource imbalance,which is the essential scheduling problem that leads to insufficient NFV performance,AdaptNF adopts a novel algorithm that can efficiently balance the workload among multiple network function instances through stateless flow *** controlled experiments show that the AdaptNF scheme can optimize resource allocation and ensure outstanding performance after *** terms of network throughput and latency,AdaptNF significantly improves the performance of the underlying NFV platform.

关键词： network function virtualization Software-defined networking Service chain Flow migration Dynamic scale

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Caught-in-Translation (CiT): Detecting Cross-Level Inconsistency Attacks in network functions virtualization (NFV)

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2024年第4期21卷 2964-2981页

作者： Lakshmanan, Sudershan Zhang, Mengyuan Majumdar, Suryadipta Jarraya, Yosr Pourzandi, Makan Wang, Lingyu Concordia Univ Concordia Inst Informat Syst Engn Montreal PQ H3G 1M8 Canada Hong Kong Polytech Univ Dept Comp Hung Hom Hong Kong Peoples R China Ericsson Ericsson Secur Res Montreal PQ H4S 0B6 Canada

As one of the main technology pillars of 5 G networks, network functions virtualization (NFV) enables agile and cost-effective deployment of network services. However, the multi-level, multi-actor design of NFV may also allow for inconsistency between the different abstraction levels to be mistakenly or intentionally introduced, as shown in recent studies. Serious security issues, such as man-in-the-middle, network sniffing, and DoS, may arise at one abstraction level without being noticed by the victims at another level. Most existing solutions are either limited to one abstraction level of NFV or reliant on direct access to lower-level data which could become inaccessible when managed by different providers. In this paper, by drawing an analogy between cross-level NFV event sequences and natural languages, we propose a Neural Machine Translation-based approach, namely, Caught-in-Translation (CiT), to detect cross-level inconsistency attacks in NFV at runtime. Specifically, we first extract event sequences from different abstraction levels of an NFV stack. We then leverage Long Short-Term Memory (LSTM) to translate the event sequences from one level to another. Finally, we apply both a similarity metric and a Siamese neural network to compare the translated event sequences with the original ones to detect attacks. We integrate CiT into OpenStack/Tacker, a popular open-source NFV implementation, and evaluate its performance using both real and synthetic data. Experimental results show the benefit of leveraging NMT as CiT achieves AUC >= 96.03%, which significantly outperforms traditional SVM-based anomaly detection. We also evaluate CiT in terms of its efficiency, scalability, and robustness for detecting inconsistency attacks in NFV platforms.

关键词： Cloud computing network function virtualization Security Recurrent neural networks Natural languages Machine translation Firewalls (computing) Inconsistency detection NFV security neural machine translation

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