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Online Coordinated NFV Resource Allocation via Novel Machine Learning Techniques

IEEE TRANSACTIONS ON network AND SERVICE MANAGEMENT 2023年第1期20卷 563-577页

作者： Li, Zhiyuan Wu, Lijun Zeng, Xiangyun Yue, Xiaofeng Jing, Yulin Wu, Wei Su, Kaile Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 610054 Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Griffith Univ Sch Informat & Commun Technol Brisbane Qld 4222 Australia

Thanks to network function virtualization (NFV), Internet Service Providers (ISPs) can improve network resource utilization with significantly reduced capital and operational expenditures. To dig deeper into the potential of NFV, an important challenge is the resource allocation problem in NFV (NFV-RA), which can be divided into three stages: VNFs chain composition, VNF forwarding graph embedding, and VNFs scheduling. The key to the NFV-RA problem is to design an effective and coordinated resource allocation algorithm for the three stages. Besides, the NFV-RA problem has been proved to be NP-Hard, and thus most existing approaches focus on heuristic and meta-heuristic algorithms. In this paper, we propose an NFV online coordinated resource allocation framework (OCRA) that completes the three stages simultaneously in a coordinated manner by combining parallel Multi-Agent Deep Reinforcement Learning with novel neural networks and RL training techniques. The extensive experimental results show that compared with the state-of-the-art solutions, OCRA is highly-efficient in terms of time, with up to 50% and 10.8% improvement on resource overhead and acceptance ratio, respectively.

关键词： Resource management Optimization Feature extraction network function virtualization Training Reinforcement learning Heuristic algorithms resource allocation multi-agent deep reinforcement learning

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NDFuzz:a non-intrusive coverage-guided fuzzing framework for virtualized network devices

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Cybersecurity 2023年第1期6卷 54-74页

作者： Yu Zhang Nanyu Zhong Wei You Yanyan Zou Kunpeng Jian Jiahuan Xu Jian Sun Baoxu Liu Wei Huo Institute of Information Engineering Chinese Academy of SciencesBejingChina School of Cyber Security University of Chinese Academy of SciencesBejingChina Key Laboratory of Network Assessment Technology Chinese Academy of SciencesBeijingChina Beijing Key Laboratory of Network Security and Protection Technology BeijingChina Renmin University of China BeijingChina

network function virtualization provides programmable in-network middlewares by leveraging virtualization tech-nologies and commodity hardware and has gained popularity among all mainstream network device *** it is challenging to apply coverage-guided fuzzing,one of the state-of-the-art vulnerability discovery approaches,to those virtualized network devices,due to inevitable integrity protection adopted by those *** this paper,we propose a coverage-guided fuzzing framework NDFuzz for virtualized network devices with a novel integrity protec-tion bypassing method,which is able to distinguish processes of virtualized network devices from hypervisors with a carefully designed non-intrusive page global directory inference *** implement NDFuzz atop of two black-box fuzzers and evaluate NDFuzz with three representative network protocols,SNMP,DHCP and NTP,on nine popular virtualized network *** obtains an average 36%coverage improvement in comparison with its black-box *** discovers 2 O-Day vulnerabilities and 11-Day vulnerability with coverage guidance while the black-box fuzzer can find only one of *** discovered vulnerabilities are confirmed by corresponding vendors.

关键词： Coverage-guided fuzzing network devices network function virtualization

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Heuristic-Based Service Chain Construction with Security-Level Management

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IEICE TRANSACTIONS ON COMMUNICATIONS 2023年第12期E106B卷 1380-1391页

作者： Amaya, Daisuke Tachibana, Takuji Univ Fukui Grad Sch Engn Fukui 9108507 Japan

network function virtualization (NFV) technology significantly changes the traditional communication network environments by providing network functions as virtual network functions (VNFs) on commercial off-the-shelf (COTS) servers. Moreover, for using VNFs in a pre-determined sequence to provide each network service, service chaining is essential. A VNF can provide multiple service chains with the corresponding network function, reducing the number of VNFs. However, VNFs might be the source or the target of a cyberattack. If the node where the VNF is installed is attacked, the VNF would also be easily attacked because of its security vulnerabilities. Contrarily, a malicious VNF may attack the node where it is installed, and other VNFs installed on the node may also be attacked. Few studies have been done on the security of VNFs and nodes for service chaining. This study proposes a service chain construction with security-level management. The security-level management concept is introduced to built many service chains. Moreover, the cost optimization problem for service chaining is formulated and the heuristic algorithm is proposed. We demonstrate the effectiveness of the proposed method under certain network topologies using numerical examples.

关键词： network function virtualization virtual network function service chaining security management optimization problem

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Enabling Dynamic Autoscaling for NFV in a Non-Standalone Virtual EPC: Design and Analysis

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第6期72卷 7743-7756页

作者： Ren, Yi Phung-Duc, Tuan Chen, Jyh-Cheng Li, Frank Y. Natl Chiao Tung Univ Dept Comp Sci Hsinchu 30010 Taiwan Univ East Anglia UEA Sch Comp Sci Norwich NR4 7TJ England Univ Tsukuba Inst Syst & Informat Engn Ibaraki 3058573 Japan Natl Yang Ming Chiao Tung Univ NYCU Dept Comp Sci Hsinchu 30010 Taiwan Univ Agder UiA Dept Informat & Commun Technol N-4898 Grimstad Norway

network function virtualization (NFV) is a novel concept that enables an architectural transition from dedicated hardware to orchestrated resource and function management. As an integral part of the core network, NFV offers a fine-grained network capability to cellular operators by scaling out or scaling in network resources in an on-demand manner to meet the performance requirements. However, designing an autoscaling algorithm with low operation cost and low latency in non-standalone networks, where legacy network equipment coexists with a virtual evolved packet core (EPC), is a challenging task. In this paper, we propose a dynamic NFV instance autoscaling algorithm that considers the tradeoff between performance and operation cost. Furthermore, we develop an analytical framework to assess the performance of the scheme by modeling the hybrid network as a queueing system that includes both legacy network equipment and NFV instances. The virtualized network function (VNF) instances are powered on or off according to the number of job requests. Numerical results based on extensive simulations validate the correctness of the model and the effectiveness of the algorithm.

关键词： EPC network function virtualization NFV instance resource allocation dynamic autoscaling algorithm modeling and analysis

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Leveraging Deep Reinforcement Learning With Attention Mechanism for Virtual network function Placement and Routing

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2023年第4期34卷 1186-1201页

作者： He, Nan Yang, Song Li, Fan Trajanovski, Stojan Zhu, Liehuang Wang, Yu Fu, Xiaoming Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China Microsoft London W2 6BD England Beijing Inst Technol Sch Cyberspace Secur Beijing 100081 Peoples R China Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA Univ Gottingen Inst Comp Sci D-37073 Gottingen Germany

The efficacy of network function virtualization (NFV) depends critically on (1) where the virtual network functions (VNFs) are placed and (2) how the traffic is routed. Unfortunately, these aspects are not easily optimized, especially under time-varying network states with different QoS requirements. Given the importance of NFV, many approaches have been proposed to solve the VNF placement and Service function Chaining (SFC) routing problem. However, those prior approaches mainly assume that the network state is static and known, disregarding dynamic network variations. To bridge that gap, we leverage Markov Decision Process (MDP) to model the dynamic network state transitions. To jointly minimize the delay and cost of NFV providers and maximize the revenue, we first devise a customized Deep Reinforcement Learning (DRL) algorithm for the VNF placement problem. The algorithm uses the attention mechanism to ascertain smooth network behavior within the general framework of network utility maximization (NUM). We then propose attention mechanism-based DRL algorithm for the SFC routing problem, which is to find the path to deliver traffic for the VNFs placed on different nodes. The simulation results show that our proposed algorithms outperform the state-of-the-art algorithms in terms of network utility, delay, cost, and acceptance ratio.

关键词： placement routing Deep reinforcement learning network function virtualization placement routing

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Service Chain Provisioning Model Considering Traffic Changes Due to Virtualized network functions

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IEEE TRANSACTIONS ON network AND SERVICE MANAGEMENT 2023年第4期20卷 4779-4802页

作者： Ozaki, Shintaro Sato, Takehiro Oki, Eiji Kyoto Univ Grad Sch Informat Kyoto 6068501 Japan

Service chaining provides network services by flexibly configuring service chains that connect virtualized network functions (VNFs) in the appropriate order so as to satisfy users' needs. Existing models can be inefficient in terms of consuming network and computation resources since the models do not consider the traffic changes due to VNFs or the models restrict routing and VNF placement. This paper proposes a service chain provisioning model that handles the traffic changes created by VNFs while determining the VNF visit order of each request, request routes, and VNF placement. The service chain provisioning problem is formulated as an integer linear programming (ILP) problem. Three methods for limiting the number of VNF visit order patterns considered in the ILP problem are introduced to shorten the computation time. In order to handle a problem that is intractable with the ILP model, we introduce a greedy algorithm and an algorithm that divides the problem into the VNF placement part and the routing part. Numerical results show that considering the traffic changes due to VNFs yields more efficient consumption of network and computation resources than the alternative of assuming that the traffic amount of each request is constant between the endpoints. The results also show that the computation time can be shortened in our examined scenarios while we obtain the objective value larger by at most 0.4% than the optimal value by limiting the number of visit order patterns considered.

关键词： network function virtualization service chaining traffic changing effect integer linear programming

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An Architecture for AoI and Cache Hybrid Multicast/Unicast/D2D With Cell-Free Massive MIMO Systems

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IEEE ACCESS 2023年 11卷 43080-43088页

作者： Zhou, Meng Li, Jinwen Yuan, Jiantao Xie, Mangang Tan, Weiqiang Yin, Rui Yang, Longxiang Hangzhou City Univ Sch Informat & Elect Engn Hangzhou 310015 Peoples R China Zhejiang Engn Res Ctr Bldg Digital Carbon Neutral Coll Mech Engn Hangzhou 310013 Peoples R China Anhui Univ Sci & Technol Coll Mech Engn Huainan 232001 Peoples R China Northwest Normal Univ Coll Comp Sci & Engn Lanzhou Peoples R China Guangzhou Univ Sch Comp Sci & Cyber Engn Guangzhou 510006 Peoples R China Nanjing Univ Posts & Telecommun Wireless Commun Key Lab Jiangsu Prov Nanjing 210003 Peoples R China

The leitmotif of this paper aims at providing an architecture for the age of information (AoI) and cache-assisted hybrid multicast/unicast/device-to-device (D2D) transmission with the promising cell-free massive multiple-input multiple-output (MIMO) technology, for enriching data transmission rate, slackening the burden of high-volume data traffic, and boosting the system efficiency. Then, we design the user-centric transmission scheme and talk about the feasibility of the preliminary solutions for the multigroup multicast, D2D, wireless caching, and AoI, typically for signal processing, transmission mode selection mechanism, and cache replacement strategy. Finally, we also shall afford a viable research prediction containing an ensemble of the potential challenges and promising research directions to set the path for re-inventing an efficient sixth-generation (6G) communication system.

关键词： 6G mobile communication Device-to-device communication Wireless communication Array signal processing Unicast Ultra reliable low latency communication network function virtualization Multicast communication AoI cache cell-free massive MIMO D2D graph neural network hybrid transmission multicast

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An Aggressive Migration Strategy for Service function Chaining in the Core Cloud

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IEEE TRANSACTIONS ON network AND SERVICE MANAGEMENT 2023年第2期20卷 2025-2039页

作者： Feng, Haoxian Shu, Zhaogang Taleb, Tarik Wang, Yuantao Liu, Zhiwei Fujian Agr & Forestry Univ Comp & Informat Coll Fuzhou 350002 Peoples R China Univ Oulu Ctr Wireless Commun Espoo 90014 Finland

Service function Chaining (SFC) is regarded as an important concept for next-generation communication networks because it can flexibly tackle diverse usage scenarios. Due to SFC requests' life-cycle and resource adjustment, the distribution of the remaining physical resources may become unbalanced, which brings negative effects to subsequent SFC requests as well as network operators. In this paper, we investigate the network SFC migration problem in the core cloud under the premise of considering the migration cost and the balance of physical resource distribution. We first model the SFC migration problem as an integer linear program and propose an aggressive migration strategy that can effectively reduce the imbalance of physical resource distribution. Then, we employ two state-of-the-art heuristics to allocate resources for subsequent SFC requests. The simulation results show that migrating SFC requests in the initial service queue can bring favorable feedback to subsequent requests as well as network operators. Compared to the conservative migration strategy, our proposed migration strategy can mitigate the imbalance of physical resource distribution more effectively, and thus the acceptance ratio of subsequent SFC requests, physical resources utilization, and the long-term profit of network operators can be further improved.

关键词： network softwarization network function virtualization service function chain migration and resource allocation

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Virtual network function Placement Model Considering Both Availability and Probabilistic Protection for Service Delay

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IEICE TRANSACTIONS ON COMMUNICATIONS 2023年第10期E106B卷 891-902页

作者： Horimoto, Shinya Oki, Eiji Kyoto Univ Kyoto 6068501 Japan

This paper proposes a virtual network function (VNF) placement model considering both availability and probabilistic protection for the service delay to minimize the service deployment cost. Both availability and service delay are key requirements of services;a service provider handles the VNF placement problem with the goal of minimizing the service deployment cost while meeting these and other requirements. The previous works do not consider the delay of each route which the service can take when considering both availability and delay in the VNF placement problem;only the maximum delay was considered. We introduce probabilistic protection for service delay to minimize the service deployment cost with availability. The proposed model considers that the probability that the service delay, which consists of networking delay between hosts and processing delay in each VNF, exceeds its threshold is constrained within a given value;it also considers that the availability is constrained within a given value. We develop a two-stage heuristic algorithm to solve the VNF placement problem;it decides primary VNF placement by solving mixed-integer second-order cone programming in the first stage and backup VNF placement in the second stage. We observe that the proposed model reduces the service deployment cost compared to a baseline that considers the maximum delay by up to 12%, and that it obtains a feasible solution while the baseline does not in some examined situations.

关键词： network function virtualization virtual network function placement service delay availability

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A Wrapper Feature Selection Based Hybrid Deep Learning Model for DDoS Detection in a network with NFV Behaviors

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WIRELESS PERSONAL COMMUNICATIONS 2023年第1期133卷 481-506页

作者： Tikhe, Gajanan Nanaji Patheja, Pushpinder Singh VIT Bhopal Univ SCSE Bhopal Madhya Pradesh India

network function virtualization (NFV) is a rapidly growing technology that permits network operators to issue their virtualized network functions (VNFs) with cheaper commodity servers. There are various VNFs, namely firewalls, switches, and virtual routers. However, detecting denial of service (DDoS) attacked VNFs is challenging as VNF behaviors are complicated and dynamic due to network traffic in the cloud. Therefore, the proposed work implemented an intrusion detection system (IDS) to detect DDoS attacks in the network. The proposed IDS is named a wrapper feature selection-based hybrid deep learning model (WF-HDL). The DDoS detection model undergoes three stages: pre-processing, feature selection, and detection. The pre-processing is achieved by the z-score normalization technique, followed by a wrapper-based feature selection achieved using the Pelican optimization algorithm (POA). Finally, the DDoS attacks are detected using deep auto-encoder-convolutional gated recurrent unit (DAE-CGRU). The proposed model detected the network's normal and attacked VNF behaviors more accurately. It can train different kinds of VNF behaviour models. In the proposed work, two VNF models, a virtual firewall and a virtual router are trained using a CIC-DDoS2019 dataset. The proposed attack detection model achieves high accuracy at 99.69%, precision at 99.03%, recall at 99.07%, f1-score at 99.05%, and receiver operating characteristic curve (ROC curve) at 99.85%.

关键词： network function virtualization Wrapper feature selection Hybrid deep learning Virtualized network functions Denial of service

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