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Energy-Efficient Placement of Virtual network functions in a Wireless Mesh network

IEEE ACCESS 2024年 12卷 64807-64822页

作者： Tchinda, Auberlin Paguem Shala, Besfort Lehmann, Armin Ghita, Bogdan Walker, David Trick, Ulrich Frankfurt Univ Appl Sci Res Grp Telecommun Networks D-60318 Frankfurt Germany Univ Plymouth Ctr Secur Commun & Network Res Plymouth PL4 8AA Devon England

In the context of service provisioning, the integration of network functions virtualization (NFV) enhances the flexibility, scalability, and programmability of telecommunication networks. However, this integration introduces challenges, particularly in optimizing the placement of Virtualized network functions (VNFs) within the NFV Infrastructure (NFVI). Existing studies have predominantly focused on well-connected, mains-powered ecosystems like datacentres and cloud networks. In contrast, the aim of this paper is to identify a solution that distributes and deploys a Wireless Mesh network (WMN) as the backbone for a disaster management communication and service infrastructure. Given the mobility of mesh routers in such scenarios, these devices are often battery-powered. Consequently, the placement of VNFs directly impacts the energy consumption in the network and, subsequently, its lifetime. The proposed solution for the energy-efficient placement of VNF is formulated as a multi-objective optimization problem. This context introduces different approaches and proposes a heuristic algorithm to optimize the placement of VNFs. The evaluation results indicate that the proposed algorithm outperforms prior alternatives in various scenarios. Notably, it surpasses established methods like the Nondominated Sorting Genetic Algorithm II (NSGA-II), commonly used to solve similar problems. This research signifies a significant advancement in addressing the specific challenges associated with NFV integration in wireless mesh networks, particularly in disaster management contexts.

关键词： Energy consumption Disaster management disaster network network function virtualization wireless mesh network

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Differentially Private and Truthful Reverse Auction With Dynamic Resource Provisioning for VNFI Procurement in NFV Markets

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IEEE TRANSACTIONS ON CLOUD COMPUTING 2025年第1期13卷 259-272页

作者： Wang, Xueyi Wang, Xingwei Wang, Zhitong Zeng, Rongfei Yu, Ruiyun He, Qiang Huang, Min Northeastern Univ Coll Software Shenyang 110819 Peoples R China Northeastern Univ Coll Comp Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ Coll Med & Biol Informat Engn Shenyang 110819 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

With the advent of network function virtualization (NFV), many users resort to network service provisioning through virtual network function instances (VNFIs) run on the standard physical server in clouds. Following this trend, NFV markets are emerging, which allow a user to procure VNFIs from cloud service providers (CSPs). In such procurement process, it is a significant challenge to ensure differential privacy and truthfulness while explicitly considering dynamic resource provisioning, location sensitiveness and budget of each VNFI. As such, we design a differentially private and truthful reverse auction with dynamic resource provisioning (PTRA-DRP) to resolve the VNFI procurement (VNFIP) problem. To allow dynamic resource provisioning, PTRA-DRP enables CSPs to submit a set of bids and accept as many as possible, and decides the provisioning VNFIs based on the auction outcomes. To be specific, we first devise a greedy heuristic approach to select the set of the winning bids in a differentially privacy-preserving manner. Next, we design a pricing strategy to compute the charges of CSPs, aiming to guarantee truthfulness. Strict theoretical analysis proves that PTRA-DRP can ensure differential privacy, truthfulness, individual rationality, computational efficiency and approximate social cost minimization. Extensive simulations also demonstrate the effectiveness and efficiency of PTRA-DRP.

关键词： Costs Cloud computing Procurement Privacy Dynamic scheduling Vectors Computational modeling virtualization Minimization Differential privacy network function virtualization virtual network function reverse auction differential privacy truthfulness

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Security-Aware Service function Chaining and Embedding With Asymmetric Dedicated Protection

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IEEE ACCESS 2024年 12卷 53944-53957页

作者： Wang, Ben Li, Jun Cao, Shaohua Guler, Evrim Zheng, Danyang Soochow Univ Sch Elect & Informat Engn Suzhou 215021 Peoples R China China Univ Petr East China Coll Comp Sci & Technol Qingdao 266580 Peoples R China Bartin Univ Dept Comp Engn TR-74100 Bartin Turkiye Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 611756 Peoples R China

In the 5G and beyond 5G networks, achieving security-aware data transmission needs to convert clients ' requests into a service function chain (SFC), each service function (SF) providing a certain security guarantee. With diverse configuration techniques, an SF may own multiple versions, each version providing various security guarantees with diverse costs. It should be notice that, as the recent software failures have caused severe financial loss, great attentions from both academia and industry have been put onto the SFC reliability. In the literature, existing works have solely investigated the following two fields: 1) how to deploy a security-aware SFC, and 2) how to protect a traditional SFC. Simply applying these techniques to dealing with the problem of security-aware SFC protection might not be efficient as the backup and primary SFCs may not be identical for security-aware SFCs. Therefore, how to jointly take these fields into account is challenging and remains open. To tackle the above problem, this paper studies how to construct and embed a security-aware SFC with asymmetric dedicated protection. We mathematically define this problem and name it security-aware service function chaining, embedding, and protection with multi-versioned SFs (SFCEP-MF) with the objective of cost optimization. Next, to optimize the SFCEP-MF problem, we construct an efficient algorithm, called augmenting-path with primary-first disjoint SFP identifier (APPF-DSI). Extensive simulation results show that the APPF-DSI algorithm outperforms the benchmark approaches that are directly extended from the state-of-the-art.

关键词： network function virtualization Service computing Reliability engineering service function with multi-versions service function chaining and embedding security reliability

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An Efficient Benders Decomposition Approach for Optimal Large-Scale network Slicing

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 4935-4949页

作者： Chen, Wei-Kun Wu, Zheyu Zhang, Rui-Jin Liu, Ya-Feng Dai, Yu-Hong Luo, Zhi-Quan Beijing Inst Technol Sch Math & Stat Beijing Key Lab MCAACI Beijing 100081 Peoples R China Chinese Acad Sci Inst Computat Math & Sci Engn Comp Acad Math & Syst Sci State Key Lab Sci & Engn Comp Beijing 100190 Peoples R China Chinese Univ Hong Kong Shenzhen Res Inst Big Data Shenzhen 518172 Peoples R China

This paper considers the network slicing (NS) problem which attempts to map multiple customized virtual network requests to a common shared network infrastructure and allocate network resources to meet diverse service requirements. This paper proposes an efficient customized Benders decomposition algorithm for globally solving the large-scale NP-hard NS problem. The proposed algorithm decomposes the hard NS problem into two relatively easy function placement (FP) and traffic routing (TR) subproblems and iteratively solves them enabling the information feedback between each other, which makes it particularly suitable to solve large-scale problems. Specifically, the FP subproblem is to place service functions into cloud nodes in the network, and solving it can return a function placement strategy based on which the TR subproblem is defined;and the TR subproblem is to find paths connecting two nodes hosting two adjacent functions in the network, and solving it can either verify that the solution of the FP subproblem is an optimal solution of the original problem, or return a valid inequality to the FP subproblem that cuts off the current infeasible solution. The proposed algorithm is guaranteed to find the globally optimal solution of the NS problem. By taking the special structure of the NS problem into consideration, we successfully develop two families of valid inequalities that render the proposed algorithm converge much more quickly and thus much more efficient. Numerical results demonstrate that the proposed valid inequalities effectively accelerate the convergence of the decomposition algorithm, and the proposed algorithm significantly outperforms the existing algorithms in terms of both solution efficiency and quality.

关键词： Signal processing algorithms Heuristic algorithms Routing Cloud computing Classification algorithms network slicing Convergence Standards Resource management network function virtualization Benders decomposition Farkas' Lemma network slicing resource allocation valid inequality

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Digital twin-assisted service function chaining in multi-domain computing power networks with multi-agent reinforcement learning

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2024年 158卷 294-307页

作者： Wang, Kan Yuan, Peng Jan, Mian Ahmad Khan, Fazlullah Gadekallu, Thippa Reddy Kumari, Saru Pan, Hao Liu, Lei Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Univ Sharjah Dept Comp Sci Coll Comp & Informat Sharjah 27272 U Arab Emirates Abdul Wali Khan Univ Mardan Dept Comp Sci Mardan Pakistan Lovely Profess Univ Div Res & Dev Phagwara India Chitkara Univ Ctr Res Impact & Outcome Rajpura Punjab India Chaudhary Charan Singh Univ Dept Math Meerut Uttar Pradesh India Xian Univ Posts & Telecommun Sch Commun & Informat Engn Xian 710100 Peoples R China Shenyang Agr Univ Coll Informat & Elect Engn Shenyang 110866 Peoples R China

The emerging computing power network (CPN) is believed to undergo the paradigm reformation of network function virtualization (NFV) and service function chaining (SFC). It is prerequisite to explore the performance upper bound of NFV-assisted CPN before truly deploying the NFV and SFC technologies onto physical networks. Inspired by the application of digital twin (DT) in the industry and due to its advantage in synchronizing physical objects with their virtual replicas, we propose to use the DT to assist the SFC deployment in the multi-domain CPN, with the aid of multi-agent deep deterministic policy gradient (MADDPG) framework. First, we build a dynamic SFC mapping problem in the virtual twin network layer, by modeling the computing power, link bandwidth, delay performance and the VNF ordering as DT objects and constraints, to jointly optimize the energy consumption, end-to-end delay and the VNF re-deploying cost. Then, the prioritized experience replay and re-parameterization trick-empowered centralized training and decentralized execution MADDPG framework is utilized to learn the SFC deployment, by taking each domain controller as one agent. Finally, numerical experiments are carried out to validate the effectiveness of MADDPG in the cross-domain SFC deployment. For performance verification, the deployment success rate, number of crossed domains, energy consumption, end-to-end latency and load balancing degree are all taken as metrics, to show the performance of MADDPG compared to other learning frameworks.

关键词： Digital twin Service function chain Multi-domain computing power network MADDPG network function virtualization

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Architecting Autonomous network Management and Control via Intent-Driven Decoupled network

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IEEE network 2024年第6期38卷 361-369页

作者： Li, Tong Yang, Chungang Song, Yanbo Cai, Lin Zheng, Ruirong Liu, Xianglin Ji, Zeyang Liu, Shuhan Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Univ Victoria Dept Elect & Comp Engn Victoria BC V8P 5C2 Canada

The current coupled network management architecture and control protocols restrict the network flexibility and scalability. In order to achieve autonomous network management and control, it is necessary to decouple network functions across various layers, including application, control, data, and management layers. Although there exists emerging decouple concepts like Intent-driven network, Software-defined network, Kubernetes, and network function virtualization, there is a lack of a generic network management architecture to achieve a full-lifecycle autonomous network management. To fill the gap, we design a generic decoupled network management architecture and interface that achieves higher flexibility and scalability by exploring and exploiting potentials of decoupled network management components. Given the decoupled network architecture, we present an intent-driven autonomous network management and control scheme, known as SAI, considering the network state, action, and potential intent. Finally, we build the intent-driven data network management prototype to prove the concept and evaluate the performance of the presented SAI management and control scheme.

关键词： Protocols Autonomous networks Task analysis Hardware Scalability network function virtualization Prototypes Decoupled network intent-driven network network management

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SafeDRL: Dynamic Microservice Provisioning With Reliability and Latency Guarantees in Edge Environments

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IEEE TRANSACTIONS ON COMPUTERS 2024年第1期73卷 235-248页

作者： Zeng, Yue Qu, Zhihao Guo, Song Ye, Baoliu Zhang, Jie Li, Jing Tang, Bin Nanjing Univ Dept Comp Sci & Technol State Key Lab Novel Software Technol Nanjing 210023 Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China Hohai Univ Minist Water Resources Key Lab Water Big Data Technol Nanjing 211100 Peoples R China Hohai Univ Coll Comp Sci & Software Engn Nanjing 211100 Peoples R China

As a key technology of 5G, network function virtualization enables each monolithic service to be divided into microservices, facilitating their deployment and management in edge environments. One of the most critical issues in 5G is how to support dynamically arriving mission-critical services with low-latency and high-reliability requirements in distributed edge environments. However, most existing works focus on how to provide reliable services without considering latency, and their heuristics struggle to cope with high-dimensional constraints and complex environments with heterogeneous infrastructure and services. In this paper, we propose a SafeDRL algorithm to resource-efficiently support these dynamically arriving services while meeting their reliability and latency requirements. Specifically, we first formulate the problem as an integer nonlinear programming and prove its NP-hardness. To tackle this problem, our SafeDRL algorithm captures delayed rewards in dynamic environments by reinforcement learning, and corrects constraint violations with high-quality feasible solutions based on expert intervention, and prunes unnecessary backup instances for optimality. The algorithm is proved to have a bounded approximation ratio in general cases. Extensive trace-driven simulations show that, compared with the state-of-the-art solution, SafeDRL can save resource costs by up to 49.32% and improve the service acceptance ratio by up to 55% with acceptable execution time.

关键词： 5G deep reinforcement learning network function virtualization edge computing mission-critical services

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Composing eBPF Programs Made Easy With HIKe and eCLAT

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IEEE TRANSACTIONS ON network AND SERVICE MANAGEMENT 2024年第2期21卷 1359-1371页

作者： Mayer, Andrea Bracciale, Lorenzo Lungaroni, Paolo Sidoretti, Giulio Salsano, Stefano Bianchi, Giuseppe Loreti, Pierpaolo Univ Roma Tor Vergata Elect Engn Dept I-00133 Rome Italy Consorzio Nazl Interuniv Telecomunicaz CNIT I-43124 Parma Italy

With the rise of the network Softwarization era, eBPF has become a hot technology for efficient packet processing on commodity hardware. However the development of custom eBPF solutions is a challenging process that requires highly qualified human resources. Indeed, in eBPF, it is difficult to devise truly modular applications since the development model does not favour the use of pre-compiled functions and libraries. In addition, for safety purposes, each eBFF program must pass a binary code verifier of the Linux kernel, which may increase the difficulty of the development process. To overcome such difficulties and enable a new development model, in this paper we propose the eCLAT framework with the goal to lower the learning curve of engineers by re-using eBPF code in a programmable way. eCLAT offers a high level programming abstraction to eBPF based network programmability, allowing a developer to create custom application logic with no need of understanding the complex details of regular eBPF programming. A developer can write eCLAT scripts in a python-like language to compose eBPF programs. To support such abstraction at the eBPF level, we created an eBPF framework called HIKe which brings code reuse and modularity in eBPF. The eCLAT/HIKe solution does not require any kernel modification. The new development model is tested through two concrete examples and compared with other proposed frameworks in the eBPF world.

关键词： Kernel Codes Programming Program processors Tail Linux Libraries Computer networks computer network management network function virtualization

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An Autonomous Deployment Mechanism for AI Security Services

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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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