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NFLCS: An Service function Chain Path Optimization Strategy Based on network-functional Layout Clustering

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第8期72卷 10813-10825页

作者： Duan, Youxiang Lu, Yuxi Shen, Shigen Yu, Shui Zhang, Peiying Zhang, Wei Igorevich, Kostromitin Konstantin China Univ Petr East China Qingdao Inst Software Coll Comp Sci & Technol Qingdao 266580 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Huzhou Univ Sch Informat Engn Huzhou 313000 Peoples R China Univ Technol Sydney Sch Comp Sci Ultimo NSW 2007 Australia Qilu Univ Technol Shandong Acad Sci Shandong Comp Sci Ctr Natl Super Comp Ctr JinanShandong Prov Key Lab Co Jinan 250013 Peoples R China South Ural State Univ Dept Phys Nanoscale Syst Chelyabinsk 454080 Russia

Under the background of 5G and 6G super bandwidth and ultra-low delay, Software Defined networking and network function virtualization (SDN & NFV) makes the communication technology develop rapidly. Service function Chain (SFC) selection is a crucial aspect of SDN & NFV, which faces many challenges of poor algorithm performance, high latency, and low profit. Moreover, most of the existing studies are stuck in optimizing for a single metric, which leads to a poor generalization of the proposed path optimization method. In this paper, we propose an SFC path optimization strategy based on the clustering of network functional layouts to address the urgent problem. First, we design a topological network identification algorithm, model the SFC path optimization problem as an attention problem and use a multi-headed attention mechanism to optimize different kinds of SFC layouts in a targeted manner. Among them, the multi-headed attention mechanism achieves the effective capture of multiple features in the SFC path selection problem compared with the traditional strategy. The experimental results demonstrate the effectiveness of network-functional Layout Clustering Strategy (NFLCS) in optimizing SFC paths and show the ability to optimize the service function chain paths well in future complex network situations.

关键词： Optimization Layout Quality of service Load modeling Clustering algorithms Service function chaining Data structures Service function chains attention mechanism network function virtualization path optimization

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DCDPI: Dynamic and Continuous Deep Packet Inspection in Secure Outsourced Middleboxes

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IEEE TRANSACTIONS ON CLOUD COMPUTING 2023年第4期11卷 3510-3524页

作者： Deng, Minjun Zhang, Kai Wu, Pengfei Wen, Mi Ning, Jianting Shanghai Univ Elect Power Coll Comp Sci & Technol Shanghai 201399 Peoples R China Natl Univ Singapore Sch Comp Singapore 119077 Singapore Fujian Normal Univ Coll Comp & Cyber Secur Fujian Prov Key Lab Network Secur & Cryptol Fuzhou 350117 Peoples R China Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur Beijing 100093 Peoples R China

Secure outsourced middleboxes are deployed in network function virtualization services that detect malicious activities on communications, which provides privacy-preserving deep packet inspection (DPI) over encrypted traffic. To boost filtering efficiency of packets, the two-layer middlebox architecture has been adopted in recent DPI systems. Nevertheless, state-of-the-art solutions based on two-layer architecture mainly suffer from two limitations: i) cannot support dynamic rule addition;ii) failed to inspect discontinuous token for rule matching. To address these limitations, this work proposes an efficient, dynamic and continuous DPI (DCDPI) system in secure outsourced middleboxes. To achieve dynamic rule addition with forward privacy, we refine a data structure called virtual binary tree (VBTree) and further introduce a variant of VBTree for DCDPI, termed VBTree+. VBTree+ supports two new desirable features: i) taking the rule action information into consideration;ii) achieving both rule identifier and rule action hiding. By introducing a token continuity check mechanism, DCDPI can effectively identify discontinuous tokens and categorize continuous tokens into one group. The extensive experiment over the real dataset and rule set confirms the practicality and efficiency of DCDPI. Compared to state-of-the-art works with same setting, DCDPI is 18%similar to 110% more efficient for a connection establishment between gateway/client and server.

关键词： Middleboxes Inspection Payloads Logic gates Servers Privacy Cloud computing Deep packet inspection encrypted traffic inspection middlebox network function virtualization network privacy

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On Jointly Optimizing Partial Offloading and SFC Mapping: A Cooperative Dual-Agent Deep Reinforcement Learning Approach

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2023年第8期34卷 2479-2497页

作者： Wang, Xinhan Xing, Huanlai Song, Fuhong Luo, Shouxi Dai, Penglin Zhao, Bowen Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 611756 Peoples R China Minist Educ Engn Res Ctr Sustainable Urban Intelli gent Transp Chengdu 611756 Peoples R China

Multi-access edge computing (MEC) and network function virtualization (NFV) are promising technologies to support emerging IoT applications, especially those computation-intensive. In NFV-enabled MEC environment, service function chain (SFC), i.e., a set of ordered virtual network functions (VNFs), can be mapped on MEC servers. Mobile devices (MDs) can offload computation-intensive applications, which can be represented by SFCs, fully or partially to MEC servers for remote execution. This article studies the partial offloading and SFC mapping joint optimization (POSMJO) problem in an NFV-enabled MEC system, where the data from an incoming task is partitioned into two parts, with one part executed locally and the other offloaded to the edge infrastructure for execution. These two parts are independent of each other, but both need to be processed by the same SFC. The objective is to minimize the average cost in the long term which is a combination of execution delay, MD's energy consumption, and usage charge for edge computing. This problem consists of two closely related decision-making steps, namely task partition and VNF placement, which is highly complex and quite challenging. To address this, we propose a cooperative dual-agent deep reinforcement learning (CDADRL) algorithm, where two agents interact with each other. Simulation results show that the proposed algorithm outperforms three combinations of deep reinforcement learning algorithms with respect to cumulative reward and it overweighs a number of baseline algorithms in terms of execution delay, energy consumption, and usage charge.

关键词： Deep reinforcement learning multi-access edge computing network function virtualization partial offloading SFC mapping

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Guest Editorial: The Interplay of Digital Twin and 6G Wireless networks

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IEEE COMMUNICATIONS MAGAZINE 2023年第11期61卷 70-71页

作者： Bariah, Lina Debbah, Merouane Sari, Hikmet Bastug, Ejder Khalifa Univ Technol Innovat Inst Abu Dhabi U Arab Emirates Western Univ London ON Canada Khalifa Univ Sci & Technol Abu Dhabi Abu Dhabi U Arab Emirates Nanjing Univ Posts & Telecommun NJUPT Nanjing Peoples R China Nokia Bell Labs Murray Hill NJ USA

Embracing the principles of network virtualization and digitization as a main pillar in the upcoming wireless generation has motivated the emergence of the digital twin (DT) paradigm as an enabler for 6G networks. The key concept of the DT technology is to create a digital replica of the physical components and functions of wireless networks, with the aim to realize ultra-reliable, low latency communication, while ensuring high energy-efficiency in resource-constrained networks. It is envisaged that DT will be the seed for the efficient deployment of intelligent, fully automated, zero-touch networks, in which artificial intelligence (AI) algorithms will be leveraged at the digital realm to enable virtualized network monitoring and operation. Despite its promising advantages, to reap the full potential of the DT technology in 6G networks, the cyber twin is envisaged to leverage AI algorithms, with novel data-driven paradigms, high performance computing, optimization theory, matching theory, as well as efficient cyber-physical interaction schemes, among others, to realize the necessary adaptation/reconfiguration at the physical twin with an imperceptible time-lag. In order to achieve the needed quality-of-service (QoS) for the successful implementation of a high-fidelity DT paradigm in 6G, a new level of stringent requirements pertaining to connectivity, reliability, latency, and data rate are imposed on future wireless generations. This Feature Topic (FT), which has attracted a large number of high-quality articles, aims to be a stepping-stone on advancing the research on DT for 6G, and to promote the development of high-fidelity Telecom Digital Twin.

关键词： Special issues and sections 6G mobile communication Digital twins Wireless networks network function virtualization

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Multivariate outlier filtering for A-NFVLearn: an advanced deep VNF resource usage forecasting technique

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JOURNAL OF SUPERCOMPUTING 2023年第14期79卷 16206-16232页

作者： St-Onge, Cedric Kara, Nadjia Edstrom, Claes Univ Quebec Ecole Technol Super Dept Software & Informat Technol Engn Montreal PQ Canada Ericsson Canada Montreal PQ Canada

Virtual network function Resource Adaptation (VNF-RA) aims at adequately adapting network function virtualization Infrastructure (NFVI) resources according to the geographical fluctuation of user demand by maximizing the quality of service (QoS) of the offered services and the energy consumption of the NFVI while limiting the risks of Service Level Agreement (SLA) breaches, the CAPEX and OPEX of the cloud operators and their customers. Virtual network function (VNF) resource usage forecasting leads, therefore, a key role in enabling proactive resource adaptation in dynamic network function virtualization (NFV) environments whose resource demand constantly changes. In parallel, Long Short-Term Memory (LSTM)-based prediction has garnered huge interest in the research community. Several research teams have proposed different VNF resource usage prediction algorithms based on this machine learning technique. However, current LSTM-based VNF resource usage forecasting techniques lack the flexibility to take several resource attributes of different scales, over many time steps, in order to predict several other resource attributes over many time steps from a Service function Chain (SFC). In this paper, we push the state of the art forward by presenting A-NFVLearn, a flexible multivariate, LSTM-based model with an attention mechanism which uses different attributes of resource load history (CPU, memory, I/O bandwidth) from various VNFs of an SFC to forecast future load of multiple resources of a VNF. Next, we propose a multivariate outlier filtering scheme at pre-processing based on Adjusted Outlyingness (AO), which improves the training time performance of LSTM-based models without impacting prediction accuracy.

关键词： network function virtualization Resource usage prediction LSTM Attention mechanism Outlier filtering Adjusted Outlyingness

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Service Deployment Model with Virtual network function Resizing Based on Per-Flow Priority

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IEICE Transactions on Communications 2023年第9期E106-B卷 786-797页

作者： Akahoshi, Keigo Oki, Eiji Kyoto University Kyoto-shi606-8501 Japan

This paper investigates a service deployment model for network function virtualization which handles per-flow priority to minimize the deployment cost. Service providers need to implement network services each of which consists of one or more virtual network functions (VNFs) with satisfying requirements of service delays. In our previous work, we studied the service deployment model with per-host priority;flows belonging to the same service, for the same VNF, and handled on the same host have the same priority. We formulated the model as an optimization problem, and developed a heuristic algorithm named FlexSize to solve it in practical time. In this paper, we address per-flow priority, in which flows of the same service, VNF, and host have different priorities. In addition, we expand FlexSize to handle per-flow priority. We evaluate per-flow and per-host priorities, and the numerical results show that per-flow priority reduces deployment cost compared with per-host priority. Copyright © 2023 The Institute of Electronics, Information and Communication Engineers.

关键词： network function virtualization

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SCHE2MA: Scalable, Energy-Aware, Multidomain Orchestration for Beyond-5G URLLC Services

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第7期24卷 7653-7663页

作者： Dalgkitsis, Anestis Garrido, Luis A. Rezazadeh, Farhad Chergui, Hatim Ramantas, Kostas Vardakas, John S. Verikoukis, Christos Iquadrat Informat Barcelona 08006 Spain Ctr Tecnol Telecomunicac Catalunya CTTC CERCA Barcelona 08860 Spain Univ Patras Dept Comp Engn & Informat CEID Univ Campus Rion 26504 Greece ATHENA ISI Patras Sci Pk Bldg Patras Rion 26504 Greece

The evolution of Software-Defined networking (SDN) and network function virtualization (NFV) in the telecommunications industry have intensified the issues of network management at large scales. Dynamic service orchestration and adaptive resource allocation became a necessity for network operators to manage the rapid growth of users and data-intensive applications. The impact of network automation on energy consumption and overall operating costs is often overlooked. Guaranteeing strict performance constraints of Ultra-Reliable Low Latency Communication (URLLC) services while enhancing energy efficiency is one of the major critical problems of future communication networks, given the urgency to reduce carbon emissions and energy consumption. In this work, we study the problem of zero-touch Service function Chain (SFC) orchestration for multi-domain networks, targeting the latency reduction of URLLC services while improving energy efficiency for beyond-5G networks. Specifically, we propose SCHE2MA, a Service CHain Energy-Efficient Management framework based on distributed Reinforcement Learning (RL), that can intelligently deploy SFCs with shared VNFs per se into a multi-domain network. Finally, we evaluate SCHE2MA through model validation and simulation while demonstrating its ability to jointly reduce average service latency by 103.4% and energy consumption by 17.1% compared to a centralized RL solution.

关键词： Energy consumption Ultra reliable low latency communication Heuristic algorithms Scalability Indium phosphide III-V semiconductor materials Costs Zero-touch orchestration network function virtualization service function chain placement distributed reinforcement learning energy efficiency

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AI-Enabled network-functions virtualization and Software-Defined Architectures for Customized Statistical QoS Over 6G Massive MIMO Mobile Wireless networks

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IEEE network 2023年第2期37卷 30-37页

作者： Zhang, Xi Zhu, Qixuan Texas A&M Univ College Stn TX 77843 USA

With the rapid deployments of the fifth generation (5G) mobile wireless networks, the shift from the 5G to the sixth generation (6G) mobile wireless networks has attracted tremendous research attention around the world. Featuring with the explosively increasing multimedia-traffics with very diverse services requirements, the 6G mobile wireless networks need to provide the customized services with heterogeneous types of quality of service (QoS) guarantees. However, how to efficiently support these customized services with heterogeneous QoS provisioning for 6G wireless networks has imposed many new challenges not encountered before. To conquer these difficulties, in this article we propose the artificial intelligence (AI)-enabled integration of massive multiple-input-multiple-output (massive-MIMO) techniques with network functions virtualization (NFV) and software-defined network (SDN) architectures to support the customized services over the 6G mobile wireless networks. Specifically, we develop the AI-enabled network architectural schemes which efficiently integrate three 6G-candidate techniques - massive-MIMO, NFV, and SDN - to significantly improve key performances of heterogeneous statistical QoS provisioning in terms of effective capacity. We apply the massive MIMO transmission to substantially improve the channel throughput. Our NFV-based schemes abstract and slice the physical infrastructure and wireless resources in network data plane into several virtualized networks and obtain the optimal service delivery path with the maximum effective capacity among virtualized networks. Also, we develop a set of AI-enabled techniques including multiagent AI-plane architectures, edge-Al frameworks, and federated learning mechanisms for efficiently implementing our developed massive-MIMO-NFV-SDN integrated schemes. Collaborating with our developed platform and techniques, our multi-agent AI-plane based SDN controller coordinates the network nodes and resources allocations f

关键词： 6G mobile communication 5G mobile communication Wireless networks Quality of service Massive MIMO Throughput network function virtualization Artificial intelligence Software defined networking

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A NFV Technology with Expanded Wireless Communication Emulation function 25

A NFV Technology with Expanded Wireless Communication Emulat...

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IEEE Wireless Communications and networking Conference (IEEE WCNC)

作者： Yuan, Jiabin Chen, Ming Zhu, Kun Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China Collaborat Innovat Ctr Novel Software Technol & I Nanjing Peoples R China

ISBN: (纸本)9798350303582;9798350303599

Emerging wireless technologies such as 6G and satellite communications need thorough testing and validation before deployment. While NFV (network function virtualization) effectively supports network analysis, evaluation, and testing, it lacks the ability to facilitate research in wireless communication and the convergence of wired and wireless networks. In this paper, we aim to enhance the capabilities of NFV by integrating the ns-3 network simulator with the NFV system. Moreover, we utilize the rich wireless modules in ns-3 to expand wireless communication emulation functions for NFV. Firstly, we discuss the operational principles of integrating the ns-3 emulated wireless network with the NFV network, which implemented by utilizing the communication interface between ns-3 and Linux. Next, we discuss the technical details of expanding the wireless communication emulation function within the NFV system. Furthermore, we provide a case study on the convergence between the NFV-based wired network and the ns-3 emulated MANET (Mobile Ad-hoc network). The experimental results demonstrate that the proposed method has extended the availability of NFV in wireless communication emulation.

关键词： network function virtualization ns-3 wireless emulation wired-wireless converged network

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Optimized Placement of Service function Chains in Edge Cloud with LSTM and ILP

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SN Computer Science 2025年第1期6卷 1-14页

作者： Vishesh, P. Poorva, K. Akshata, H. Ritwik, B. Narayan, D.G. Savanur, Sadaf School of Computer Science and Engineering KLE Technological University Karnataka Hubballi India

Edge computing has emerged as a transformative approach for reducing latency and enhancing network performance by placing computing resources closer to data sources and end users via edge nodes. This approach addresses the delays inherent in traditional cloud computing architectures, where data must travel long distances to centralized data centers. Within this architecture, network function virtualization (NFV) enables network service deployment by executing Virtual network functions (VNFs) on virtualized infrastructure, establishing Service function Chains (SFCs) to manage network traffic efficiently. In this work, we address the challenge of optimizing SFC placement that combines Long Short-Term Memory (LSTM) networks and Integer Linear Programming (ILP). The LSTM model predicts dynamic traffic patterns and resource demands of edge nodes, allowing for a more accurate anticipation of future network conditions. These predictions are then used to formulate an ILP-based optimization model that determines the optimal placement of SFCs to minimize deployment costs while considering constraints like delay, memory, CPU, and bandwidth. We implemented and evaluated the proposed approach in a real-time KubeEdge cluster consisting of one master node and eight edge nodes. The results demonstrate that the LSTM-ILP scheduler significantly improves SFC placement efficiency by effectively identifying underutilized nodes, leading to reduced response times and optimized resource usage. The LSTM-ILP scheduler improves response time by 12.5% compared to the default Kubernetes scheduler when handling 50,000 requests. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Edge cloud Integer Linear Programming KubeEdge Long Short-Term Memory network function virtualization Service function Chains

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