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Intelligent-Slicing: An AI-Assisted network Slicing Framework for 5G-and-Beyond networks

IEEE TRANSACTIONS ON network AND SERVICE MANAGEMENT 2023年第2期20卷 1024-1039页

作者： Awad Abdellatif, Alaa Abo-Eleneen, Amr Mohamed, Amr Erbad, Aiman Navkar, Nikhil V. Guizani, Mohsen Qatar Univ Coll Engn Doha Qatar Hamad Bin Khalifa Univ Coll Sci & Engn Doha Qatar Hamad Med Corp Dept Surg Doha Qatar Mohamed Bin Zayed Univ Artificial Intelligence Machine Learning Dept Abu Dhabi U Arab Emirates

5G-and-beyond networks are designed to fulfill the communication and computation requirements of various industries, which requires not only transporting the data, but also processing them to meet/address diverse key performance indicators (KPIs). network function virtualization (NFV) has emerged to enable this vision by: (i) collecting the requirements of diverse services, using graphs of Virtual network functions (VNFs);and (ii) mapping these requirements into network management decisions. Because of the latter, we need to efficiently allocate computing and network resources to support the desired services, and because of the former such decisions must be jointly optimized considering all KPIs associated with supported services. Thus, this paper proposes an optimized, intelligent network slicing framework to maintain a high performance of network operation by supporting diverse and heterogeneous services, while meeting new KPIs, e.g., reliability, energy consumption, and data quality. Different from the existing works, which are mainly designed considering traditional metrics like throughput and latency, we present a novel methodology and resource allocation schemes that enable high-quality selection of radio points of access, VNF placement and data routing, as well as data compression ratios, from the end users to the cloud. Our results depict the efficiency of the proposed framework in enhancing the network performance when compared to baseline approaches that consider partial network view or fair resource allocation.

关键词： 6G network network function virtualization software-defined networking pervasive network intelligence AI for slicing

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Shared Backup Allocation Model of Middlebox Based on Workload-Dependent Failure Rate

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IEICE TRANSACTIONS ON COMMUNICATIONS 2023年第5期E106B卷 427-438页

作者： Zhang, Han He, Fujun Oki, Eiji Kyoto Univ Grad Sch Informat Kyoto 6068501 Japan Kyoto Univ Kyoto 6068501 Japan

With the network function virtualization technology, a middlebox can be deployed as software on commercial servers rather than on dedicated physical servers. A backup server is necessary to ensure the normal operation of the middlebox. The workload can affect the failure rate of backup server;the impact of workload-dependent failure rate on backup server allocation considering unavailability has not been extensively stud-ied. This paper proposes a shared backup allocation model of middlebox with consideration of the workload-dependent failure rate of backup server. Backup resources on a backup server can be assigned to multiple functions. We observe that a function has four possible states and analyze the state transitions within the system. Through the queuing approach, we compute the probability of each function being available or unavailable for a certain assignment, and obtain the unavailability of each function. The proposed model is designed to find an assignment that minimizes the maximum un-availability among functions. We develop a simulated annealing algorithm to solve this problem. We evaluate and compare the performances of pro-posed and baseline models under different experimental conditions. Based on the results, we observe that, compared to the baseline model, the pro-posed model reduces the maximum unavailability by an average of 29% in our examined cases.

关键词： load-dependent queuing theory network function virtualization backup resource allocation

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Backup Resource Allocation Model with Probabilistic Protection Considering Service Delay

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IEICE TRANSACTIONS ON COMMUNICATIONS 2023年第9期E106B卷 798-816页

作者： Horimoto, Shinya He, Fujun Oki, Eiji Kyoto Univ Grad Sch Informat Kyoto 6068501 Japan Kyoto Univ Kyoto 6068501 Japan

This paper proposes a backup resource allocation model for virtual network functions (VNFs) to minimize the total allocated computing capacity for backup with considering the service delay. If failures occur to primary hosts, the VNFs in failed hosts are recovered by backup hosts whose allocation is pre-determined. We introduce probabilistic protection, where the probability that the protection by a backup host fails is limited within a given value;it allows backup resource sharing to reduce the total allocated computing capacity. The previous work does not consider the service delay constraint in the backup resource allocation problem. 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. We introduce a basic algorithm to solve our formulated delay-constraint optimization problem. In a problem with the size that cannot be solved within an acceptable computation time limit by the basic algorithm, we develop a simulated annealing algorithm incorporating Yen's algorithm to handle the delay constraint heuristically. We observe that both algorithms in the proposed model reduce the total allocated computing capacity by up to 56.3% compared to a baseline;the simulated annealing algorithm can get feasible solutions in problems where the basic algorithm cannot.

关键词： backup resource allocation edge computing network function virtualization service delay

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Deep Multi-Agent Reinforcement Learning With Minimal Cross-Agent Communication for SFC Partitioning

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IEEE ACCESS 2023年 11卷 40384-40398页

作者： Pentelas, Angelos De Vleeschauwer, Danny Chang, Chia-Yu De Schepper, Koen Papadimitriou, Panagiotis Nokia Bell Labs B-2018 Antwerp Belgium Univ Macedonia Dept Appl Informat Thessaloniki 54636 Greece

network function virtualization (NFV) decouples network functions from the underlying specialized devices, enabling network processing with higher flexibility and resource efficiency. This promotes the use of virtual network functions (VNFs), which can be grouped to form a service function chain (SFC). A critical challenge in NFV is SFC partitioning (SFCP), which is mathematically expressed as a graph-to-graph mapping problem. Given its NP-hardness, SFCP is commonly solved by approximation methods. Yet, the relevant literature exhibits a gradual shift towards data-driven SFCP frameworks, such as (deep) reinforcement learning (RL). In this article, we initially identify crucial limitations of existing RL-based SFCP approaches. In particular, we argue that most of them stem from the centralized implementation of RL schemes. Therefore, we devise a cooperative deep multi-agent reinforcement learning (DMARL) scheme for decentralized SFCP, which fosters the efficient communication of neighboring agents. Our simulation results (i) demonstrate that DMARL outperforms a state-of-the-art centralized double deep $Q$ -learning algorithm, (ii) unfold the fundamental behaviors learned by the team of agents, (iii) highlight the importance of information exchange between agents, and (iv) showcase the implications stemming from various network topologies on the DMARL efficiency.

关键词： Topology Servers Resource management Virtual links network topology network function virtualization Multi-agent systems Reinforcement learning Multi-agent reinforcement learning network function virtualization self-learning orchestration

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Performance Analysis for Subgraph Isomorphism Based Embedding Service function Chains

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IEEE TRANSACTIONS ON network SCIENCE AND ENGINEERING 2023年第6期10卷 3099-3113页

作者： Zheng, Shuya Ren, Zhiyuan Cheng, Wen Chi Zhang, Hailin Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China

Embedding Service function Chains (SFCs) into the network represents scheduling network resources to provide user services. Embedding schemes are affected by many factors, such as the topology of network Service Requests (NSRs), the demands of NSRs on Virtual network functions (VNFs) computing power, and network link quality. However, most researches focus on improving the performance and algorithms of embedding SFCs, and the effect of the above factors has yet to be intensively studied. Studying the influence of the above factors can not only guide users to initiate more suitable NSRs but also have guiding significance for scheduling network resources. Due to the subgraph isomorphism that can quickly find all feasible solutions, it is applied to embedding SFCs to explore the influence of the above factors on embedding SFC. However, subgraph isomorphism in large-scale networks is inefficient due to its high computational complexity. We propose an adaptive adjustment strategy to prune the network to reduce the computational complexity of subgraph isomorphism. Simulation results show that the subgraph isomorphism can search all feasible embedding schemes quickly and observe the influence of the above factors on embedding schemes. Those factors significantly impact embedding SFCs, mainly reflected in scheme numbers and latency performance.

关键词： Multi-access edge computing network function virtualization service function chain subgraph isomorphism virtual network function

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Modeling and Analysis of Different Reconfiguration Strategies for Virtual network function Placement and Chaining with Service Classes Identification

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IEEE LATIN AMERICA TRANSACTIONS 2023年第3期21卷 475-482页

作者： Araujo, Samuel M. A. de Souza, Fernanda S. H. Mateus, Geraldo R. Fed Univ Minas Gerais Brazil Belo Horizonte MG Brazil Fed Univ Ouro Preto Brazil Ouro Preto Brazil

The Virtual network function Placement and Chaining problem (VNF-PC) is an important part of the network functions virtualization (NFV) based-technologies implementation. VNF-PC problem focuses on the allocation of customer demands on the Substrate network. Aiming to evaluate the impact of diverse modeling, various reconfiguration strategies based on implicit steps in solving the VNF-PC are proposed: resizing virtual network instances, re-routing chaining, and repositioning network functions (NF) instances on different servers. In addition, this work analyzes, compares, and discusses the advantages and disadvantages of each proposed reconfiguration strategy in an online scenario. Traditionally, VNF-PC solutions from literature process requests generated at random and do not take into account real-world demands. Complementing the analyses of reconfiguration strategies, works from the literature are surveyed to identify commonly used network Services (NS). Following that, these NS are classified into service classes, and used to generate realistic requests to be mapped in the experimental stage of the reconfiguration approaches. The experiments are conducted using realistic requests and a real-world network topology. An Integer Linear Programming model is used to process the requests. Simulations show that repositioning NF instances can generate up to asymptotic to 25% more profit than reconfiguring only the VNF instances, but the processing time increases by up to 99.99%. On the other hand, resizing virtual network instances and rerouting the chaining had no significant impact on runtime.

关键词： network function virtualization service function chaining reconfiguration classes of service

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Toward Cloud-Native VNFs: An ETSI NFV Management and Orchestration Standards Approach

IEEE Communications Standards Magazine

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IEEE Communications Standards Magazine 2024年第2期8卷 12-19页

作者： Aelken, Jorg Triay, Joan Chatras, Bruno De Nicolas, Arturo Martin Ericsson Sweden DOCOMO Communications Lab. Europe Germany Bruno Chatras Is with Orange France

network functions virtualization (NFV) is becoming the norm for deploying network functions and managing their lifecycles. While the vast majority of NFV deployments so far have been focusing on hosting virtualized network functions in virtual machines managed by hypervisors, container-based virtualization is gaining momentum in the NFV ecosystem and has become the technology of choice for virtualizing 5G networks. This article provides an overview of the evolution of ETSI NFV Management and Orchestration standards to cope with the rise of container technologies. © 2017 IEEE.

关键词： network function virtualization

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Two extended formulations for the virtual network function placement and routing problem

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networkS 2023年第1期82卷 32-51页

作者： Mouaci, Ahlam Gourdin, Eric Ljubic, Ivana Perrot, Nancy Orange Labs MORE Dept Paris France Univ Paris 09 LAMSADE Paris France ESSEC Business Sch Informat Syst Decis Sci & Stat Dept Cergy France Orange Innovat Paris France

Given a bi-directed graph modeling a telecommunication network, and a set of origin-destination pairs representing traffic requests (commodities) along with their associated Service function Chains (SFCs), the Virtual network function Placement and Routing Problem (VNFPRP) aims to find, for each commodity, one latency-constrained routing path that visits the required Virtual network functions in a specific order. The function installation costs together with the node activation costs have to be minimized. In this paper, we present two extended Mixed Integer Programming (MIP) formulations to model the VNFPRP. For each formulation we define the master problem, the pricing problem, the associated Lagrangian bound and a specific branching scheme, in order to derive an efficient Branch-and-Price algorithm. We also provide several families of valid inequalities to strengthen the LP-relaxation bounds. Computational results are reported comparing the performance of the two Branch-and-Price algorithms with a compact MIP formulation and its Branch-and-Benders-cut implementation on a set of SNDlib instances representing telecommunication networks.

关键词： branch-and-price column generation network function virtualization networks service functions chaining

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Multiplicative long short-term memory-based software-defined networking for handover management in 5G network

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SIGNAL IMAGE AND VIDEO PROCESSING 2023年第6期17卷 2933-2941页

作者： Bandani, Anil Kumar Riyazuddien, Shaik Divakarachari, Parameshachari Bidare Patil, Sujata N. Kumar, G. Arvind BV Raju Inst Technol Medak Telangana India Vasireddy Venkatadri Inst Technol Dept ECE Guntur India Nitte Meenakshi Inst Technol Dept Elect & Commun Engn Bengaluru 560064 Karnataka India KLE Dr MS Sheshgiri Coll Engn & Technol Dept Elect & Commun Engn Belgaum Karnataka India Ramaiah Inst Technol Elect & Telecommun Engn Dept Bengaluru India

In recent years, the 5G network faces more traffic congestion due to the increasing number of mobile operators. In the networking field, learning methods are widely used to reduce the cost and increase flexibility and scalability. network function virtualization is used to set the hardware functions as well as Software-Defined networking (SDN) helps to concentrate the programming setup. SDN is a key method designed at sustaining high data traffic in next-generation 5G networks. Conversely, the compact deployment of small cells introduces numerous challenges such as frequent handovers, inconsistencies, interfaces, and so on. The software-defined 5G network is one important technique to solve these problems. Moreover, due to the separation of control and data signaling in 5G technology, the operation of handover should be performed. Therefore, Multiplicative Long Short-Term Memory (mLSTM)-based resource estimation strategy is proposed in this study to solve the handover problem. Finally, analyze the observed delays using the densification ratio metric to evaluate the standard and suggested handover procedures for proposed mLSTM which depends on user count. While related to the traditional SDN technique, the proposed mLSTM reduces the handover delay by 4.28 ms.

关键词： Handover delay Multiplicative long short-term memory network function virtualization Software-defined networking 5G network

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Elastic Adaptive network Slicing Scheme Based on Multi-Priority Cooperative Prediction in Fi-Wi Access network

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2023年第2期41卷 396-403页

作者： Tian, Qinghua Li, Shixuan Wang, Fu Gao, Ran Yao, Haipeng Tian, Feng Yang, Leijing Zhang, Qi Xin, Xiangjun Beijing Univ Posts & Telecommun BUPT State Key Lab Informat Photon & Opt Commun Beijing 100876 Peoples R China BUPT Sch Elect Engn Beijing Key Lab Space Ground Interconnect & Conver Beijing 100876 Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China BUPT State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

With the development of information technology, optical access network gradually presents the characteristics of multi-domain, multi-layer and multi-vendor. The network slicing technology provides a feasible scheme for flexible management of heterogeneous resources. Fi-Wi access network has attracted much attention because of its unique advantages in integrating with network slicing technology. An elastic adaptive network slicing scheme based on multi-priority cooperative prediction in Fi-Wi access network is proposed in this paper. In this scheme, priority model is used to sort the processing order of requests to reduce the delay of delay-sensitive requests and depth-first search algorithm is used to select links with more idle resources to ensure more uniform resource allocation in access network. At the same time, LSTM model is used to predict the traffic, and the resource boundary is changed in advance to offset the extra delay caused by moving the resource isolation boundary when the in-chip reserved resources are insufficient. Simulation results show that the proposed scheme can not only preferentially process delay-sensitive requests, but also select links that reserve more resources for requests. In addition, compared with static slicing and dynamic slicing based on DES prediction, the proposed scheme has lower bandwidth blocking probablity.

关键词： Resource management Bandwidth network slicing Delays Wireless networks Prediction algorithms network function virtualization Fi-Wi access network network slicing software defined network traffic prediction

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