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Online Adaptive Interference-Aware VNF Deployment and Migration for 5G network Slice

IEEE-ACM TRANSACTIONS ON networkING 2021年第5期29卷 2115-2128页

作者： Zhang, Qixia Liu, Fangming Zeng, Chaobing Natl Engn Res Ctr Big Data Technol & Syst Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Serv Comp Technol & Syst Lab Cluster & Grid Comp Lab Wuhan 430074 Peoples R China

Based on network function virtualization (NFV) and software defined network (SDN), network slicing is proposed as a new paradigm for building service-customized 5G network. In each network slice, service-required virtual network functions (VNFs) can be flexibly deployed in an on-demand manner, which will support a variety of 5G use cases. However, due to the real-time network variations and diverse performance requirements among different 5G scenarios, online adaptive VNF deployment and migration are needed to dynamically accommodate to service-specific requirements. In this paper, we first propose a time-slot based 5G network slice model, which jointly includes both edge cloud servers and core cloud servers. Since VNF consolidation may cause severe performance degradation, we adopt a demand-supply model to quantify the VNF interference. To achieve our objective-maximizing the total reward of accepted requests (i.e., the total throughput minus the weighted total VNF migration cost), we propose an Online Lazy-migration Adaptive Interference-aware Algorithm (OLAIA) for real-time VNF deployment and cost-efficient VNF migration in a 5G network slice, where an Adaptive Interference-aware Algorithm (AIA) is proposed as OLAIA's core function for placing a given set of requests' VNFs with maximized total throughput. Through trace-driven evaluations on two typical 5G network slices, we demonstrate that OLAIA can efficiently handle the real-time network variations and the VNF interference when deploying VNFs for real-time arriving requests. In particular, OLAIA improves the total reward by 22.18% in the autonomous driving scenario and by 51.10% in the 4K/8K HD video scenario, as compared with other state-of-the-art solutions.

关键词： 5G mobile communication Servers Real-time systems Throughput Interference Adaptive systems Quality of service network function virtualization 5G network slice performance interference online deployment and migration

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Fair Scheduling and Rate Control for Service function Chain in NFV Enabled Data Center

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IEEE TRANSACTIONS ON network AND SERVICE MANAGEMENT 2021年第3期18卷 2975-2986页

作者： Zu, Jiachen Hu, Guyu Peng, Dongyang Xie, Shengxu Gao, Wenbin Army Engn Univ Inst Command & Control Engn Nanjing 210007 Peoples R China

With the emerging paradigm of Virtual network and network function virtualization (NFV), the data center operator can flexibly manage the network to reduce Operating Expenditures (OPEX) and Capital Expenditures (CAPEX). Some new issues including the assignment of Virtual network functions (VNFs) and Service function Chain (SFC) scheduling should be considered to apply in the communication. Mathematically, the SFC scheduling process can be formulated as a queuing system, and every server installed with VNF instances can be regarded as a service node, where batched requests are parallel submitted and processed one after another. In the management of SFC, the cloud service provider needs to resolve the problem of network congestion. Latency and throughput are two important but contradictory indexes in the network management, and both of them are deserved to be optimized during the scheduling of SFC requests. In this paper, different from most existing studies, we focus on the service rate control problem in the scheduling of SFC requests. Firstly, we formulate this problem as an integer programming problem. Through a cooperative game approach, a Nash bargaining based model is proposed to jointly optimize the latency and throughput, which is proven to provide fair performance guarantees by both theoretical analysis and simulation. To improve the scalability of the proposed algorithm, we also design a polynomial two-phase heuristic to perform Pareto optimization. Simulation evaluation shows that the proposed algorithm can implement balanced traffic scheduling and avoid excessive server latency caused by network congestion.

关键词： Servers Bandwidth Throughput Heuristic algorithms Containers Dynamic scheduling Data centers Service function chain network function virtualization network resource allocation Nash bargaining game

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An End-to-End (E2E) network Slicing Framework for 5G Vehicular Ad-Hoc networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第7期70卷 7103-7112页

作者： Khan, Ammara Anjum Abolhasan, Mehran Ni, Wei Lipman, Justin Jamalipour, Abbas Univ Technol Sydney Sch Elect & Data Engn Ultimo NSW 2007 Australia CSIRO Data61 Marsfield NSW 2122 Australia Univ Sydney Ubiquitous Mobile Networking Sydney NSW 2006 Australia

network slicing is emerging as a promising solution for end-to-end resource management and orchestration together with Software Defined networking (SDN) and network function virtualization (NFV) technologies. In this paper, a comprehensive network slicing framework is presented to achieve end-to-end (E2E) QoS provisioning among customized services in 5G-driven VANETs. The proposed scheme manages the cooperation of both RAN and Core network (CN), using SDN, NFV and Edge Computing technologies. Furthermore, a dynamic radio resource slice optimization scheme is formulated mathematically, that handles a mixture of mission-critical and best effort traffic, by delivering the QoS provisioning of Ultra-reliability and low latency. The proposed scheme adjusts the optimal bandwidth slicing and dynamically adapts to instantaneous network load conditions in a way that a targeted performance is guaranteed. The problem is solved using a Genetic Algorithm (GA) and results are compared with the previously proposed 5 G VANET architecture. Simulation reveal that the proposed slicing framework is able to optimize resources and deliver on the key performance metrics for mission critical communication.

关键词： network slicing 5G mobile communication Mission critical systems Quality of service Cloud computing Reliability Servers network slicing 5 G vehicular ad-hoc networks VANETs edge computing software defined networking network function virtualization genetic algorithm

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Dynamic network function Provisioning to Enable network in Box for Industrial Applications

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第10期17卷 7155-7164页

作者： Sun, Gang Xu, Zhu Yu, Hongfang Chang, Victor Univ Elect Sci & Technol China Minist Educ Key Lab Opt Fiber Sensing & Commun Chengdu 611731 Peoples R China Agile & Intelligent Comp Key Lab Sichuan Prov Chengdu 611731 Peoples R China Peng Cheng Lab Shenzhen 518066 Peoples R China Teesside Univ Sch Comp & Digital Technol Artificial Intelligence & Informat Syst Res Grp Middlesbrough TS1 3BX Cleveland England

network function virtualization (NFV) in 6G can use standard virtualization techniques to enable network functions via software. Resource scheduling is one of the key research areas of NFV in 6G and is mainly used to deploy service function chains (SFCs) in substrate networks. However, determining how to utilize network resources efficiently has always been a difficult problem in SFC deployment. This article focuses on how to efficiently provision online SFC requests in NFV with 6G. We first establish a mathematical model for the problem of online SFC provisioning. Then, we propose an efficient online service function chain deployment (OSFCD) algorithm that selects the path to deploy that is close to the SFC length. Finally, we compare our proposed algorithm with three other existing algorithms by simulation experiments. The experimental results show that the OSFCD algorithm optimizes multiple performance indicators of online SFC deployment.

关键词： 6G mobile communication Machine learning algorithms Service function chaining Heuristic algorithms Software network function virtualization Mathematical model 6G latency network function provisioning network in box resource efficiency

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A Theoretical Discussion and Survey of network Automation for IoT: Challenges and Opportunity

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IEEE INTERNET OF THINGS JOURNAL 2021年第15期8卷 12021-12045页

作者： Arzo, Sisay Tadesse Naiga, Claire Granelli, Fabrizio Bassoli, Riccardo Devetsikiotis, Michael Fitzek, Frank H. P. Univ Trento Dept Informat Engn & Comp Sci I-38122 Trento Italy Tech Univ Dresden Fac Elect & Comp Engn Inst Commun Technol Deutsch Telekom Chair Commun Networks D-01062 Dresden Germany Tech Univ Dresden Ctr Tactile Internet Human Loop Cluster Excellence D-01062 Dresden Germany Univ New Mexico Dept Elect & Comp Engn Albuquerque NM 87131 USA

The introduction of the Internet of Things (IoT) and massive machine-type communications has implied an increase in network size and complexity. In particular, there is already a huge number of IoT devices in the market in various sectors, such as smart agriculture, smart city, smart home, smart transportation, etc. The IoT interconnectivity technologies are also increasing. Therefore, these are increasingly overwhelming the efforts of network administrators as they try to design, reconfigure and manage such networks. Relying on humans to manage such complex and dynamic networks is becoming unsustainable. network automation promises to reduce the cost of administration and maintenance of network infrastructure, by offering networks the capability to manage themselves. network automation is the ability of the network to manage itself. Various standardization organizations are taking the initiative in introducing network automation, such as European Telecommunication Standardization Institute (ETSI). ETSI is leading the standardization activities for network automation. It has provided different versions of reference architecture called generic autonomic network architecture (GANA), which describes a four-level abstraction for network-management decision elements (DEs), protocol level, function level, node level, and network level. In this article, we review and survey the existing works before and after the introduction of software-defined networking (SDN) and network-function-virtualization (NFV). We relate the main trending paradigms being followed, such as SDN, NFV, machine learning (ML), microservices, multiagent system (MAS), containerization, and cloudification, as a pivotal enabler of full network automation. We also discuss the autonomic architectures proposed in the literature. Finally, we presented possible future research directions and challenges that need to be tackled to progress in achieving full network automation.

关键词： Automation Internet of Things Computer architecture Market research Software Monitoring Cloud computing Autonomic networking machine learning multiagent system (MAS) network function virtualization network management system (NMS) network softwarization software-defined networking (SDN)

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A novel approach to efficient resource allocation in load-balanced cellular networks using hierarchical DRL

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第5期13卷 2887-2901页

作者： Rahimi, A. Mirzaei Ziaeddini, Amir Gonglee, Shu Islamic Azad Univ Ardabil Branch Dept Comp Engn Ardebil Iran Commun & Informat Technol Inst Comp Networks Lab Tehran Iran Chengdu Univ Technol Dept Comp Sci Chengdu 610059 Sichuan Peoples R China

5G wireless networks require highly spectral-efficient multiple access techniques, which play an important role in determining the performance of mobile communication systems. Multiple access techniques can be classified into orthogonal and nonorthogonal based on the way the resources are allocated to the users. This paper investigates the joint user association and resource allocation problem in an uplink multicast NOMA system to maximize the power efficiency with guaranteeing the quality-of-experience of all subscribers. We also introduce an adaptive load balancing approach that aspires to obtain "almost optimal" fairness among servers from the quality of service (QoS) perspective in which learning automata (LA) has been used to find the optimal solution for this dynamic problem. This approach contains a sophisticated learning automata which consists of time-separation and the "artificial" ergodic paradigms. Different from conventional model-based resource allocation methods, this paper suggested a hierarchical reinforcement learning based frameworks to solve this non-convex and dynamic power optimization problem, referred to as hierarchical deep learning-based resource allocation framework. The entire resource allocation policies of this framework are adjusted by updating the weights of their neural networks according to feedback of the system. The presented learning automata find the epsilon-optimal solution for the problem by resorting to a two-time scale-based SLA paradigm. Numerical results show that the suggested hierarchical resource allocation framework in combination with the load balancing approach, can significantly improve the energy efficiency of the whole NOMA system compared with other approaches.

关键词： Hierarchical resource allocation Fair load balancing network function virtualization Next generation mobile networks

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Embedding Multicast Service function Chains in NFV-Enabled networks

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IEEE COMMUNICATIONS LETTERS 2021年第4期25卷 1264-1268页

作者： Asgarian, Mina Mirjalily, Ghasem Luo, Zhi-Quan Yazd Univ Dept Elect Engn Yazd *** Iran Chinese Univ Hong Kong Sch Data Sci Shenzhen 518172 Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518172 Peoples R China

In network function virtualization (NFV), a multicast data flow must be traversed through a Service function Chain (SFC) that is a predefined sequence of Virtual network functions (VNFs). In this letter, we formulate the problem of optimal embedding of NFV-Enabled multicast service chains as a mixed binary linear program. Then to solve the problem in a reasonable time, we use the penalty-based successive upper bound minimization algorithm. Also, a heuristic solution based on the decomposition of the problem into smaller sub-problems that can be solved iteratively and sequentially, is proposed. Simulation results demonstrate the effectiveness of the proposed solution methods.

关键词： Multicast algorithms Heuristic algorithms Approximation algorithms network function virtualization Minimization Upper bound Optimization Multicast network function virtualization service function chain

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network function Placement Under Randomly Arrived networking Traffic

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND networkING 2021年第4期7卷 1382-1398页

作者： Sun, Jie Liu, Feng Wang, Huandong Ahmed, Manzoor Li, Yong Zhang, Lianlian Zeng, Hao Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 10084 Peoples R China Qingdao Univ Coll Comp Sci & Technol Qingdao 266071 Peoples R China

Y The virtual network functions (VNFs) placement problem has drawn significant attention from both academia and industry in recent years. Most of the researchers have ignored the fact that the probability of traffic flows through VNFs cannot always be 100%. In this paper, we study the placement scheme for virtual network function considering randomized data traffic (VNFPRAT). Our objective is to determine optimal deployment locations for VNFs and minimize total end-to-end delay. We formulate the VNFPRAT problem as a 0-1 nonlinear programming problem and prove its NP-hardness. This formulation is linearized to obtain the optimal solution for small scale networks. Besides, two efficient metaheuristics, i.e., greedy and simulated annealing, are proposed quickly find a near-optimal placement solution. Extensive simulations demonstrate that our proposed approach achieves 38.8% less end-to-end delay than the generic algorithm.

关键词： network function virtualization VNF placement randomly arrived traffic

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Security-SLA-guaranteed service function chain deployment in cloud-fog computing networks

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CLUSTER COMPUTING-THE JOURNAL OF networkS SOFTWARE TOOLS AND APPLICATIONS 2021年第3期24卷 2479-2494页

作者： Zhao, Dongcheng Luo, Long Yu, Hongfang Chang, Victor Buyya, Rajkumar Sun, Gang Univ Elect Sci & Technol China Key Lab Opt Fiber Sensing & Commun Minist Educ Chengdu Peoples R China Peng Cheng Lab Shenzhen Peoples R China Teesside Univ Sch Comp Engn & Digital Technol Middlesbrough Cleveland England Univ Melbourne Sch Comp & Informat Syst Cloud Comp & Distributed Syst CLOUDS Lab Parkville Vic Australia Agile & Intelligent Comp Key Lab Sichuan Prov Chengdu Peoples R China

network function virtualization (NFV) has gained prominence in next-generation cloud computing, such as the fog-based radio access network, due to their ability to support better QoS in network service provision. However, most of the current service function chain (SFC) deployment researches do not consider the Security-Service-Level-Agreement (SSLA) in the deployment solution. Therefore, in this work, we introduce the SSLA into SFC deployment to defend attacks. Firstly, we formulate the SSLA guaranteed SFC deployment problem by using linear programming. Then, we propose the Maximal-security SFC deployment algorithm (MS) to maximize the security of the SFC deployment. However, the MS algorithm results in a high deployment cost. To reduce the deployment cost, we propose the Minimal-cost and SSLA-guaranteed SFC deployment algorithm (MCSG) to minimize the deployment while satisfying the SSLA. In order to reduce the blocking ratio caused by MCSG, the Minimal-cost and SSLA-guaranteed SFC deployment algorithm with feedback adjustment (MCSG-FA) is proposed. Finally, we evaluate our proposed algorithms through simulations. The simulation results show that the blocking ratio and the deployment cost of our algorithms are better than that of the existing algorithm when meeting the SSLAs.

关键词： Service function chain network function virtualization Deployment Security Fog-cloud computing

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Elastic Provisioning of Stateful Telco Services in Mobile Cloud networking

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2021年第3期14卷 710-723页

作者： Bellavista, Paolo Corradi, Antonio Edmonds, Andy Foschini, Luca Zanni, Alessandro Bohnert, Thomas Michael Univ Bologna Viale Risorgimento 2 I-40136 Bologna Italy InIT Cloud Comp Lab ZHAW Sch Engn Obere Kirchgasse 2 CH-8400 Winterthur Switzerland

Several relevant research and innovation activities have recently investigated the technical and economic advantages of cloud computing for the provisioning of telco service infrastructures, in particular towards all-IP next generation 5G networks. In fact, the evolution of telco service infrastructures traditionally requires a significant upfront investment (and a long adoption process). Conversely, cloud exploitation significantly lowers investment risks by potentially providing elasticity in service provisioning via flexible Virtual network functions (VNFs) on top of a network functions virtualization (NFV) Infrastructure. In this context, the paper presents novel solutions that we have designed, implemented, and evaluated within the EU FP7 Mobile Cloud networking project (MCN). Their aim is to achieve cost-effective elastic provisioning of telco services over heterogeneous and federated cloud providers, with the specific focus of supporting the extreme quality levels that are demanded by traditional, non-virtualized, and dedicated telco infrastructures. In particular, we concentrate on how to effectively and efficiently automate service state migration for coarse-grained telco service (cloudified) components by leveraging industry-mature orchestration technologies and cloud management frameworks. While our proposed state migration model and procedure are general, its implementation is experimented for MCN's Rating, Charging, and Billing as a Service (RCBaaS). This MCN functionality has been chosen by purpose due to its challenging reliability and uptime requirements. The reported experimental and simulation results show the technical feasibility of the proposed solution under different and realistic load conditions for next-generation and cloudified 5G services.

关键词： Cloud computing Proposals network function virtualization Data centers Databases 5G mobile communication Resource management Cloud computing service state migration virtual machine and containers service provisioning service composition mobile cloud networking network function virtualization resource management

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