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IEEE Global Communications Conference (GLOBECOM)

作者： Nacef, Abdelhakim Bagaa, Miloud Aklouf, Youcef Kaci, Abdellah Dutra, Diego Leonel Cadette Ksentini, Adlen Univ Sci & Technol Houari Boumediene USTHB Res Lab Informat Intelligence Math & Applicat RII Bab Ezzouar Algeria Ecole Natl Super Technol ENST Lab Technol Innovantes LTI Bordj El Kiffan Algeria CSC Espoo Finland Univ Fed Rio de Janeiro Rio De Janeiro Brazil EURECOM F-06410 Sophia Antipolis France

ISBN: (纸本)9781728181042

The fifth generation of the mobile network aims to revolutionize mobile communication by offering both unparalleled performance and broader service offerings. 5G technology responds to the growing demand for higher bandwidth and lower latency, caused by a significant increase of connected resources. Leveraging on Software-Defined networking (SDN) and artificial intelligence (AI) technologies, the 6G system can autonomously adapt to user requirements. This paper proposes a framework, named intelligent optimization framework (IoF), that leverages both network optimization and machine learning techniques for achieving the best performance results. The IoF framework allows for finding an exemplary resource allocation configuration of the mobile network by leveraging SDN technology. This work aims to configure the SDN-enabled switches and Open vSwitchs (OVSs) to enable an optimized data plane that reduces the overall operational expense (OPEX) cost and capital expense (CAPEX) cost within an optimized execution time. The evaluation results show our proposed framework's efficiency for delivering optimal configurations by reducing the number of allocated OVSs in a reasonable execution time.

关键词： network Optimization Machine Learning Soft-ware Defined networks network functions virtualization Service Function Chaining Multi-path routing

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A comprehensive survey on software-defined networking for smart communities

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2022年第1期38卷

作者： Chaudhary, Rajat Aujla, Gagangeet Singh Kumar, Neeraj Chouhan, Pushpinder Kaur Karlstad Univ Dept Math & Comp Sci Karlstad Sweden Univ Durham Dept Comp Sci Durham DH1 3LE England Thapar Inst Engn & Technol Patiala Punjab India British Telecommun PLC London England

The need to provide services closer to the end-user proximity leads to the exchange of a large volume of data generated from the smart devices deployed at different geo-distributed sites. The massive amount of data generated from the smart devices need to be transmitted, analyzed, and processed. This requires seamless data exchanges among geo-separated nodes, which results in a considerable burden on the underlying network infrastructure and can degrade the performance of any implemented solution. Therefore, a dynamic, agile, and programmable network management paradigm is required. To handle the challenges mentioned above, software-defined networking (SDN) gained much attention from academia, researchers, and industrial sectors. Shifting the computational load from forwarding devices to a logically centralized controller is a dream of every network operator who wants to have complete control and global visibility of the network. Also, the concept of network functions virtualization (NFV) in SDN controller is required to increase resource utilization efficiency. Thus, in this paper, a comprehensive survey on SDN for various smart applications is presented. This survey covers the infrastructural details of SDN hardware and OpenFlow switches, controllers, simulation tools, programming languages, open issues, and challenges in SDN implementation with advanced technologies such as 5G and microservices. In addition, the challenges on the control plane and data plane are highlighted in detail, such as fault tolerance, routing, scheduling of flows, and energy consumption on OpenFlow switches. Finally, various open issues and challenges future scope of SDN are discussed and analyzed in the proposal.

关键词： data center networks edge computing fog computing healthcare ecosystem internet of things network functions virtualization software-defined networking

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Machine learning based fast self optimized and life cycle management network

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COMPUTER networkS 2022年 209卷 108895-108895页

作者： Nacef, Abdelhakim Kaci, Abdellah Aklouf, Youcef Cadette Dutra, Diego Leonel Univ Sci & Technol Houari Boumediene USTHB Res Lab Informat Intelligence Math & Applicat AIW Algiers Algeria Ecole Natl Super Technol ENST Lab Technol Innovantes LTI Algiers Algeria Univ Fed Rio de Janeiro Rio De Janeiro Brazil

6G system targets many emerging industrial verticals, including industry 4.0 and autonomous driving, with extreme service requirements and a high amount of resources, which further overload mobile networks. These industrial verticals are characterized by intense, continuous, and conflicting requirements that harden the mission of the next generation. To achieve the coveted objectives and deal with these shortcomings, the end-to-end network communication should be self-managed, self-orchestrated, and self-optimized, including network edges and clouds. Besides software-defined networking (SDN) and network function virtualization (NFV) that offer network softwarization, elasticity, and flexibility, Machine Learning (ML) is expected to play a vital role in the next-generation of networks. The decisions made at the orchestration plan should be quick besides their accuracy and optimality to overcome the foreseeable challenges. In this paper, we propose a generic framework, named Deep Learning Optimization for Service Function Chaining (DLO-SFC), that provides fast optimal configurations using deep learning techniques and network optimization. We have designed the proposed framework to be generic and orthogonal to a specific use case. Indeed, the framework can be leveraged for any networking configuration and orchestration problems with a slight modification. We evaluate our proposed framework performances showing that it reduces the OPEX costs while optimizing the execution time.

关键词： Software defined networks network functions virtualization Service Function Chaining

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Performance Models of NFV-Based Hybrid Systems for Delay-Sensitive Services 1

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26th International Conference, on Analytical and Stochastic Modelling Techniques and Applications (ASMTA)

作者： Sato, Mitsuki Kawamura, Kohei Kawanishi, Ken'ichi Tuan Phung-Duc Gunma Univ Grad Sch Sci & Technol Kiryu Gumma Japan Gunma Univ Dept Informat Maebashi Gumma Japan Univ Tsukuba Dept Policy & Planning Sci Tsukuba Ibaraki Japan

ISBN: (数字)9783030918255

ISBN: (纸本)9783030918255;9783030918248

NFV (network functions virtualization) technology widely deployed in recent cloud computing is essential to provide network services. NFV is useful for saving operational cost because VNFs (Virtualized network functions) are created dynamically by scaling in/out virtual machines. On the other hand, utilizing existing computing resources, which are provided by legacy equipments and thus do not possess adaptive property of allocating virtual computing resources, is of importance to contribute for maintaining the required performance. In this paper, we propose queueing models of hybrid systems that utilize the NFV technology as well as legacy network equipments. Examples of such systems include 5G networks systems. We focus on a scenario of providing delay-sensitive real-time services, and in particular evaluate the delay performance.

关键词： Cloud computing network functions virtualization Markov chains Delay-sensitive services Delay performance

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A Machine Learning Approach for Service Function Chain Embedding in Cloud Datacenter networks 10

A Machine Learning Approach for Service Function Chain Embed...

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10th IEEE International Conference on Cloud networking (IEEE CloudNet)

作者： Wassing, Tom Jenno De Vleeschauwer, Danny Papagianni, Chrysa Univ Amsterdam Informat Inst Amsterdam Netherlands Nokia Bell Labs Network Syst & Secur Res Lab Antwerp Belgium

ISBN: (纸本)9781665435383

network functions virtualization (NFV) is an industry effort to replace traditional hardware middleboxes with virtualized network functions (VNFs) running on general-build hardware platforms, enabling cost reduction, operational efficiency, and service agility. A Service Function Chain (SFC) constitutes an end-to-end network service, formed by chaining together VNFs in specific order. Infrastructure providers and cloud service providers try to optimally allocate computing and network resources to SFCs, in order to reduce costs and increase profit margins. The corresponding resource allocation problem, known as SFC embedding problem, is proven to be NP-hard. Traditionally the problem has been formulated as Mixed Integer Linear Program (MILP), assuming each SFC's requirements are known a priori, while the embedding decision is based on a snapshot of the infrastructure's load at request time. Reinforcement learning (RL) has been recently applied, showing promising results, specifically in dynamic environments, where such assumptions are considered unrealistic. However, standard RL techniques such as Q-learning might not be appropriate for addressing the problem at scale, as they are often ineffective for high-dimensional domains. On the other hand, Deep RL (DRL) algorithms can deal with high dimensional state spaces. In this paper, a Deep Q-Learning (DQL) approach is proposed to address the SFC resource allocation problem. The DQL agent utilizes a neural network for function approximation in Q-learning with experience replay learning. The simulations demonstrate that the new approach outperforms the linear programming approach. In addition, the DQL agent can perform SFC request admission control in real time.

关键词： network functions virtualization resource allocation reinforcement learning deep Q-learning

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A Vision to Software-Centric Cloud Native network functions: Achievements and Challenges 22

A Vision to Software-Centric Cloud Native Network Functions:...

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IEEE 22nd International Conference on High Performance Switching and Routing (IEEE HPSR)

作者： Kawashima, Ryota Nagoya Inst Technol Grad Sch Engn Nagoya Aichi Japan

ISBN: (纸本)9781665440059

network slicing qualitatively transforms network infrastructures such that they have maximum flexibility in the context of ever-changing service requirements. While the agility of cloud native network functions (CNFs) demonstrates significant promise, virtualization and softwarization severely degrade the performance of such network functions. Considerable efforts were expended to improve the performance of virtualized systems, and at this stage 10 Gbps throughput is a real target even for container/VM-based applications. Nonetheless, the current performance of CNFs with state-of-the-art enhancements does not meet the performance requirements of next-generation 6G networks that aim for terabit-class throughput. The present pace of performance enhancements in hardware indicates that straightforward optimization of existing system components has limited possibility of filling the performance gap. As it would be reasonable to expect a single silver-bullet technology to dramatically enhance the ability of CNFs, an organic integration of various data-plane technologies with a comprehensive vision is a potential approach. In this paper, we show a future vision of system architecture for terabit-class CNFs based on effective harmonization of the technologies within the wide-range of network systems consisting of commodity hardware devices. We focus not only on the performance aspect of CNFs but also other pragmatic aspects such as interoperability with the current environment (not clean slate). We also highlight the remaining missing-link technologies revealed by the goal-oriented approach.

关键词： 6G network functions virtualization Cloud native Data-plane

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Cooperative Multi-Agent Deep Reinforcement Learning for Dynamic Virtual network Allocation 30

Cooperative Multi-Agent Deep Reinforcement Learning for Dyna...

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30th International Conference on Computer Communications and networks (ICCCN)

作者： Suzuki, Akito Kawahara, Ryoichi Harada, Shigeaki NTT Corp NTT Network Technol Labs Tokyo Japan Toyo Univ Fac Informat Networking Innovat & Design Tokyo Japan

ISBN: (纸本)9780738113302

network traffic and computing demand have been changing dramatically due to the growth of various types of network services, e.g., high-quality video delivery and operating system (OS) updates. To maximize the utilization efficiency of limited network resources, network resource control technology is required for smooth and quick operation when network demands change. We propose a dynamic virtual network (VN) allocation method based on cooperative multi-agent deep reinforcement learning (Coop-MADRL). This method can quickly optimize network resources even while network demands are drastically changing by learning the relationship between network demand patterns and optimal allocation by using deep reinforcement learning (DRL) in advance. The key idea is to use a multi-agent technique for a reinforcement learning (RL) based dynamic VN allocation method, which can reduce the number of candidate actions per agent and can improve the performance for VN allocation. Moreover, a cooperation technique improves the efficiency of VN allocation. From results of a simulation evaluation, Coop-MADRL can calculate effective allocation within 1 s, which reduces the maximum server and link utilization and drastically reduces the average constraint violation compared with that of the static VN allocation method.

关键词： network functions virtualization Reinforcement Learning network Control Dynamic Resource Allocation

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A Survey on FPGA Support for the Feasible Execution of Virtualized network functions

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IEEE COMMUNICATIONS SURVEYS AND TUTORIALS 2020年第1期22卷 504-525页

作者： Niemiec, Gabriel S. Batista, Luis M. S. Schaeffer-Filho, Alberto E. Nazar, Gabriel L. Univ Fed Rio Grande do Sul Inst Informat BR-90040060 Porto Alegre RS Brazil

network functions virtualization (NFV) has received considerable attention in the past few years, both from industry and academia, due to its potential for reducing capital and operational expenditures, thus enabling faster innovation in networks. NFV proposes decoupling network functions from fixed hardware platforms and implementing them as virtual machines on off-the-shelf servers. Although potentially able to provide the mentioned benefits, software-based implementations of compute-intensive network functions still struggle to perform at the desired speed, especially when requiring line-rate processing for ever-faster communication links. To address this, hardware acceleration can be used to improve the throughput and latency of virtualized network functions (VNFs). In order to provide the advantages foreseen for NFV, however, such accelerators cannot be fixed and application-specific hardware, since they need to cope with new VNFs as well as fluctuations in demand. In this context, Field-Programmable Gate Arrays (FPGAs) are particularly suitable, since they are able to provide high-performance implementations of network functions and they are completely reprogrammable, thereby being able to implement different VNFs even after deployment. There have been many recent efforts to enable the use of FPGAs in the NFV context, including efficient implementations of network functions on FPGAs, platforms to manage the integration and coexistence of multiple VNFs on an FPGA, and high-level synthesis tools especially tailored to ease the programming of VNFs for FPGAs. In this work we survey previous work covering these aspects, and discuss the main open research challenges that must be addressed before FPGA adoption in NFV infrastructures becomes effectively seamless and efficient.

关键词： Field programmable gate arrays virtualization Tutorials Hardware acceleration Servers Industries network functions virtualization hardware acceleration FPGA

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Management in a Cyber-Physical World: A Report on APNOMS 2019

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JOURNAL OF network AND SYSTEMS MANAGEMENT 2020年第2期28卷 423-431页

作者： Kitaguchi, Yoshiaki Seok, Woojin Huang, Jiun-Long Ju, Hong-Taek Chen, Yen-Wen Yoshihara, Kiyohito Tokyo Inst Technol Meguro Ku 2-12-1 Ookayama Tokyo 1528550 Japan Korea Inst Sci & Technol 245 Daehak Ro Daejeon 34141 South Korea Natl Chiao Tung Univ 1001 Univ Rd Hsinchu 30010 Taiwan Keimyung Univ 1095 Dalgubeol Daero Daegu 42601 South Korea Natl Cent Univ 300 Zhongda Rd Taoyuan 32001 Taiwan KDDI Res 2-1-15 Ohara Saitama 3568502 Japan

This article presents a report on APNOMS 2019, which was held on September 18-20, 2019 in Kunibiki Messe, Matsue, Japan. The theme of APNOMS 2019 was "Management in a Cyber-Physical World."

关键词： network Operations and Management Software Defined networks network functions virtualization Cyber-Physical System

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Uninterruptible IMS: Maintaining Users Access During Faults in Virtualized IP Multimedia Subsystem

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2020年第7期38卷 1464-1477页

作者： Raza, Muhammad Taqi Lu, Songwu Univ Arizona Dept Informat Syst Tucson AZ 85721 USA Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90095 USA

network function virtualization (NFV) of IP Multimedia Subsystem (IMS) pose promise to service increasing multimedia traffic demand. In this paper, we show that virtualized IMS (vIMS) is unable to provide session-level resilience under faults and becomes the bottleneck to high service availability. We propose a design to provide fault-tolerance for vIMS operations. In control-plane, our system decomposes single IMS operation into different atomic actions, and partition these actions into critical and non-critical actions. Only the critical actions are then monitored in real time and the system can easily resume IMS operations after failure. In data-plane, we decompose multimedia traffic flows and partition each multimedia service as a separate Virtualized network Function (VNF). Through data-plane partitioning, our design restricts the damage from faults to only failed VNF. Thereafter, impacted service flow is merged with other ongoing service flows. We build our system prototype of open source IMS over virtualized platform. Our results show that we can achieve session-level resilience by performing fail-over procedure within tens of milliseconds under different combinations of IMS failures in both control-plane and data-plane operations.

关键词： network functions virtualization LTE IP multimedia subsystem fault tolerance software defined networking

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