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2024 International Conference on Communications Control and Computing Technologies for Smart Grids

作者： Delgado, Brian G. Kodiles Aguayo, Jared M. Rivera, Abel O. Gomez Tosh, Deepak K. Univ Texas El Paso Dept Comp Sci El Paso TX 79968 USA Johns Hopkins Univ Appl Phys Lab Laurel MD USA Sandia Natl Labs Albuquerque NM USA

ISBN: (纸本)9798350318562;9798350318555

The ongoing transition to Industry 4.0, which is characterized by increased inter-connectivity of cyber-physical systems, requires having time-sensitive, high throughput, and secure transfer of critical data in industrial sites. In this context, network slicing emerges as a critical tool to ensure timely data delivery by provisioning the network resources to cater to specific applications' requirements and mitigating potential cyber attacks. To address these challenges, this paper aims to tackle two key questions essential for the successful implementation of network slicing in industrial environments. First, it investigates architectural considerations for developing a network infrastructure capable of supporting network slicing functionalities effectively. The proposed approach significantly improves deployment efficiency over traditional manual configurations. Second, it delves into the automated orchestration process, elucidating the steps and components involved in transitioning from a static network management approach to dynamically leverage network function virtualization schemes for creating network slices in ad-hoc manner. The system demonstrates high throughput suitable for production-level solutions and maintains exceptionally low latency, making it ideal for ultra-reliable low-latency communications. Even with increased network demands, the system remains stable, with effective Quality of Service (QoS) management, ensuring reliable performance under varying conditions. The proposed architecture outlines the necessary components, services, and communication protocols required for a production-level orchestrator for network segmentation in SCADA environments.

关键词： Cyber-Physical Systems network function virtualization Operational Technology Software-Defined network

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AI-based network function virtualization Orchestration

AI-based Network Function Virtualization Orchestration

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IEEE/IFIP network Operations and Management Symposium (NOMS)

作者： Kim, Hee-Gon Yoo, Jae-Hyoung Hong, James Won-Ki Pohang Univ Sci & Technol Comp Sci & Engn Pohang South Korea

ISBN: (纸本)9798350327939;9798350327946

network orchestration is pivotal in automating device, equipment, and service management within the network system. Currently, network function virtualization (NFV) offers immense potential, but the challenge still lies in designing a capable orchestrator for dynamic networks. To address this challenge, we propose an AI-based NFV Orchestration Framework that leverages self-learning capabilities to detect dynamic network changes and make optimal decisions. This framework covers a range of essential functionalities, including NFV Orchestration, VNF Deployment, Service function Chaining (SFC), Auto-Scaling, Migration, Anomaly Detection, Power Management, and Attack & Intrusion Detection. These functions collectively form a comprehensive ML-driven orchestration framework that offers adaptability, intelligence, and efficiency across the entire NFV environment. Our proposed structure aims for zero-touch automation, contributing to the efficient management of dynamic NFV network environments, and making it a compelling solution for the future of networking.

关键词： network Orchestration network function virtualization Machine Learning

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StateOS: Enabling Versatile network function virtualization in Edge Clouds

StateOS: Enabling Versatile Network Function Virtualization ...

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IEEE/IFIP network Operations and Management Symposium (NOMS)

作者： Doan, Tung V. Fitzek, Frank H. P. Nguyen, Giang T. Tech Univ Dresden Hapt Commun Syst Dresden Germany Tech Univ Dresden Deutsch Telekom Chair Commun Networks Dresden Germany Ctr Tactile Internet Human In The Loop CeTI Dresden Germany

ISBN: (纸本)9798350327939;9798350327946

network functions (NFs) in edge clouds are required to provide scalability, fault tolerance, and mobility support. They all require maintaining NF states (i.e., processing results), e.g., for recovery, especially for stateful NFs like firewalls. Even though current solutions provide an alternative to storing states in memory, their design can support only a single requirement, either fault tolerance or scaling. Advocating the versatility, we propose StateOS - an operating system of NF states for user-defined programs supporting different requirements. Additionally, we propose a state transfer scheme, namely Divide-and-Conquer (DAC), to accelerate StateOS. The combination of DAC and StateOS demonstrates its efficiency for all three scenarios: scaling, fault tolerance, and service function chain acceleration.

关键词： State management network function virtualization edge clouds

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Towards Intent-based Configuration for network function virtualization using In-context Learning in Large Language Models

Towards Intent-based Configuration for Network Function Virt...

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IEEE/IFIP network Operations and Management Symposium (NOMS)

作者： Nguyen Van Tu Yoo, Jae-Hyoung Hong, James Won-Ki POSTECH Dept CSE Pohang South Korea

ISBN: (纸本)9798350327939;9798350327946

network function virtualization (NFV) enables the execution of Virtual network functions (VNFs) on standard commodity servers. This brings flexibility, allowing for the rapid deployment of various network services while reducing costs. However, NFV configurations are becoming increasingly complex, necessitating experts for the setup. Intent-based network configuration has emerged as a solution to simplify NFV configuration and management. Nonetheless, it presents challenges, such as translating high-level natural language intents into low-level network configurations. In this work, we propose NFV-Intent - a system that leverages in-context learning in Large Language Models to perform the intent translation task. In-context learning enables NFV-Intent to work without retraining the Large Language Models, which is a difficult and expensive task. NFV-Intent uses a JSON template as the desired output, allowing Large Language Models to learn with a small number of examples and enabling easy verification of the configuration. Our evaluation showed that the intent can be translated into JSON configuration with high accuracy. To demonstrate the feasibility of NFV-Intent, we implemented and integrated it into the NI-testbed, our previously developed system for AI-based NFV life-cycle management.

关键词： network function virtualization Virtual network function Intent-based networking In-context Learning Large Language Model

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Placement of Deep Reinforcement Learning in Service function Chain of network function virtualization 2

Placement of Deep Reinforcement Learning in Service Function...

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2nd International Conference on Data Science and Information System, ICDSIS 2024

作者： Xu, Hongsheng Huang, Kaifeng Luoyang Normal University College Of Electronic And Commerce Luoyang China Luoyang Normal University Henan Key Laboratory For Big Data Processing & Analytics Of Electronic Commerce Luoyang China

ISBN: (纸本)9798350381665

Reinforcement learning is a goal-oriented algorithm. The core goal of the algorithm is to learn to make decisions during the interaction between intelligent agents and the environment. Deep Reinforcement Learning is an algorithm that combines the perception ability of deep learning and the powerful decision-making ability of reinforcement learning. It can directly understand the network and task from experience, thus improving the accuracy and reducing repeated modeling, and is widely used to solve complex problems. A network Service in NFV can be represented as a Service function Chain (SFC), which consists of a series of Virtual network functions (VNF) in a given order. In order to provide efficient services, SFCS are deployed in a number of different servers and direct traffic through a set of VNFS in a specific order. This paper proposes SFC placement in NFV based on deep reinforcement learning model. Finally, it is found that the deep learning model can effectively improve the efficiency of SFC placement. © 2024 IEEE.

关键词： network function virtualization

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DRL-Based Service function Chains Embedding Through network function virtualization in STINs 2

DRL-Based Service Function Chains Embedding Through Network ...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Li, Li Yang, Chuhong Li, Haoyang He, Dongxuan Beijing Institute of Technology School of Information and Electronics Beijing China

ISBN: (纸本)9798331515669

Space-terrestrial integrated networks (STINs) are gaining increasing attention for their outstanding benefits in providing seamless connectivity, enhancing network resilience, increasing capacity, and expanding coverage. The combination of network function virtualization (NFV) and deep reinforcement learning (DRL) is a promising candidate to boost the capability of STINs to deliver high-quality services. In this paper, a STIN architecture based on artificial intelligence (AI) algorithms and the process of embedding Service function Chains (SFCs) into highly dynamic STINs are studied. Then, we propose an enhanced DRL-based SFCs embedding algorithm that initiates with a feature fusion phase of links, followed by aggregating the feature vectors of all nodes and their neighboring nodes to form context information. This context information is then used as part of the input state for the DRL-based SFCs embedding algorithm. Simulation results show that the proposed algorithm can effectively reduce the latency of SFCs embedding when compared with other existing algorithms, thus showing the superiority of our proposed algorithm. © 2024 IEEE.

关键词： network function virtualization

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Data-Plane-Assisted State Replication With network function virtualization

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IEEE SYSTEMS JOURNAL 2022年第2期16卷 2934-2945页

作者： Lotfimahyari, Iman Sviridov, German Giaccone, Paolo Bianco, Andrea Politecn Torino Dipartimento Elettron & Telecomunicaz I-10129 Turin Italy

Modern 5G networks are capable of providing ultralow-latency and highly scalable network services by employing modern networking paradigms, such as software-defined networking (SDN) and network function virtualization. The latter enables performance-critical network applications to be run in a distributed fashion directly inside the infrastructure. Being distributed, those applications rely on sophisticated state replication algorithms to synchronize states among each other. Nevertheless, current implementations of such algorithms do not fully exploit the potential of the modern infrastructures, thus leading to suboptimal performance. In this article, we propose STARE, a novel state replication system tailored for 5G networks. At its core, STARE exploits stateful SDN to offload replication-related processes to the data plane, ultimately leading to reduced communication delays and processing overhead for virtual network functions. We provide a detailed description of the STARE architecture alongside a publicly-available P4-based implementation. Furthermore, our evaluation shows that STARE is capable of scaling to big networks while introducing low overhead in the network.

关键词： Protocols network function virtualization Partitioning algorithms 5G mobile communication Wireless fidelity Scalability Real-time systems 5G network function virtualization (NFV) programmable data planes publish– subscribe model state sharing

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Enhancing network function virtualization and Service function Chaining: Innovative Optimization Strategies and Their Impact 1

Enhancing Network Function Virtualization and Service Functi...

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1st International Conference on Pioneering Developments in Computer Science and Digital Technologies, IC2SDT 2024

作者： Upadhyay, Deepak Gupta, Mridul Sharma, Kunj Bihari Upadhyay, Abhay Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350365016

Throughout this paper, the reader was able to better understand how innovative optimization techniques through NFV and SFC frameworks could and should be applied to address the developing and shifting focus of contemporary network infrastructures. Focusing on performance metrics performance via latency, throughput, resource utilization, and operational cost, the presented algorithms and machine learning models developed and executed due to this research showed innovative and dynamic optimization of network functions. Through further extensive simulations equalling 125 seconds, our findings reported creative results of improving performance in several significant areas: latency was reduced, throughput was enhanced, resource utilization was more efficient, and operational cost was reduced. These results indicated that our optimization strategies can pay off and bring a number of benefits to NFV and SFC that address a clear need for change in the current type of network management models. Therefore, this paper is valuable and serves as a testament of our strategies' power along with opening various doors for following network optimization research. Therefore, one can see how important it is to implement innovation in the face of change. © 2024 IEEE.

关键词： network function virtualization

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network function virtualization Resource Allocation Based on Joint Benders Decomposition and ADMM

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2020年第2期69卷 1706-1718页

作者： Yu, Ye Bu, Xiangyuan Yang, Kai Nguyen, Hung Khanh Han, Zhu Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Univ Houston Dept Elect & Comp Engn Houston TX 77004 USA Univ Houston Houston TX 77004 USA Kyung Hee Univ Dept Comp Sci & Engn Seoul 446701 South Korea

network function virtualization (NFV) has emerged as a new technology to reduce the cost of hardware deployment. It is an architecture that using virtualized functions run on the virtual machine to achieve services instead of using specific hardware. Although NFV brings more opportunities to enhance the flexibility and efficiency of the network, resource allocation problems should be well taken into consideration. In this paper, we investigate the virtual network function (VNF) resource allocation problem to minimize the network operation cost for different services. Both setting the VNF instances for each virtual machine and allocating the traffic volume in the network are considered. The problem is formulated as a mixed integer programming problem. Although it can be solved in a centralized fashion which requires a central controller to collect information from all virtual machines, it is not practical for large-scale networks. Thus, we propose a distributed iteration algorithm to achieve the optimal solution. The proposed algorithm framework is developed based on the joint Benders decomposition and alternating direction method of multipliers (ADMM), which allows us to deal with integer variables and decompose the original problem into multiple subproblems for each virtual machine. Furthermore, we describe the detail implementation of our algorithm to run on a computer cluster using the Hadoop MapReduce software framework. Finally, the simulation results indicate the effectiveness of the algorithm.

关键词： network function virtualization resource allocation Benders decomposition alternating direction method of multipliers Hadoop MapReduce

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Research on Energy Saving of network function virtualization Based on OpenStack

Research on Energy Saving of Network Function Virtualization...

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International Conference on Internet of Things, Communication and Intelligent Technology, IoTCIT 2022

作者： Qiu, Rixuan Wu, Lingzhi Chen, Mingliang Zhang, Junfeng Wang, Shuang Jing, Sitong Information and Communication Branch China State Grid Jiangxi Electric Power Co. Ltd Jiangxi Province Nanchang City China Power Dispatching Control Center of State Grid Jiangxi Electric Power Co. Ltd Jiangxi Province Nanchang City China China State Grid Jiangxi Electric Power Co. Ltd Jiangxi Province Nanchang City China Jiangxi Nanchang Power Construction Group Jiangxi Electric Power Design Institute Co. Ltd Jiangxi Province Nanchang City China

ISBN: (纸本)9789819904150

network service has become the most important content in the network function at the present stage, due to the limitation of technical conditions, the previous application of proprietary hardware equipment has been di cult to meet the high quality and e client needs of the current network service. Applied to the standard virtualization technology to the network function virtualization, can effectively meet the needs of improving the quality of network service. This paper studies the application of network virtualization and analyzes the energy saving of network virtualization based on OpenStack. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： network function virtualization

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