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 indust...
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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.
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 li...
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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.
networkfunctions (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 ...
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ISBN:
(纸本)9798350327939;9798350327946
networkfunctions (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.
network function virtualization (NFV) enables the execution of Virtual networkfunctions (VNFs) on standard commodity servers. This brings flexibility, allowing for the rapid deployment of various network services whi...
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ISBN:
(纸本)9798350327939;9798350327946
network function virtualization (NFV) enables the execution of Virtual networkfunctions (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.
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 a...
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Space-terrestrial integrated networks (STINs) are gaining increasing attention for their outstanding benefits in providing seamless connectivity, enhancing network resilience, increasing capacity, and expanding covera...
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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...
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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 networkfunctions. 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.
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 contemp...
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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 in...
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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 networkfunction (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.
network service has become the most important content in the networkfunction at the present stage, due to the limitation of technical conditions, the previous application of proprietary hardware equipment has been di...
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