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Edge Computing in the Internet of Things: A 6G Perspective

IT PROFESSIONAL 2024年第5期26卷 62-70页

作者： Ishtiaq, Mariam Saeed, Nasir Khan, Muhammad Asif Univ Sci & Technol Korea Railroad Res Inst Uiwang 16105 Gyepnggi Do South Korea United Arab Emirates Univ Dept Elect & Commun Engn Al Ain U Arab Emirates Qatar Mobil Innovat Ctr Innovat Ctr Doha Qatar

Edge computing is one of the key driving forces to enable beyond 5G (B5G) and 6G networks. To cope with the unprecedented increase in traffic volumes and computation demands of future networks, multiaccess (or mobile) edge computing (MEC) is considered a promising solution that provides cloud computing capabilities within the radio access network. There exists a significant amount of research on MEC and its potential applications;however, very little has been said about the key factors of MEC deployment to meet the diverse demands of future applications. This article presents key considerations for edge deployments in B5G/6G networks, including edge architecture, server location, capacity, user density, security, etc. We further present state-of-the-art edge-centric services in future B5G/6G networks, including experimental evaluation of edge-based computing. Lastly, we present some interesting insights and open research problems in edge computing for 6G networks.

关键词： 6 G Mobile Communication Cloud Computing Multi Access Edge Computing 5 G Mobile Communication Computer Architecture Servers Security Radio Access networks Internet Of Things Edge Cloud Infrastructure Data Center Cloud Computing Open Research network Operators Internet Of Things Devices Computational Capabilities 5 G networks Edge Server Mobile Edge Computing Radio Access network Computation Offloading Microservices 6 G networks Ultra Low Latency High Degree Of Mobility Future Of Humanity Open Research Problem Mobile Edge Computing Server Local Edge Distributed Energy Resources Predictive Maintenance Smart Manufacturing Mobile network Intelligent Transportation Systems Mobile network Operators virtual network functions Edge Caching

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Resource Allocation in NFV: A Comprehensive Survey

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IEEE TRANSACTIONS ON network AND SERVICE MANAGEMENT 2016年第3期13卷 518-532页

作者： Herrera, Juliver Gil Botero, Juan Felipe Univ Antioquia Fac Engn Medellin 1226 Colombia

network functions virtualization (NFV) is a new network architecture framework where network functions that traditionally used dedicated hardware (middleboxes or network appliances) are now implemented in software that runs on top of general purpose hardware such as high volume servers. NFV emerges as an initiative from the industry (network operators, carriers, and manufacturers) in order to increase the deployment flexibility and integration of new network services with increased agility within operator's networks and to obtain significant reductions in operating expenditures and capital expenditures. NFV promotes virtualizing network functions such as transcoders, firewalls, and load balancers, among others, which were carried out by specialized hardware devices and migrating them to software-based appliances. One of the main challenges for the deployment of NFV is the resource allocation of demanded network services in NFV-based network infrastructures. This challenge has been called the NFV resource allocation (NFV-RA) problem. This paper presents a comprehensive state of the art of NFV-RA by introducing a novel classification of the main approaches that pose solutions to solve it. This paper also presents the research challenges that are still subject of future investigation in the NFV-RA realm.

关键词： network function virtualization virtual network functions resource allocation NFV orchestration VNF forwarding graph scheduling service chaining and placement

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Congestion Minimization for Service Chain Routing Problems With Path Length Considerations

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IEEE-ACM TRANSACTIONS ON networkING 2020年第6期28卷 2643-2656页

作者： Gao, Lingnan Rouskas, George N. North Carolina State Univ Dept Comp Sci Raleigh NC 27695 USA North Carolina State Univ Raleigh NC 27695 USA King Abdulaziz Univ Dept Comp Sci Jeddah 21589 Saudi Arabia

network function virtualization (NFV), with its perceived potential to accelerate service deployment and to introduce flexibility in service provisioning, has drawn a growing interest from industry and academia alike over the past few years. One of the key challenges in realizing NFV is the service chain routing problem, whereby traffic must be routed so as to traverse the various components of a network service that have been mapped onto the underlying network. In this work, we consider the online service chain routing problem. We route the service chain with the goal of jointly minimizing the maximum network congestion and the number of hops from the source to the destination. To this end, we present a simple yet effective online algorithm in which the routing decision is irrevocably made without prior knowledge of future requests. We prove that our algorithm is O(log m)-competitive in terms of congestion minimization, where m is the number of edges of the underlying network topology, and we show that this ratio is asymptotically optimal.

关键词： Routing Heuristic algorithms Minimization network function virtualization Resource management IEEE transactions Noise measurement network function virtualization virtual network functions NFV orchestration online algorithm resource allocation

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Accurate Prediction of Required virtual Resources via Deep Reinforcement Learning

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IEEE-ACM TRANSACTIONS ON networkING 2023年第2期31卷 920-933页

作者： Huang, Haojun Li, Zhaoxi Tian, Jialin Min, Geyong Miao, Wang Wu, Dapeng Oliver Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Peoples R China Univ Exeter Dept Comp Sci Exeter EX4 4QF Devon England City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

Resource provisioning for the ever-increasing applications to host the necessary network functions necessitates the efficient and accurate prediction of required resources. However, the current efforts fail to leverage the inherent features hidden in network traffic, such as temporal stability, service correlation and periodicity, to predict the required resources in an intelligent manner, incurring coarse-grain prediction accuracies. To tackle this problem, in this paper, we propose an Accurate Prediction of Required virtual Resources (APRR) approach via Deep Reinforcement Learning (DRL). We first confirm the resource requests have more similar features and identify the high-dimensional required resources in computing, storage and bandwidth can be effectively consolidated into a single standardized value. Built upon these observations, we then model the required resources as a time-variant network matrix, which includes a number of elements, obtained from the network measurements, and some missing elements needed to be inferred. To obtain accurately predicted results, DRL-based matrix factorization with a set of available rules has been introduced into APRR and alternately executed in agent to minimize the prediction errors. Moreover, the error-prioritized designed for model training with quicker convergence. Simulation experiments on real-world datasets illustrate that APRR can accurately predict the required virtual resources compared with the related approaches.

关键词： Predictive models Stability analysis Quality of service Correlation Computational modeling Bandwidth Reinforcement learning virtual network functions virtual resources network matrix deep reinforcement learning

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Tabu Search For Service Function Chain Composition In NFV

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IEEE LATIN AMERICA TRANSACTIONS 2021年第1期19卷 17-25页

作者： Gil-Herrera, Juliver Botero, Juan Felipe Fac Ingn Tecnol Antioquia IU Calle 78B 72A-220 Medellin Colombia Univ Antioquia Fac Ingn Calle 67 53-108 Medellin Colombia

One of the main challenges for service providers when they try to deploy network Function virtualization based networks is to efficiently make use of substrate network resources to facilitate the design, delivery and operation of network services in a dynamic and scalable manner. This challenge is known as network Function virtualization Resource Allocation, which can be addressed in two stages called Service Function Chain Composition and Service Function Chain Embedding. The main objective of the first stage is to compose an optimized chain of virtual network functions, which offers a complete network service. The second stage is responsible for the embedding of such network service onto a physical infrastructure based on virtualization technologies. In this paper, we propose a metaheuristic algorithm to face the scalability challenges of exact approaches, when solving the composition stage. The performance of our metaheuristic approach shows that large, complex and heterogeneous service requests can be processed in reasonable running times, finding better results compared with existing heuristics in the state of the art.

关键词： Service function chaining network function virtualization Middleboxes IEEE transactions virtualization Software Silicon compounds communication networks network function virtualization virtual network functions resource allocation

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Algorithms for functionalities of virtual network: a survey

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JOURNAL OF SUPERCOMPUTING 2021年第7期77卷 7368-7439页

作者： Umrao, Brajesh Kumar Yadav, Dharmendra Kumar Motilal Nehru Natl Inst Technol Allahabad Dept Comp Sci & Engn Prayagraj India

Nowadays, network functionalities are provided through dedicated hardware middleboxes. These hardware middleboxes are statically embedded with network topology to provide the network services. The types of middleboxes increase in the network, with an increased number of users and network services. The growth in multiple kinds of middleboxes creates an ossified network that is difficult for network management. network Function virtualization (NFV) deals with such problems by eliminating network functions from the piece of hardware and implement it as a software to execute in a commodity server. The network virtualization provides more flexibility and elasticity in the network to automatically manage and control the network functions. NFV internally implements the different algorithms to automate the on-demand service provisioning mechanism. In the service provisioning process, NFV allows to share the physical resources among the virtual network functions (VNFs). In order to optimally allocate the physical resources to each VNF, there is a need to include some efficient resource sharing mechanisms in NFV infrastructure. These resource sharing mechanisms are the VNF placement, service chaining, VNFs scheduling, and VNF migration. Therefore, this survey discusses the solutions, implementation tools, and limitations with respect to these resource sharing mechanisms. Moreover, research challenges and current trends related to the resource sharing strategies are explored.

关键词： network functions virtualization Provisioning Ossification virtual network functions Placement Scheduling Migration Chaining

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6G⁺ networks Through Enhanced Efficiency and Sustainability With MADDPG-Driven network Slicing in SoS Environments

IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING

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IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND networkING 2024年第4期8卷 1752-1761页

作者： Andreou, Andreas Mavromoustakis, Constandinos X. Univ Nicosia Dept Comp Sci CY-1700 Nicosia Cyprus

This study explores the integration of sustainable practices in the advancing domain of sixth-generation and beyond (6G(+)) network technologies, with a particular focus on enhancing the efficiency of search and rescue operations. It presents a comprehensive strategy for network slicing designed to bolster seamless communication and operational efficacy of emergency response teams in varied and ever-changing conditions. It presents an innovative approach to managing workload fluctuations in network slicing. Also, it introduces a new slice configuration mechanism to prioritize signals for devices within the complex, compelling, hierarchical network systems. Incorporating a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm is central to the approach, tackling the complexity of implementing effective communication strategies across multiple network layers. Our findings demonstrate a highly adaptable and real-time slice configuration technique within System of Systems (SoS) environments, offering significant enhancements in systems engineering and emergency communication management. This approach contributes to the robustness and reliability of emergency response communications and underscores the importance of integrating environmental sustainability in developing next-generation network technologies.

关键词： network slice multi-agent deep deterministic policy gradient system of systems system of systems virtual network functions virtual network functions multi-agent deep reinforcement learning multi-agent deep reinforcement learning virtual network functions multi-agent deep reinforcement learning

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network functions virtualization: The Long Road to Commercial Deployments

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IEEE ACCESS 2019年 7卷 60439-60464页

作者： Rehman, A. U. Aguiar, Rui L. Barraca, Joao Paulo Inst Telecomunicacoes P-3810193 Aveiro Portugal Univ Aveiro Campus Univ Santiago P-3810193 Aveiro Portugal

network operators are under pressure to offer efficient network-based services while keeping service deployment costs to a minimum. network functions virtualization (NFV) can potentially revolutionize network-based services bringing low-deployment costs for network operators. The NFV has been introduced to ultimately extend the non-proprietary and open-standard-based model to network and service deployments, significant improvements to today's proprietary locked implementations. Notwithstanding the continuous efforts of both academia and industry to support the NFV paradigm, the current NFV solutions offered are still in its infancy. In this survey, we provide a detailed background of NFV to establish a comprehensive understanding of the subject, ranging from the basics to more advanced topics. Moreover, we offer a comprehensive overview of the NFV main concepts, standardization efforts, the benefits of NFV, and discussions of the NFV architecture as defined by the European telecommunications standardization institute (ETSI). Furthermore, we discuss the NFV applicability and current open source projects. We then highlight NFV requirements, design considerations, and developmental architectural impairments and barriers to commercial NFV deployments. Finally, we conclude enumerating future directions for NFV development.

关键词： network functions virtualization virtual network functions server virtualization hypervisors containers Unikernels network management and orchestration network programmability network softwarization

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Auto-Scaling network Service Chains Using Machine Learning and Negotiation Game

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IEEE TRANSACTIONS ON network AND SERVICE MANAGEMENT 2020年第3期17卷 1322-1336页

作者： Rahman, Sabidur Ahmed, Tanjila Huynh, Minh Tornatore, Massimo Mukherjee, Biswanath Univ Calif Davis Dept Comp Sci Davis CA 95616 USA Univ Calif Davis Dept Elect & Comp Engn Davis CA 95616 USA AT&T Labs Florham Pk NJ 07932 USA Politecn Milan Dept Elect & Informat I-20133 Milan Italy

network Function virtualization (NFV) enables network Operators (NOs) to efficiently respond to the increasing dynamicity of network services. virtual network functions (VNFs) running on commercial off-the-shelf servers are easy to deploy, update, monitor, and manage. Such virtualized services are often deployed as Service Chains (SCs), which require in-sequence placement of computing and memory resources as well as routing of traffic flows. Due to the ongoing migration towards cloudification of networks, the concept of auto-scaling which originated in Cloud Computing, is now receiving attention from networks professionals too. Prior studies on auto-scaling use measured load to dynamically react to traffic changes. Moreover, they often focus on only one of the resources (e.g., compute only, or network capacity only). In this study, we consider three different resource types: compute, memory, and network bandwidth. In prior studies, NO takes auto-scaling decisions, assuming tenants are always willing to auto-scale, and Quality of Service (QoS) requirements are homogeneous. Our study proposes a negotiation-game-based auto-scaling method where tenants and NO both engage in the auto-scaling decision, based on their willingness to participate, heterogeneous QoS requirements, and financial gain (e.g., cost savings). In addition, we propose a proactive Machine Learning (ML) based prediction method to perform SC auto-scaling in dynamic traffic scenario. Numerical examples show that our proposed SC auto-scaling methods powered by ML present a win-win situation for both NO and tenants (in terms of cost savings).

关键词： Cloud computing Quality of service Prediction methods Bandwidth Prediction algorithms Heuristic algorithms Economics Auto-scaling service chains virtual network functions machine learning negotiation game cost savings QoS resource disaggregation edge datacenters

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Availability-driven NFV orchestration

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COMPUTER networkS 2019年 155卷 47-61页

作者： Casazza, Marco Bouet, Mathieu Secci, Stefano Univ Milan Dipartimento Informat Via Bramante 65 I-26013 Crema Italy Cedric Cnam 292 Rue St Martin F-75003 Paris France Thales 4 Ave Louvresses F-92230 Gennevilliers France

virtual network functions as a Service (VNFaaS) is a promising business whose technical directions consist of providing network functions as a Service instead of delivering standalone network appliances, leveraging a virtualized environment named NFV Infrastructure (NFVI) to provide higher scalability and reduce maintenance costs. Operating the NFVI under stringent availability guarantees is fundamental to ensure the proper functioning of the VNFaaS against software attacks and failures, as well as common physical device failures. Indeed the availability of a VNFaaS relies on the failure rate of its single components, namely the physical servers, the hypervisor, the VNF software, and the communication network. In this paper, we propose a versatile orchestration model able to integrate an elastic VNF protection strategy with the goal to maximize the availability of an NFVI system serving multiple VNF demands. The elasticity derives from (i) the ability to use VNF protection only if needed, or (ii) to pass from dedicated protection scheme to shared VNF protection scheme when needed for a subset of the VNFs, (iii) to integrate traffic split and load-balancing as well as mastership role election in the orchestration decision, (iv) to adjust the placement of VNF masters and slaves based on the availability of the different system and network components involved. We propose a VNF orchestration algorithm based on Variable Neigh-boring Search, able to integrate both protection schemes in a scalable way and capable to scale, while outperforming standard online policies. (C) 2019 Published by Elsevier B.V.

关键词： High Availability NFV virtual network functions NFV Orchestration Variable neighborhood search Greedy heuristic

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