检索结果-内蒙古大学图书馆

Delay-Aware Virtual network function Placement and Routing in Edge Clouds

IEEE TRANSACTIONS ON MOBILE COMPUTING 2021年第2期20卷 445-459页

作者： Yang, Song Li, Fan Trajanovski, Stojan Chen, Xu Wang, Yu Fu, Xiaoming Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China Philips Res NL-5656 AE Eindhoven Netherlands Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Peoples R China Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA Univ Gottingen Inst Comp Sci D-37077 Gottingen Germany

Mobile Edge Computing (MEC) offers a way to shorten the cloud servicing delay by building the small-scale cloud infrastructures at the network edge, which are in close proximity to the end users. Moreover, network function virtualization (NFV) has been an emerging technology that transforms from traditional dedicated hardware implementations to software instances running in a virtualized environment. In NFV, the requested service is implemented by a sequence of Virtual network functions (VNF) that can run on generic servers by leveraging the virtualization technology. Service function Chaining (SFC) is defined as a chain-ordered set of placed VNFs that handles the traffic of the delivery and control of a specific application. NFV therefore allows to allocate network resources in a more scalable and elastic manner, offer a more efficient and agile management and operation mechanism for network functions and hence can largely reduce the overall costs in MEC. In this paper, we study the problem of how to place VNFs on edge and public clouds and route the traffic among adjacent VNF pairs, such that the maximum link load ratio is minimized and each user's requested delay is satisfied. We consider this problem for both totally ordered SFCs and partially ordered SFCs. We prove that this problem is NP-hard, even for the special case when only one VNF is requested. We subsequently propose an efficient randomized rounding approximation algorithm to solve this problem. Extensive simulation results show that the proposed approximation algorithm can achieve close-to-optimal performance in terms of acceptance ratio and maximum link load ratio.

关键词： network function virtualization mobile edge computing delay placement routing

来源：评论

学校读者我要写书评

暂无评论

Reliability-Aware VNF Placement Using a Probability-Based Approach

引用

IEEE TRANSACTIONS ON network AND SERVICE MANAGEMENT 2021年第3期18卷 2478-2491页

作者： Wu, Yunyi Zheng, Weichang Zhang, Yongbing Li, Jie Univ Tsukuba Grad Sch Syst & Informat Engn Tsukuba Ibaraki 3058577 Japan Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China

network function virtualization (NFV) is a new network architecture concept that simplifies the deployment of network services and improves service management. However, it is challenging for a network service provider (NSP) to decide where to place virtual network functions (VNFs). Most previous studies have considered only single-chain services, wherein the VNFs for a request are executed in sequence. In contrast to previous approaches, we consider more general and practical situations in which the VNFs of a request can be executed in parallel and are represented as a forwarding graph. Our objective is to maximize the profits earned by providing network services while satisfying the delay requirements of requests. We formulate the VNF placement problem as an integer linear programming (ILP) problem. Due to the complexity of this problem, we propose a probability-based approach called PBP, in which the placements of the VNFs are determined based on their probabilities of contributing to the profit. Furthermore, we propose a heuristic reliability-aware algorithm to guarantee service reliability, in which each VNF of a request is assigned a backup that can be shared with other requests. Simulation experiments show that PBP achieves a much shorter computation time than previous algorithms while earning higher profit, and furthermore, our reliability-aware algorithm provides the same reliability as a previous algorithm while yielding much higher profit.

关键词： Delays Reliability Hardware Resource management Genetic algorithms virtualization Heuristic algorithms network function virtualization probability-based algorithm VNF forwarding graph reliability resource allocation profit maximization end-to-end delay requirement

来源：评论

学校读者我要写书评

暂无评论

Toward Optimal Partial Parallelization for Service function Chaining

引用

IEEE-ACM TRANSACTIONS ON networkING 2021年第5期29卷 2033-2044页

作者： Lin, I-Chieh Yeh, Yu-Hsuan Lin, Kate Ching-Ju Natl Yang Ming Chiao Tung Univ Dept Comp Sci Hsinchu 30010 Taiwan

The emergence of network function virtualization (NFV) and Service function Chaining (SFC) together enable flexible and agile network management and traffic engineering. Due to the sequential execution nature of SFC, the latency would grow linearly with the number of functions. To resolve this issue, function parallelization has recently been proposed to enable independent functions to work simultaneously. Existing solutions, however, assume all the function instances are installed in the same physical machine and, thus, can be parallelized with only a little overhead. Nowadays, most of the networks deploy function instances in distributed servers for load balancing, parallelization across different servers would, in fact, introduce a non-negligible cost of duplicating or merging packets. Hence, in this work, we propose PPC (Partial Parallel Chaining), which only parallelizes functions if parallelization can indeed reduce the latency after considering function placement and the required additional parallelization cost. To this end, we design two schemes, partial parallelism enumeration and instance assignment to identify the optimal partial parallelism that minimizes the latency. Our simulation results show that PPC effectively adapts the degree of parallelism and, hence, outperforms both sequential chaining and full parallelism in any general scenario. Overall, the latency reduction can be up to 47.2% and 35.2%, respectively, as compared to sequential chaining and full parallelism.

关键词： Parallel processing Noise measurement Servers Routing Service function chaining Merging Delays network function virtualization service function chaining network function parallelization

来源：评论

学校读者我要写书评

暂无评论

Dynamic Service function Chain Deployment and Readjustment Method Based on Deep Reinforcement Learning

引用

SENSORS 2023年第6期23卷 3054页

作者： Ran, Jing Wang, Wenkai Hu, Hefei Beijing Univ Posts & Telecommun Sch Elect Engn Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing 100876 Peoples R China

With the advent of Software Defined network (SDN) and network functions virtualization (NFV), network operators can offer Service function Chain (SFC) flexibly to accommodate the diverse network function (NF) requirements of their users. However, deploying SFCs efficiently on the underlying network in response to dynamic SFC requests poses significant challenges and complexities. This paper proposes a dynamic SFC deployment and readjustment method based on deep Q network (DQN) and M Shortest Path Algorithm (MQDR) to address this problem. We develop a model of the dynamic deployment and readjustment of the SFC problem on the basis of the NFV/SFC network to maximize the request acceptance rate. We transform the problem into a Markov Decision Process (MDP) and further apply Reinforcement Learning (RL) to achieve this goal. In our proposed method (MQDR), we employ two agents that dynamically deploy and readjust SFCs collaboratively to enhance the service request acceptance rate. We reduce the action space for dynamic deployment by applying the M Shortest Path Algorithm (MSPA) and decrease the action space for readjustment from two dimensions to one. By reducing the action space, we decrease the training difficulty and improve the actual training effect of our proposed algorithm. The simulation experiments show that MDQR improves the request acceptance rate by approximately 25% compared with the original DQN algorithm and 9.3% compared with the Load Balancing Shortest Path (LBSP) algorithm.

关键词： network function virtualization service function chain dynamic deployment resource allocation deep reinforcement learning deep Q-networks network readjustment

来源：评论

学校读者我要写书评

暂无评论

Resilient Placement of VNFs and Distributed MANO Components in a WMN-based NFV Infrastructure 26

Resilient Placement of VNFs and Distributed MANO Components ...

引用

26. ITG-Fachtagung Mobilkommunikation: Technologien und Anwendungen - 26th ITG Symposium on Mobile Communication: Technologies and Applications

作者： Frick, Gregor Trick, Ulrich Lehmann, Armin Ghita, Bogdan Research Group for Telecommunication Networks Frankfurt University of Applied Sciences Frankfurt/Main Germany Centre for Security Communications and Network Research University of Plymouth Plymouth United Kingdom

ISBN: (纸本)9783800758739

Wireless Multi-Radio Outdoor-Routers are building a Wireless Mesh network (WMN), which is used as an emergency communication infrastructure in case a disaster destroyed the existing communication system. A dynamic service provisioning shall be achieved by integrating network function Virtualisation (NFV) into the network. A distributed management and orchestration (MANO), which operates in-band of the network, controls the NFV infrastructure (NFVI). The primary goal of the distributed MANO consists of improving the network resilience to changes affecting the NFVI, namely node and link failures. For this aspect, the position of the Virtual network functions (VNFs), implementing a functional service component, as well as the position of the components building the distributed MANO within the WMN-based NFVI is essential. For this purpose, this publication proposes a concept for the resilient placement of the VNFs and distributed MANO components required within the WMN-based NFVI. Furthermore, a mathematical model of the network and an Integer Linear Programming (ILP) based optimisation target for implementing the concept are proposed. The optimisation target considers the connectivity of NFVI-Nodes at the placement by preferring well-connected NFVI-Nodes and avoiding poorly-connected ones. If required due to limited hardware resources, priorities among the different kinds of network functions are considered. Multiple existing solvers are used on the optimisation target to evaluate their performance in terms of their required time for providing an optimal solution based on different network sizes. © 2022 Mobilkommunikation: Technologien und Anwendungen - 26. ITG-Fachtagung

关键词： network function virtualization

来源：评论

学校读者我要写书评

暂无评论

A Comparative Study of Artificial Intelligence and Machine Learning in 5G Innovation 1

A Comparative Study of Artificial Intelligence and Machine L...

引用

1st International Conference on Futuristic Technologies, INCOFT 2022

作者： Srinivasan, S. Singh, Ashutosh Kumar Galgotias University Department of Computer Science and Engineering Uttar Pradesh Greater Noida India Noida Institute of Engineering and Technology Department of Electronics and Communication Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9781665450461

4G/LTE (Long Term Evolution) mobile network deployment has addressed the high capacity problem to establish genuine broadband mobile Internet.5G has overcome some of these difficulties via huge MIMO radio interface. Adopting software-defined networks (SDN) and network function virtualization (NFV) has given flexibility, allowing operators to handle demanding vertical market *** physical layer and flexible network architecture made this feasible. Virtual reality (VR), augmented reality (AR), etc., are bandwidth-intensive. Mobile networks face new applications that require high dependability and near-zero latency, including car communications or Internet-of-Vehicles (IoV).This article describes the role of AI and ML in 5G and beyond to develop a cost-effective, adaptive next-generation mobile network. AI/ML use in network lifecycle is explored. MATLAB mimics these findings. © 2022 IEEE.

关键词： network function virtualization

来源：评论

学校读者我要写书评

暂无评论

A Delay-Aware Approach for Distributed Embedding Towards Cross-Slice Communication 2

A Delay-Aware Approach for Distributed Embedding Towards Cro...

引用

2nd IEEE International Mediterranean Conference on Communications and networking, MeditCom 2022

作者： Dimolitsas, Ioannis Spatharakis, Dimitrios Dechouniotis, Dimitrios Papavassiliou, Symeon School of Electrical and Computer Engineering National Technical University of Athens Athens Greece

ISBN: (数字)9781665498258

ISBN: (纸本)9781665498258

The evolution of modern applications and the adoption of the 5G network slicing architecture require minimizing the communication delays. Virtual network functions (VNFs) offer low latency services to remote users. In the context of Edge Computing, these virtualized services are hosted in Edge Clouds, which, however, have limited resources. Embedding the network Slice requests from users is a challenging problem, and demands distributed solutions in the presence of resource and delay constraints in the Edge network. This paper introduces a distributed delay-aware service discovery mechanism, which performs cache-based forwarding, trying to efficiently discover VNFs in the Edge Clouds (ECs) and enable Cross-Slice Communication (CSC);that is the sharing of common VNFs between different network Slices (NSes). Furthermore, the distributed NS embedding (NSE) problem is addressed by a modified k-Shortest Path (k-SP) approach to minimize the total round-trip network delay under the capacity constraints of the underlying infrastructures. The proposed method significantly decreases the execution time by 70% to provide the distributed NSE solution compared to the baseline k-SP algorithm. © 2022 IEEE.

关键词： network function virtualization

来源：评论

学校读者我要写书评

暂无评论

Follow-me Cloud: An OpenDayLight Implementation for 5G automotive

Follow-me Cloud: An OpenDayLight Implementation for 5G autom...

引用

2022 International Balkan Conference on Communications and networking, BalkanCom 2022

作者： Mendes, Bruno Martins, Diogo Barbosa, Raul Araujo, Marco Capgemini Engineering Porto Portugal

ISBN: (数字)9781665487641

ISBN: (纸本)9781665487641

with the advent of Software Defined networks (SDN) that allow for the near real-time topological change of the telecommunications infrastructure, aided by network function virtualization (NFV) techniques, critical services that demand Ultra Reliable Low Latency Communications (URLLC) can be enforced into an edge Mobile Edge Center (MEC) in order to be processed near the User Equipment (UE). The problem arises when a specific service requires ubiquitous mobility, which is the case of autonomous driving. This paper provides a follow-me cloud solution based on a popular SDN controller, with practical applications. © 2022 IEEE.

关键词： network function virtualization

来源：评论

学校读者我要写书评

暂无评论

Service function Chain Mapping in Fog networks

引用

IEEE COMMUNICATIONS LETTERS 2021年第1期25卷 99-102页

作者： Siasi, Nazli Jasim, Mohammed Ghani, Nasir Christopher Newport Univ Dept Phys Comp Sci & Engn Newport News VA 23606 USA Univ Mt Union Sch Engn Alliance OH 44601 USA Univ S Florida Dept Elect Engn Tampa FL 33620 USA

network function virtualization (NFV) uses commodity servers to run "softwarized" networking device functions and replace costly, proprietary hardware systems. This paradigm is now being used to build and deploy highly-customized service function chains (SFC) for clients. However most related studies have only considered function placement in cloud-based networks. There is now a growing need for NFV service chaining in more specialized fog networks to support users with stringent delay and localized contextual data needs. Hence this letter presents novel fog-based SFC mapping schemes that take into account delay, bandwidth, and node resource parameters.

关键词： network function virtualization service function chaining fog computing

来源：评论

学校读者我要写书评

暂无评论

Combining Deep Reinforcement Learning With Graph Neural networks for Optimal VNF Placement

引用

IEEE COMMUNICATIONS LETTERS 2021年第1期25卷 176-180页

作者： Sun, Penghao Lan, Julong Li, Junfei Guo, Zehua Hu, Yuxiang Natl Digital Switching Syst Engn & Technol Res & Dept Comp Sci Zhengzhou 450001 Peoples R China Beijing Inst Technol Beijing 100811 Peoples R China

network function virtualization (NFV) technology utilizes software to implement network function as virtual instances, which reduces the cost on various middlebox hardware. A Virtual network function (VNF) instance requires multiple resource types in the network (e.g., CPU, memory). Therefore, an efficient VNF placement policy should consider both the resource utilization problem and the Quality of Service (QoS) of flows, which is proved NP-hard. Recent studies employ Deep Reinforcement Learning (DRL) to solve the VNF placement problem, but existing DRL-based solutions cannot generalize well to different topologies. In this letter, we propose to combine the advantage of DRL and Graph Neural network (GNN) to design our VNF placement scheme DeepOpt. Simulation results show that DeepOpt outperforms the state-of-the-art VNF placement schemes and shows a much better generalization ability in different network topologies.

关键词： Deep reinforcement learning network function virtualization graph neural networks software-defined networking

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：