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检索条件"主题词=Virtual network functions"

共 106 条记录，以下是71-80 订阅

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Towards Latency-aware vNF Placement on Heterogeneous Hosts at the network Edge

Towards Latency-aware vNF Placement on Heterogeneous Hosts a...

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IEEE Conference on Global Communications (IEEE GLOBECOM) - Intelligent Communications for Shared Prosperity

作者： Adoga, Haruna Umar Pezaros, Dimitrios P. Univ Glasgow Sch Comp Sci Glasgow G12 8QQ Scotland

ISBN: (纸本)9798350310900

In this paper, we investigate the optimal placement of vNFs over the distributed edge network while considering the heterogeneity of packet processing elements. Unlike previous efforts in this domain, our proposed hybrid placement scheme places vNFs on user space hosts and the network data plane to efficiently minimise end-to-end path latency and achieve faster service delivery. We formulate and solve the Hybrid vNF Placement Problem as a linear integer programming (ILP) problem and propose a hybrid placement heuristic algorithm (HYPHA), which performs an incremental placement of vNFs on heterogeneous hosts to find a near-optimal solution to the problem quickly. We evaluated our proposed solution using a simulation of real-world network topology with realistic latency conditions. We show that leveraging the fast packet processing speed of the network data plane in conjunction with abundant user space resources for vNF placement yields minimal overall end-to-end latency and fulfils the placement of a diverse set of vNF requests to speed up service delivery. Also, our results show that HYPHA can obtain near-optimal vNF mapping while incurring fewer latency threshold violations set by network operators.

关键词： Latency minimisation heterogeneous data plane Hybrid VNF Placement network Function virtualization Edge network network Softwarization virtual network functions

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Development of network Security Models in the Software-defined Infrastructure of virtual Data Center

Development of Network Security Models in the Software-defin...

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IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus)

作者： Bolodurina, Irina P. Parfenov, Denis I. Orenburg State Univ Dept Appl Math Orenburg Russia Orenburg State Univ Fac Distance Learning Technol Orenburg Russia

ISBN: (纸本)9781538643402

The novelty of the research is the combination of technologies of software-defined networks and virtualization of network functions for optimization network organization in virtual data centers. We was prepared approaches for solving tasks for describing firewall rules and the rules for selecting nodes of the physical infrastructure for the placement of security networks elements with using neural network and Data Mining methods. Our solutions will provide the required level of security and maintain a specified quality of service for applications and services of multi-cloud platforms. This approach was combined with efficient means for processing Big Data flows. It allows predicting infrastructure behavior over time and warning of possible failures, security threats and cyber attacks. The uniqueness our technology is that resource management that provides security and quality of service is carried out autonomously, using the principles of self-organization, without interference from administrators and third parties.

关键词： multi-cloud platforms software-defined networks software-defined infrastructure virtual data center virtual network functions

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An Automated VNF Manager based on Parameterized Action MDP and Reinforcement Learning

An Automated VNF Manager based on Parameterized Action MDP a...

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IEEE International Conference on Communications (ICC)

作者： Li, Xinrui Samaan, Nancy Karmouch, Ahmed Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON Canada

ISBN: (纸本)9781728171227

Managing and orchestrating the behaviour of virtual network functions (VNFs) remains a major challenge due to their heterogeneity and the ever-increasing resource demands of the served flows. In this paper, we propose a novel VNF manager (VNFM) architecture to automate the process of selecting appropriate VNF management actions (e.g., migration and vertical and horizontal scaling) with their corresponding configuration parameters (e.g., migration location or amount of resources needed for scaling). More precisely, we first propose a novel Markov decision process with parameterized actions to accurately describe each VNF and its permissible lifecycle management (LCM) operations. The use of parameterized actions allows us to rigorously represent the functionalities of the VNFM in order perform various operations on the VNFs. Next, we propose a two-stage reinforcement learning (RL) scheme that alternates between learning optimal LCM actions and updating their parameters selection policy. In contrast to existing schemes, the proposed work uniquely provides a holistic management platform that unifies individual efforts targeting single LCM functions such as VNF placement and scaling. Performance evaluation results demonstrate the efficiency of the proposed VNFM in maintaining the required performance level of the VNF while optimizing its resource configurations.

关键词： virtual network functions Markov decision process reinforcement learning

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Service-Aware virtual network Function Migration Based on Deep Reinforcement Learning 16th

Service-Aware Virtual Network Function Migration Based on De...

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16th European-Alliance-for-Innovation (EAI) International Conference on Communications and networking in China (ChinaCom)

作者： Li, Zeming Liu, Ziyu Liang, Chengchao Liu, Zhanjun Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing Peoples R China

ISBN: (纸本)9783030992002;9783030991999

network Function virtualization (NFV) aims to provide a way to build agile and flexible networks by building a new paradigm of provisioning network services where network functions are virtualized as virtual network functions (VNFs). network services are implemented by service function chains, which are formed by a series of VNFs with a specific traversal order. VNF migration is a critical procedure to reconfigure VNFs for providing better network services. However, the migration of VNFs for dynamic service requests is a key challenge. Most VNF migration works mainly focused on static threshold trigger mechanism which will cause frequent migration. Therefore, we propose a novel mechanism to solve the issue in this paper. With the objective of minimizing migration overhead, a stochastic optimization problem based on Markov decision process is formulated. Moreover, we prove the NP-hardness of the problem and propose a service-aware VNF migration scheme based on deep reinforcement learning. Extensive simulations are conducted that the proposed scheme can effectively avoid frequent migration and reduce the migration overhead.

关键词： network Function virtualization virtual network functions Markov decision process Migration Deep reinforcement learning

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On Congestion Minimization for Service Chain Routing Problems

On Congestion Minimization for Service Chain Routing Problem...

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IEEE International Conference on Communications (IEEE ICC)

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

ISBN: (纸本)9781538680889

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 with the goal of minimizing the maximum network congestion. 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, where m is the number of edges of the underlying network topology, and we show that this ratio is asymptotically optimal.

关键词： network Function virtualization virtual network functions NFV Orchestration Online Algorithm Resource Allocation

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Minimizing Delay in network Function Visualization with Quantum Computing 18

Minimizing Delay in Network Function Visualization with Quan...

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18th IEEE International Conference on Mobile Ad hoc and Smart Systems (IEEE MASS)

作者： Xuan, Wenlu Zhao, Zhongqi Fan, Lei Han, Zhu Univ Houston Dept Elect & Comp Engn Houston TX 77004 USA Univ Houston Dept Engn Technol Houston TX 77004 USA

ISBN: (纸本)9781665449359

network function virtualization (NFV) is a crucial technology for the 5G network development because it can improve the flexibility of employing hardware and reduce the construction of base stations. There are vast service chains in NFV to meet users' requests, which are composed of a sequence of network functions. These virtual network functions (VNFs) are implemented in virtual machines by software and virtual environment. How to deploy VMs to process VNFs of the service chains as soon as possible when users' requests are received is very challenging to solve by traditional algorithms on a large scale. Compared with traditional algorithms, quantum computing has better computational performance because of quantum parallelism. We build an integer linear programming model of the VNF scheduling problem with the objective of minimizing delays, and transfer it into the quadratic unconstrained binary optimization (QUBO) model. Our proposed heuristic algorithm employs a quantum annealer to solve the model. Finally, we evaluate the computational results and explore the feasibility of leveraging quantum computing to solve the VNF scheduling problem.

关键词： network function virtualization virtual network functions quantum computing scheduling problem delay

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Deep Reinforcement Learning for Demand-Aware Joint VNF Placement-and-Routing

Deep Reinforcement Learning for Demand-Aware Joint VNF Place...

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IEEE Global Communications Conference (IEEE GLOBECOM)

作者： Wang, Shaoyang Lv, Tiejun Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Minist Educ Key Lab Trustworthy Distributed Comp & Serv Beijing 100876 Peoples R China

ISBN: (纸本)9781728109602

One main challenge facing the deployment of the network function virtualization is the resource allocation on demand. As the primary stage of the resource allocation, the effective virtual network function (VNF) placement-and-routing (P&R) is particularly difficult. To this end, we propose a deep reinforcement learning (DRL)-based approach for solving the joint VNF P&R problem subject to diverse service demands. Our approach exhibits strong stability and excellent load balancing ability. It can also effectively counter the non-uniformity of P&R policies. We first introduce the service request model, and formulate the VNF P&R problem, while taking both the resource consumption cost and service delay into the optimization objective. A demand-aware factor is also employed to characterize the diversity of service demands. In the considered problem, the DRL-based P&R scheme is presented, invoking the deep deterministic policy gradient as the main learning algorithm. According to the results of extensive experiments made on the realistic "COST266" network topology, our scheme can approach the optimum, and is superior to existing heuristic algorithms.

关键词： network functions virtualization resource allocation virtual network functions placement-and-routing deep reinforcement learning

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A Novel Resource Reliability-Aware Infrastructure Manager for Containerized network functions

A Novel Resource Reliability-Aware Infrastructure Manager fo...

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IEEE International Conference on Communications (ICC)

作者： Huang, Zhuonan Samaan, Nancy Karmouch, Ahmed Univ Ottawa Sch Elect & Comp Engn Ottawa ON Canada

ISBN: (纸本)9781728171227

A major challenge to the anticipated large scale deployment of virtual network functions (VNFs) at the network edge (NE) is the ability to efficiently allocate and manage its scarce resources to meet these functions workload fluctuations. In this paper, we describe a novel containerized infrastructure manager (cIM) that extends current managers, such as Kubernetes, with the necessary building blocks to provide an accurate resource allocation service to containerized VNFs at scale. The proposed cIM treats the containerized VNF components (cNFCs), as atomic special purpose functions that can be rapidly deployed to form complex network services. The main component of the proposed cIM, the resource reservation manager (RRM), employs concepts of risk pooling in the insurance industry to accurately reserve the needed resources for the hosting containers and meet anticipated cNFCs demand fluctuation. The reserved quota of resources ensures the desired availability level of the cNFCs without over-provisioning the scarce resources of the NE. Experimental results demonstrate that our proposed cIM significantly improve the performance of the cNFCs and guarantees their availability with minimal resource reservation.

关键词： virtual network functions containers network edge resource reservation statistical modelling

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A Real-time Simulation Framework for Complex and Large-scale Optical Transport networks based on the SDN Paradigm 24

A Real-time Simulation Framework for Complex and Large-scale...

引用

IEEE/ACM 24th International Symposium on Distributed Simulation and Real Time Applications (DS-RT)

作者： Shah, A. A. Mussini, M. Nicassio, F. Parladori, G. Triggiani, E. Grieco, G. Iaffaldano, G. Piro, G. Politecn Bari DEI V Orabona 4 I-70125 Bari Italy Consorzio Nazl Interuniv Telecomunicaz CNIT Genoa Italy Experis Srl V Kennedy 34 I-20871 Vimercate Italy SM Opt Srl V P Castaldi 8 I-20124 Milan Italy

ISBN: (纸本)9781728173436

Thanks to the recent advancements in the Software-Defined networking (SDN) and network Function virtualization research domains, telecom operators are encouraged to upgrade their optical transport networks towards programmable, energy-efficient, service-oriented, and interoperable architectures. The availability of a large set of open-source building blocks, supported by different standardization bodies makes the selection and the integration of such technologies a very complex task. In this context, the INTENTO project has the objective to create an innovative simulation framework by selecting the best technologies and use it to test applications, services, and advanced optimization algorithms in a real environment. In the initial phase, the project designed a large-scale, distributed, and hierarchical Transport SDN architecture, where optical switches and networking functionalities are monitored and dynamically configured through a two-level structure of SDN controllers. On top of that, virtual network functions are optimally deployed and managed by a centralized orchestrator, based on network condition, user requests, and application requirements. Based on this architecture, the project team started to develop a complex simulation environment that harmoniously integrates within the OpenStack cloud: optical node simulators composed by simulation agent and a suitable hardware emulation layer;proprietary SDN network controller designed to enable the innovative optical nodes characteristics;Open network Operating System as the second level controller, enabling the integration of third-party or standardized models (multivendor environment), based on standardized interfaces and communication protocols. After having described the main components and functionalities already implemented into the simulation framework, the paper concludes by highlighting future research and development activities.

关键词： Optical Transport networks Software-Defined networking virtual network functions Simulation Framework

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Declarative Provisioning of virtual network Function Chains in Intent-based networks 9

Declarative Provisioning of Virtual Network Function Chains ...

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9th IEEE International Conference on network Softwarization (IEEE NetSoft) - Boosting Future networks through Advanced Softwarization

作者： Massa, Jacopo Forti, Stefano Paganelli, Federica Dazzi, Patrizio Brogi, Antonio Univ Pisa Dept Comp Sci Pisa Italy

ISBN: (纸本)9798350399806

Intent-based networking (IBN) aims at simplifying network configuration and management by using high-level objectives that express the desired state of the network rather than the details of how to implement it. In this article, we propose a declarative methodology and an associated open-source Prolog prototype (i) to model IBN intents related to the provisioning of virtual network Function (VNF) chains, and (ii) to process those intents to assemble and place a VNF chain that fulfils them. Our prototype is assessed over a lifelike motivating scenario.

关键词： intent-based networking declarative programming virtual network functions cloud-edge continuum

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