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Fine-Grained Cloud Resource Provisioning for Virtual network function

IEEE TRANSACTIONS ON network AND SERVICE MANAGEMENT 2020年第3期17卷 1363-1376页

作者： Yu, Hui Yang, Jiahai Fung, Carol Tsinghua Univ Inst Network Sci & Cyberspace Beijing 100084 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Beijing 100084 Peoples R China Virginia Commonwealth Univ Dept Comp Sci Med Coll Virginia Campus Richmond VA 23284 USA

The deployment of Virtualized network functions is expected to be dynamic and swift when using network function virtualization technology. The dynamic nature of workload from users requires the resource allocation of underlying infrastructure to be flexible to cope with the changes. Existing works investigated elastic NFV solutions by dynamically creating and dismantling Virtual Machine (VM) replicas, while maintaining balanced workload among VMs. However, those solutions are coarse-grained which may cause unnecessary resource over-provisioning as different network functions consume different amount of resources. In this paper, we present ElasticNFV, a dynamic and fine-grained cloud resource provisioning solution for VNF. ElasticNFV takes real-time resource demand of multiple service chains and allocates resources through an elastic provision mechanism. When a scaling conflict occurs, ElasticNFV provides a two-phase minimal migration algorithm to optimize the migration time and embedding cost of VNF instances. We implement ElasticNFV on top of the KVM platform to provide elastic VM for each VNF instance and Open vSwitch to form elastic intra-cloud network with virtual links between VNF instances. Our evaluation results show that ElasticNFV can improve VNF performance significantly, and achieve high resource utilization and fast migration time with low cost.

关键词： Cloud computing Resource management Dynamic scheduling Heuristic algorithms Middleboxes Bandwidth network function virtualization Middlebox network function virtualization (NFV) resource allocation service chain cloud computing resource scaling

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Reconfiguration of Optical-NFV network Architectures Based on Cloud Resource Allocation and QoS Degradation Cost-Aware Prediction Techniques

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IEEE ACCESS 2020年 8卷 200834-200850页

作者： Eramo, Vincenzo Lavacca, Francesco Giacinto Catena, Tiziana Di Giorgio, Flavio Sapienza Univ Rome DIET I-00184 Rome Italy Fdn Ugo Bordoni I-00161 Rome Italy

The high time required for the deployment of cloud resources in network function virtualization network architectures has led to the proposal and investigation of algorithms for predicting traffic or the necessary processing and memory resources. However, it is well known that whatever approach is taken, a prediction error is inevitable. Two types of prediction errors can occur that have a different impact on the increase in network operational costs. In case the predicted values are higher than the real ones, the resource allocation algorithms will allocate more resources than necessary with the consequent introduction of an over-provisioning cost. Conversely, when the predicted values are lower than the real values, the allocation of fewer resources will lead to a degradation of QoS and the introduction of an under-provisioning cost. When over-provisioning and under-provisioning costs are different, most of the prediction algorithms proposed in the literature are not adequate because they are based on minimizing the mean square error or symmetric cost functions. For this reason we propose and investigate a forecasting methodology in which it is introduced an asymmetric cost function capable of weighing the costs of over-provisioning and under-provisioning differently. We have applied the proposed forecasting methodology for resource allocation in a network function virtualization architectures where the network function virtualization Infrastructure Point-of-Presences are interconnected by an elastic optical network. We have verified a cost savings of 40% compared to solutions that provide a minimization of the mean square error.

关键词： network function virtualization computing resources bandwidth resources elastic optical networks

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Joint Diversity and Redundancy for Resilient Service Chain Provisioning

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2020年第7期38卷 1490-1504页

作者： Alleg, Abdelhamid Ahmed, Toufik Mosbah, Mohamed Boutaba, Raouf Univ Bordeaux Bordeaux INP CNRS LaBRIUMR5800 F-33076 Bordeaux France Univ Waterloo David R Cheriton Sch Comp Sci Waterloo ON N2L 3G1 Canada

Achieving network resiliency in terms of availability, reliability and fault tolerance is a central concern for network designers and operators to achieve business continuity and increase productivity. It is particularly challenging in increasingly virtualized network environments where network services are exposed to both hardware (e.g., bare-metal servers, switches, links, etc.) and software (VNF instances) failures. This increased risk of failures can severely deteriorate the quality of the deployed services and even lead to complete service outages. In this context, deploying services in operational networks often exacerbates the availability problem and requires considering availability of hardware and software components both individually and collectively. A key challenge in this perspective is the additional resources needed to achieve partial or full recovery after failures. In this paper, we propose a joint selective diversity and tailored redundancy mechanism to provision resilient services in an NFV framework. Diversity splits a single VNF into a pool of "N" active instances called replicas while redundancy provides "P" standby ready-to-use instances called backups. Based on an enhanced N+P model, we propose a placement solution of Service function Chains (SFC) modeled as a Mixed Integer Linear Program (MILP). The proposed solution is designed to meet a target SFC availability level and, at the same time, to reduce the inherent cost due to diversity (overhead) and redundancy (backup resources). We evaluate the efficiency of the proposed solution through numerically and experimentally. Results demonstrate that our solution, not only, improves service resiliency by avoiding complete service outages but can also overcome network resource fragmentation.

关键词： Redundancy Resilience Hardware Software Middleboxes network function virtualization network function virtualization network resilience redundancy network reliability placement and chaining network diversity

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ReLU Neural networks with Linear Layers are Biased Towards Single- and Multi-Index Models

arXiv

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arXiv 2023年

作者： Parkinson, Suzanna Ongie, Greg Willett, Rebecca Committee on Computational and Applied Mathematics University of Chicago ChicagoIL United States Department of Mathematical and Statistical Sciences Marquette University MilwaukeeWI United States Department of Statistics Department of Computer Science Committee on Computational and Applied Mathematics University of Chicago ChicagoIL United States

Neural networks often operate in the overparameterized regime, in which there are far more parameters than training samples, allowing the training data to be fit perfectly. That is, training the network effectively learns an interpolating function, and properties of the interpolant affect predictions the network will make on new samples. This manuscript explores how properties of such functions learned by neural networks of depth greater than two layers. Our framework considers a family of networks of varying depths that all have the same capacity but different representation costs. The representation cost of a function induced by a neural network architecture is the minimum sum of squared weights needed for the network to represent the function;it reflects the function space bias associated with the architecture. Our results show that adding additional linear layers to the input side of a shallow ReLU network yields a representation cost favoring functions with low mixed variation – that is, it has limited variation in directions orthogonal to a low-dimensional subspace and can be well approximated by a single- or multi-index model. This bias occurs because minimizing the sum of squared weights of the linear layers is equivalent to minimizing a low-rank promoting Schatten quasi-norm of a single "virtual" weight matrix. Our experiments confirm this behavior in standard network training regimes. They additionally show that linear layers can improve generalization and the learned network is well-aligned with the true latent low-dimensional linear subspace when data is generated using a multi-index model. Copyright © 2023, The Authors. All rights reserved.

关键词： network function virtualization

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Supervised Machine Learning for Cloud Security

Supervised Machine Learning for Cloud Security

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International Conference on Information Systems and Computer networks (ISCON)

作者： Saurabh Singhal Rishabh Srivastava R Shyam Deepak Mangal Department of CEA GLA University Mathura Uttar Pradesh India Department of CSE(AIML& Cyber Security) Jain University Bengaluru India Department of CS & IT Jain University Bengaluru India

Although there is a lot of interest in cloud computing, security concerns have prevented it from becoming mainstream. Users of cloud services frequently worry about the loss of data, the compromise of security, and the unavailability of the services at important moments. Security applications that employ learning-based solutions are gaining attraction in the literature thanks to recent advancements. However, the most challenging aspect of these approaches is the objective datasets. Numerous internal datasets are off-limits for public usage for various reasons, including privacy and the possibility of missing statistical information. Even though there is some lacking, researchers are using these datasets for training and testing in experimental settings. Using a single dataset to train a machine learning model often produces misleading findings. How well these models perform when applied to data from a variety of sources and contexts is an open question, although it hasn’t been thoroughly explored in the literature. As, cloud problems are unique, therefore it is crucial to evaluate the performance of these models over a wide range of circumstances. To train the supervised machine learning models used in this research, we make use of the dataset made available by UNSW. For evaluating the performance of these models, we have used the ISOT dataset.

关键词： Training Service function chaining Computational modeling Cloud computing security Machine learning Data models network function virtualization

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A joint orchestration of security and functionality services at network edge

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COMPUTER networkS 2022年 212卷 1页

作者： Qin, Yudong Guo, Deke Luo, Lailong Xu, Ming Natl Univ Def Technol Coll Comp Sci & Technol Changsha Peoples R China Natl Univ Def Technol Sci & Technol Lab Informat Syst Engn Changsha Peoples R China Natl Univ Def Technol State Key Lab High Performance Comp Changsha Peoples R China

Edge computing emerges as a new paradigm to provide low-latency network services in close proximity to end users. Based on the network function virtualization (NFV) technology, network services can be flexibly and scalably provisioned as virtual network function (VNF) chains deployed at edge servers. With such advantages, both the industry and research communities have done extensive studies on deploying VNF chains at network edge. The existing works mainly take an ideal assumption that the network is totally safe and there are no malicious users. Therefore, they leverage all available resources to serve their users. However, such an assumption is impractical in real networks. Security services, such as firewall, deep packet detection, intrusion detection, are always required for production networks. The existing service deployment methods fail to consider the co-existence of security services and functionality services. In this paper, we present the topic of joint deployment of both security and functionality services, wherein the security services are responsible to check the data flows before being processed by the functionality services. To solve this problem, we propose the Secure Deployment Pattern, which aims to simultaneously satisfy the security protection and QoS requirements at network edge. It divides the services into two kinds, i.e., the user-oriented functionality services, and the service provider-oriented security services. In this case, it is very challenging to jointly deploy the security services and functional services with respect to the resource and latency constraints. We formulate this problem as an integer programming model, and propose the heuristic algorithms to solve it. As far as we know, this paper is the first step, which targets at a proper orchestration of security and functionality services in edge computing. Extensive evaluations show that the proposed algorithms are effective and efficient, in terms of the execution time and the

关键词： Edge computing network function virtualization network security

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A Zero Touch Emulation Framework for network Slicing Management in a 5G Core Testbed 17

A Zero Touch Emulation Framework for Network Slicing Managem...

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17th International Conference on network and Service Management, CNSM 2021

作者： Vittal, Shwetha Sarkar, Sourav P S, Prashanth Franklin, Antony Indian Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9783903176362

network slicing is one of the core features of the 5G network to meet the requirements of various network services, namely, enhanced Mobile Broadband (eMBB), ultra Reliable Low Latency Communications (uRLLC), and massive Machine Type Communication (mMTC) by building an isolated virtual network of resources typically in a network function virtualization (NFV) environment. We have built a 3GPP compliant 5G Core (5GC) testbed and enabled network slicing on it to provide eMBB, uRLLC, and mMTC services to end users. In this demonstration, we showcase the capabilities of our zero-touch framework of 5GC network slicing management and orchestration in an NFV environment with the network functions of 5GC and the applications on eMBB and uRLLC slice services. We believe that this work will guide Mobile network Operator (MNO)s in building zero-touch autonomous network slices in 5GC along with their smart management and orchestration in closed loop automation. © 2021 IFIP.

关键词： network function virtualization

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Neural network-Based Traffic Prediction Model with Adaptive Spatial-Temporal Analysis in NFV networks 21

Neural Network-Based Traffic Prediction Model with Adaptive ...

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21st IEEE International Conference on Communication Technology, ICCT 2021

作者： Yu, Fenglei Xu, Zhanqi Yang, Fan Du, Shuang School of Telecomm. Eng Xidian University Xi'an China

ISBN: (纸本)9781665432061

network function virtualization (NFV) aims at satisfying various performance requirements of network services and increasing the performance/cost ratio mostly by decoupling software and hardware. However, network operators in the NFV scenario are facing several challenges in allocating resources and reducing operating expenses due to traffic explosion, and traffic prediction is helpful for operators to tackle this issue. The available models only consider the spatial or temporal correlation among traffic flows, but fail to combine the two aspects. In this paper, a neural network-based model with adaptive spatial-temporal analysis is proposed to predict the future traffic in NFV networks. First, we use the Long Short-Term Memory (LSTM) model to predict the traffic matrixes by capturing the long-term dependency. Then, we put forward a new mechanism to consider the spatial-temporal correlation between the target flow and the other flows in each traffic matrix. Besides, we adaptively select the most relevant flows, which are used to modify the prediction result through the Back Propagation Neural network (BPNN). The experiments show that the proposed model can significantly improve the prediction accuracy compared to the available work. © 2021 IEEE.

关键词： network function virtualization

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Research of Power Communication network System Architecture Based on 5G 4

Research of Power Communication Network System Architecture ...

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4th IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2021

作者： Liu, Shan Tang, Baoguo Tian, Yun State Grid Corporation of China Shanxi Electric Power Research Institute Taiyuan China

ISBN: (纸本)9781728185347

the wide application of 5G technology is changing our production and life. Power grid enterprise is regarded as the basic support for the development of the national economy. The power grid is important. Power communication network is the foundation of power grid. Based on the current situation of power communication network and the advantages of 5G technology, this paper put forward the system architecture of power communication network The system architecture adopt network slice technology. This architecture can meet the differentiated needs of power enterprises. And this architecture can make full use of network resources. This approach has a certain reference value. © 2021 IEEE.

关键词： network function virtualization

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Optimal Service Provisioning Schemes for Multi-Layer Fog networks

Optimal Service Provisioning Schemes for Multi-Layer Fog Net...

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2021 IEEE Globecom Workshops, GC Wkshps 2021

作者： Riedl, Anton Siasi, Nazli Christopher Newport University Department of Physics Computer Science and Engineering Newport NewsVA United States

ISBN: (纸本)9781665423908

The deployment of network function virtualization (NFV) in fog networks allows network providers to offer dynamic, delay-sensitive services on top of general purpose hardware. A key challenge is the provisioning of service function chaining (SFC), i.e., the placement of requested virtual network functions (VNF) across the nodes, in ways that make efficient use of the sometimes limited resources while meeting stringent quality of service (QoS) *** this paper we propose a novel optimization scheme for placing VNFs in the network and we apply it to a multi-layer fog architecture composed of heterogeneous global and local fog nodes. The algorithm is based on integer linear programming (ILP) and gives particular consideration to multi-mapping and instance-sharing. Results show that SFC with VNF multi-mapping and instance-sharing generally yields the best resource utilization, along with achieving short delays and high admission rates. © 2021 IEEE.

关键词： network function virtualization

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