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Proceedings of the 18th International Conference on network and Service Management

作者： Amina Hentati Amin Ebrahimzadeh Roch H. Glitho Fatna Belqasmi Rabeb Mizouni Concordia University Montréal QC Canada Concordia University Montréal QC Canada and University of Western Cape Bellville South Africa Zayed University Dubai United Arab Emirates Khalifa University Abu Dhabi United Arab Emirates

ISBN: (纸本)9783903176515

Remote robotic surgery is one of the most interesting Tactile Internet (TI) applications. It has a huge potential to deliver healthcare services to remote locations. Moreover, it provides better precision and accuracy to diagnose and operate on patients. Remote robotic surgery requires ultra-low latency and ultra-high reliability. The aforementioned stringent requirements do not apply for all the multimodal data traffic (i.e., audio, video, and haptic) triggered during a surgery session. Hence, customizing resource allocation policies according to the different quality-of-service (QoS) requirements is crucial in order to achieve a cost-effective deployment of such system. In this paper, we focus on resource allocation in a softwarized 5G-enabled TI remote robotic surgery system through the use of network functions virtualization (NFV). Specifically, this work is devoted to the joint placement and scheduling of application components in an NFV-based remote robotic surgery system, while considering haptic and video data. The problem is formulated as an integer linear program (ILP). Due to its complexity, we propose a greedy algorithm to solve the developed ILP in a computationally efficient manner. The simulation results show that our proposed algorithm is close to optimal and outperforms the benchmark solutions in terms of cost and admission rate. Furthermore, our results demonstrate that splitting application traffic to multiple VNF-forwarding graphs (VNF-FGs) with different QoS requirements achieves a significant gain in terms of cost and admission rate compared to modeling the whole application traffic with one VNF-FG having the most stringent requirements.

关键词： latency VNF forwarding graph tactile internet network function virtualization virtual network function remote robotic surgery joint placement and scheduling

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Performance Analysis of Nfv-Based Hybrid Systems Via Queueing Models with Setup Time

SSRN

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SSRN 2023年

作者： Sato, Mitsuki Kawamura, Kohei Kawanishi, Ken'ichi Phung-Duc, Tuan Graduate School of Science and Technology Gunma University 1-5-1 Tenjin-cho Gunma Kiryu376-8515 Japan Department of Informatics Gunma University 1-5-1 Tenjin-cho Gunma Kiryu376-8515 Japan Division of Policy and Planning Sciences University of Tsukuba 1-5-1 Tenjin-cho Ibaraki Tsukuba376-8515 Japan

NFV (network functions virtualization) technology widely deployed in recent cloud computing is essential to provide network services. NFV is useful for saving operational cost because VNFs (Virtualized network functions) are created dynamically by scaling in/out virtual machines. On the other hand, utilizing existing computing resources, which are provided by legacy equipments and thus do not possess adaptive property of allocating virtual computing resources, is of importance for maintaining the required performance. In this paper, we propose queueing models of hybrid systems that utilize the NFV technology as well as legacy network equipments. Examples of such systems include 5G networks systems. We focus on a scenario of providing delay-sensitive real-time services, and in particular evaluate the mean and distribution of the delay, which are used for service-level agreements. © 2023, The Authors. All rights reserved.

关键词： network function virtualization

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Uniform Resource Clustering for Service function Chaining in NFV-based Cloud Environment

Uniform Resource Clustering for Service Function Chaining in...

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International Conference for Convergence of Technology (I2CT)

作者： Anshul Mishra Amruta Ayachit Shivani Halemani Sparsha Shetty Narayan D G Somashekar Patil School of Computer Science and Engineering KLE Technological University Hubballi India

network function virtualization (NFV) and software-defined networking (SDN) have had a significant positive influence on the development of technology that mostly resolves the problems with conventional network management systems. Hypervisors enable the virtualization of numerous network functions and services. Virtualized network services such as firewall, load balancer, and network address translator (NAT) along with others can be chained together to form a service chain. A data packet can go through the service chain as per its defined policy and not every available service. Such service function chaining (SFC) policies can be hosted at end devices to conserve a significant amount of energy and time. In this work, we have proposed an approach in which the SFC policies use an adequate amount of network parameters and a technique that uses uniform resource clustering (URC). Two Parameters namely bandwidth and CPU utilization are considered. Our findings indicate that the URC approach saves energy and time significantly. The average network service time with our approach is 9% less than the default approach.

关键词： Cloud computing Virtual machine monitors Service function chaining Firewalls (computing) Bandwidth network function virtualization virtualization

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Truthful auction mechanisms for VNF chain provisioning and allocation across geo-distributed datacenters

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COMPUTER networkS 2022年 217卷

作者： Wang, Xueyi Wang, Xingwei Wu, Dongkuo Ma, Lianbo Huang, Min Northeastern Univ Coll Software Shenyang 110819 Liaoning Peoples R China Northeastern Univ Coll Comp Sci & Engn Shenyang 110819 Liaoning Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China

With the fast development of network function virtualization (NFV), NFV markets are emerging that allows network service providers (NSPs) to trade different virtual network function (VNF) chains among users. Therefore, efficient mechanisms for VNF chain provisioning and allocation are key to guarantee efficient operations of the NFV markets. One fundamental issue is how to maximize the social welfare of the market such that selfish users have incentives to acquire VNF chains from the markets. To overcome this issue, in this paper, we propose a truthful randomized combinatorial auction mechanism (TRCAM), which integrates the primal-dual approximation algorithm design technique and efficient pricing strategy. This mechanism considers dynamic VNF chain provisioning of the NSP and flexible VNF chain deployment of users. To be specific, we first formulate the social welfare maximization problem as an integer linear program, which is proven to be NP-hard. Then, we propose a primal-dual approximate algorithm which is near-optimal with polynomial-time complexity and employ it as a building block to design the TRCAM. Furthermore, we develop a novel binary -search-based algorithm to boost the average-case performance of the TRCAM. Both strict theoretical analysis and extensive experimental studies demonstrate the effectiveness of our proposed TRCAM, which achieves approximate truthfulness, near-optimal social welfare, individual rationality, and computational efficiency.

关键词： Randomized combinatorial auction network function virtualization Dynamic provisioning Flexible deployment Truthfulness

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Fine-Grained Cloud Resource Provisioning for Virtual network function

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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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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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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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ParaGraph: Subgraph-Level network function Composition With Delay Balanced Parallelism

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IEEE ACCESS 2020年 8卷 199308-199322页

作者： Shen, Dian Liu, Haoyang Wang, Rui Dong, Fang Li, Fucun Southeast Univ Sch Comp Sci & Engn Nanjing 210096 Peoples R China Huawei Technol Co Ltd Shenzhen 518129 Peoples R China Univ Wollongong Fac Engn & Informat Sci Wollongong NSW 2522 Australia

Recent efforts in the network function virtualization (NFV) field have targeted ameliorating end-to-end latency of service function chains (SFCs) using network function (NF) composition. NF composition breaks the NF into building blocks and decides the appropriate combination for these blocks. However, two issues remain in current NF composition methods: (1) In the sequential scope, the fine-grained function's block-level composition eliminates redundancy, but with the drawback of flexibility restriction. (2) In the vertical scope, complete NF parallelism adds overhead in packet copying and reordering. To reconcile this, here we present ParaGraph, a subgraph-level NF composition with delay-balanced parallelism. ParaGraph has three main components: an NF subgraph-extraction module that extrapolates right-grained core function subgraphs from NFs;an orchestrator that dynamically composes subgraphs with delay-balanced parallelism;and an infrastructure performing lightweight packet copying and merging. We implement a ParaGraph prototype based on Click and the Data Plane Development Kit (DPDK);extensive evaluations show that with minimum overhead, ParaGraph reaches line-speed packet processing and reduces latency by up to 55% compared to state-of-the-art methods.

关键词： network function virtualization datacenter service function chain network function composition

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Machine Learning Security Algorithms and Framework for IOT System

Machine Learning Security Algorithms and Framework for IOT S...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Megha Mishra Vishnu Kumar Mishra Sunil Tekale T. NagaPraveena Kimidi Parijatha Bhupesh Dewangan Saddamhusian Hadimani Computer Science Engineering Shri Shankaracharya Technical campus CSVTU University Bhilai Chhattisgarh India Computer Science & Engineering Malla Reddy College of Engineering JNTUH University Secunderabad Telangana India Computer Science & Engineering OP Jindal University Raigarh Chhattisgarh India

This paper cover different security issues needs along with attack and vectors attacks and also highlighted current issues and solutions for IoT security for networks are all thoroughly reviewed in this study. Here we cover ML and DL methods for holes in these security solutions that demand strategies are then highlighted. We also go into great detail about the ML and DL technologies that are now being used to solve a range of security harms to different IoT networks. In this study we highlighted the future research prospects for ML (Machine Leraning) and DL (Deep Learning) based IoT security based on the thorough analysis of the already published solutions. This work focused on a brand-new (ML) machine learning based security framework that adapts automatically to the evolving security needs of the IoT domain. For the purpose of reducing various vulnerabilities, this framework formulate utilize of both (NFV) network function virtualization enabler’s and (SDN) Software Defined networking. To accomplish this result we are using supervised learning and their framework that system more secures and distributed data mining system, and neural networks.

关键词： Machine learning algorithms Supervised learning Software algorithms Web servers network function virtualization Security Internet of Things

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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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