networkfunctions Virtualization (NFV) revolutionizes network services by eliminating the need for dedicated hardware. This virtualization enables flexible and efficient deployment of various networkfunctions like pr...
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networkfunctions Virtualization (NFV) revolutionizes network services by eliminating the need for dedicated hardware. This virtualization enables flexible and efficient deployment of various networkfunctions like proxies, firewalls, and load balancers. Providing the service requested by the user in the network is done by a sequence of virtual networkfunctions, which are known as service functions chain. One of the main challenges in the development of networkfunctions virtualization architecture is the allocation of resources to the requested network services in network infrastructures, this challenge is called network function virtualization resource allocation problem. Therefore, this paper addresses the resource allocation problem in networkfunctions Virtualization (NFV) architectures using mathematical programming techniques. A multi-objective mixed-integer linear programming (MILP) model is proposed to optimize resource allocation for virtual networkfunctions (VNFs). The model incorporates constraints related to node and link resource capacities, as well as delay requirements. The objective functions focus on maximizing network throughput, minimizing node resource costs (CPU cores and memory), reducing capital and operational expenses, and ensuring efficient execution time. These constraints and objective functions are formally defined by mathematical functions. The proposed mathematical model is implemented and solved using the Cplex solver. To evaluate the effectiveness of the proposed mathematical model, various network topologies were evaluated under different parameters. These parameters included the length of Service Function Chains (SFCs), the number and length of flows, node resource capacities, the number of nodes and VNFs. The experimental results demonstrated the model's ability to efficiently allocate resources to VNFs across these different scenarios.
Offering virtualized network functions (VNFs) as a service requires automation of cloud resource management to allocate cloud resources for the VNFs dynamically. Most of the existing solutions focus only on the initia...
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ISBN:
(纸本)9783903176157
Offering virtualized network functions (VNFs) as a service requires automation of cloud resource management to allocate cloud resources for the VNFs dynamically. Most of the existing solutions focus only on the initial resource allocation. However, the allocation of resources must adapt to dynamic traffic demands and support fast scaling mechanisms. There are three basic scaling models: vertical where re-scaling is achieved by changing the resources assigned to the VNF in the host server, horizontal where VNFs are replicated or removed to do rescaling, and migration where VNFs are moved to servers with more resources. In this paper, we present an Iterated Local Search (ILS) based framework for automation of resource reallocation that supports the three scaling models. We, then, use the framework to run experiments and compare the different scaling approaches, specifically how the optimization is affected by the scaling approach and the optimization objectives.
Emerging technologies such as network function virtualization and software-defined networking (SDN) have made a phenomenal breakthrough in network management by introducing softwarization. The provision of assets to e...
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Emerging technologies such as network function virtualization and software-defined networking (SDN) have made a phenomenal breakthrough in network management by introducing softwarization. The provision of assets to each virtualized network functions autonomously as well as efficiently and searching for an optimal pattern for traffic routing challenges are still under consideration. Unfortunately, the traditional methods for estimating the desired performance indicators are insufficient for a self-driven SDN. In the last decade, a combination of machine learning and cognitive techniques construct a knowledge plane (KP) for the Internet which introduces numerous benefits to networking, like automation and recommendation. Furthermore, the inclusion of KP to the conventional three planes SDN architectures recently has added another knowledge defined networking (KDN) architecture to drive an SDN autonomously. In this article, a self-driving system has been proposed based on KDN to achieve the selection of an optimal path for the deployment of service function chaining (SFC) and reactive traffic routing among the edge clouds. Considering the limited resource of edge clouds, the proposed system also maintains a balance among edge cloud resources while orchestrating SFC resources. The graph neural network has been also applied in the proposed system to recognize the composite relationship concerning topology, traffic features, and routing patterns for accurate estimation of key performance indicators. The proposed system improves resource utilization efficiency for SFC deployment by 20%, maximum network throughput by 5%, and CPU load by 13%.
network function virtualization (NFV) is a rapidly growing technology that permits network operators to issue their virtualized network functions (VNFs) with cheaper commodity servers. There are various VNFs, namely f...
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network function virtualization (NFV) is a rapidly growing technology that permits network operators to issue their virtualized network functions (VNFs) with cheaper commodity servers. There are various VNFs, namely firewalls, switches, and virtual routers. However, detecting denial of service (DDoS) attacked VNFs is challenging as VNF behaviors are complicated and dynamic due to network traffic in the cloud. Therefore, the proposed work implemented an intrusion detection system (IDS) to detect DDoS attacks in the network. The proposed IDS is named a wrapper feature selection-based hybrid deep learning model (WF-HDL). The DDoS detection model undergoes three stages: pre-processing, feature selection, and detection. The pre-processing is achieved by the z-score normalization technique, followed by a wrapper-based feature selection achieved using the Pelican optimization algorithm (POA). Finally, the DDoS attacks are detected using deep auto-encoder-convolutional gated recurrent unit (DAE-CGRU). The proposed model detected the network's normal and attacked VNF behaviors more accurately. It can train different kinds of VNF behaviour models. In the proposed work, two VNF models, a virtual firewall and a virtual router are trained using a CIC-DDoS2019 dataset. The proposed attack detection model achieves high accuracy at 99.69%, precision at 99.03%, recall at 99.07%, f1-score at 99.05%, and receiver operating characteristic curve (ROC curve) at 99.85%.
Despite the many advantages of network Function Virtualization (NFV) technology, the dependability of virtual services must be carefully addressed so that NFV can meet the requirements of commercial carriers. In parti...
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ISBN:
(纸本)9781450397377
Despite the many advantages of network Function Virtualization (NFV) technology, the dependability of virtual services must be carefully addressed so that NFV can meet the requirements of commercial carriers. In particular, it is essential to provide mechanisms to ensure their correct and continuous operation. In this work we propose NHAM: an NFV High Availability Module designed within the NFV-MANO (NFV Management and Orchestration) reference model. NHAM allows the creation and management of fault-tolerant virtual network services consisting of stateful VNFs (virtualized network functions) and SFCs (Service Function Chains). The proposed architecture provides fault management, including a choice of recovery mechanisms that can be applied depending on the specific needs of each service. The solution is holistic in the sense that it does not require any modifications of the source code of VNFs/SFCs to make them fault-tolerant. The strategy is based on SFC buffer management coupled with VNF checkpoint/restore, providing high availability in a transparent way. A prototype was implemented and experimental results are presented.
It is anticipated that future networks support networkfunctions, such as firewalls, load balancers and intrusion prevention systems in a fully automated, flexible, and efficient manner. In cloud computing environment...
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It is anticipated that future networks support networkfunctions, such as firewalls, load balancers and intrusion prevention systems in a fully automated, flexible, and efficient manner. In cloud computing environments, networkfunctions virtualization (NFV) aims to reduce cost and simplify operations of such network services through the virtualization technologies. To enforce network policies in NFV-based cloud environments, network services are composed of virtualized network functions (VNFs) that are chained together as service function chains (SFCs). All network traffic matching a policy must traverse networkfunctions in the chain in a sequence to comply with it. While SFC has drawn considerable attention, relatively little has been given to dynamic auto-scaling of VNF resources in the service chain. Moreover, most of the existing approaches focus only on allocating computing and network resources to VNFs without considering the quality of service requirements of the service chain such as end-to-end latency. Therefore, in this paper, we define a unified framework for building elastic service chains. We propose a dynamic auto-scaling algorithm called ElasticSFC to minimize the cost while meeting the end-to-end latency of the service chain. The experimental results show that our proposed algorithm can reduce the cost of SFC deployment and SLA violation significantly. (C) 2019 Elsevier Inc. All rights reserved.
Despite the many advantages of network Function Virtualization (NFV) technology, the dependability of virtual services must be carefully addressed so that NFV can meet the requirements of commercial carriers. In parti...
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ISBN:
(纸本)9781450397377
Despite the many advantages of network Function Virtualization (NFV) technology, the dependability of virtual services must be carefully addressed so that NFV can meet the requirements of commercial carriers. In particular, it is essential to provide mechanisms to ensure their correct and continuous operation. In this work we propose NHAM: an NFV High Availability Module designed within the NFV-MANO (NFV Management and Orchestration) reference model. NHAM allows the creation and management of fault-tolerant virtual network services consisting of stateful VNFs (virtualized network functions) and SFCs (Service Function Chains). The proposed architecture provides fault management, including a choice of recovery mechanisms that can be applied depending on the specific needs of each service. The solution is holistic in the sense that it does not require any modifications of the source code of VNFs/SFCs to make them fault-tolerant. The strategy is based on SFC buffer management coupled with VNF checkpoint/restore, providing high availability in a transparent way. A prototype was implemented and experimental results are presented.
Virtualization is getting unprecedented attention from Mobile network Operators (MNOs) as it provides agility in deployment, especially when coupled with the Cloud that offers inherent elasticity and load-balancing of...
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Virtualization is getting unprecedented attention from Mobile network Operators (MNOs) as it provides agility in deployment, especially when coupled with the Cloud that offers inherent elasticity and load-balancing of resources. MNOs have to ensure operational excellence by meeting several objectives. In this context, we propose in this paper, a framework for optimizing the mapping of next Generation Node-Bs (gNBs) to Software-Defined 5G Core (5GC) delay tolerant networkfunctions (NFs). These NFs are considered to be deployed as a Virtual Machine (VM) pool, or containers, in order to minimize cloud computing cost, processing power and at the same time maximize network load. First, we formulate this problem as an integer linear program, while taking into account multiple constraints including Virtual Central Processing Unit (vCPU) capacity, central processing load limits and integrality of mapping relations between gNBs and 5GC NFs. Then, we propose an algorithm to solve large problem instances based on Branch, Cut and Price (BCP) combining all of "Branch and Price", "Branch and Cut" and "Branch and Bound" frameworks. We present several schemes reflecting different optimization goals that the MNO can foster: virtualization cost, power minimization, network load or all. Simulation results demonstrate the good performance of our proposed algorithm to solve the gNBs-VM pool mapping for all evaluated schemes, while also emphasizing the advantages of a particular one (EWoS-333 for Equal Weight optimization Scheme) that can decrease virtualization cost by almost one order of magnitude compared to a static selection scheme, while considering the other two objectives.
In the network softwarization, network Function Virtualisation (NFV) has shifted the standard of network services deployment and management for telecommunication. However, the allocation of physical resources to the V...
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ISBN:
(纸本)9781728167589
In the network softwarization, network Function Virtualisation (NFV) has shifted the standard of network services deployment and management for telecommunication. However, the allocation of physical resources to the Virtualised networkfunctions (VNFs) dynamically, efficiently, and autonomously is one of the key tasks to achieve this objective. network designing is a serious factor for the deployment of VNFs to create a Service Function Chaining (SFC) with efficient resource utilization and optimal path in the edge computing environment. The development of a self-driven Software-Defined network (SDN) also requires an intelligent network design, especially to find optimum routing patterns for traffic steering that meet the goals set by administrators. But unfortunately, the current network designing methods do not fulfill the desired requirement for precise estimations of related performance metrics. Recently, the application of Artificial Intelligence (AI) is considered by the research community to operate and control the network. The Graph Neural network (GNN) is adopted as an AI solution in the field of the network because GNN can understand the complex relationship between network traffic features, routing, and topology to produce an accurate estimation of relevant performance metrics. In this paper, we propose the implementation of the Knowledge-Defined networking (KDN) system based on Graph Neural network (GNN) to predict the optimal path for SFC deployment and traffic steering. The proposed system is evaluated using the complete virtualized environment which is composed of ONOS, OpenStack, and open-source MANO (OSM).
Multicasting is a fundamental functionality of many network applications, including online conferencing, event monitoring, video streaming, and so on. To ensure reliable, secure, and scalable multicasting, a service c...
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Multicasting is a fundamental functionality of many network applications, including online conferencing, event monitoring, video streaming, and so on. To ensure reliable, secure, and scalable multicasting, a service chain that consists of networkfunctions (e.g., firewalls, intrusion detection systems, and transcoders) usually is associated with each multicast request. We refer to such a multicast request with service chain requirement as an network function virtualization (NFV)-enabled multicast request. In this paper, we study NFV-enabled multicasting in a software-defined network (SDN) with an aim to maximize network throughput while minimizing the implementation cost of admitted NFV-enabled multicast requests, subject to network resource capacity, where the implementation cost of a request consists of its computing resource consumption cost in servers and its network bandwidth consumption cost when routing and processing its data packets in the network. To this end, we first formulate two NFV-enabled multicasting problems with and without resource capacity constraints and one online NFV-enabled multicasting problem. We then devise two approximation algorithms with an approximation ratio of 2M for the NFV-enabled multicasting problems with and without resource capacity constraints, if the number of servers for implementing the service chain of each request is no greater than a constant M (>= 1). We also study dynamic admissions of NFV-enabled multicast requests without the knowledge of future request arrivals with the objective to maximize the network throughput, for which we propose an efficient heuristic, and for the special case of dynamic request admissions, we devise an online algorithm with a competitive ratio of O(log n) for it when M = 1, where n is the number of nodes in the network. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising
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