Virtual network embedding through topology-aware node ranking

Aiming at the virtual network embedding problem under dynamic service request, Service Request Aware-based Dynamic Virtual Network Embedding (SRAD) algorithm is proposed. We first present virtual network embedding model, and then define virtual network construction cost according to the dynamic characteristic of service request. Our goal is to find the optimal reconfiguration policies by awaring service request type and changing trends, which can minimize the overall construction cost using substrate resource. Experimental results show that the proposed algorithm satisfies the service request, achieves higher success ratio and gains higher revenue/cost ratio for substrate network comparing with the existing algorithms.

The virtual network embedding problem is a majorchallenge in this field. Its target is to efficiently map the virtual nodes and virtual linksonto the substrate network resources. Due to multiple objectives and multipleconstraints, finding the optimal solution turns out to be a very difficult problem. This paper describes the virtual network embedding, integer programming form to design an algorithm for virtual network embedding based on integer programming to improve the benefit / cost ratio of the virtual network mapping. Introduction Network virtualization has been identified as a promising technology toovercome the current ossification of the Internet by running multiple network servicesand experiments simultaneously on the same substrate network. First, a subset of nodes in the virtual network is found to be a node set of the virtual nodes. The nodes are satisfied with the distance of the virtual nodes in DV. Then, all virtual nodes are connected to each of the nodes, the link between these virtual nodes and their nodes is a link, and the bandwidth of each link is set up in the extended enhanced graph. Each of the starting point and end point of a virtual link is connected to a node of the entity network, and the optimal route is equivalent to the virtual link to find an optimal path for the virtual link to meet the demand of the network. Some two value constraints are used to ensure that each virtual node can only be connected to one of the nodes of the node as a node of the node. The main goal is to increase the average income of virtual network embedding. Its limiting conditions include capacity constraints, traffic constraints, two element constraints and domain constraints. Node mapping and link mapping can be done at the same time without the need to do additional calculations. The algorithm proposed in this paper is also added to the target function to increase the acceptance rate of the network map by adding the key index CI in Topology-Awareness. Algorithm for Virtual Network EmbeddingBased on Integer Programming Virtual Network Embedding. Virtual network embedding is a key problem of network virtualization. Virtual network mapping problem is a major challenge in this field. Its goal is to embed the virtual nodes and virtual links embedded in virtual network requests into the network resources effectively. Previous research focused on the design of heuristic algorithm or attempts to solve the solution through two stages, that is, the node mapping is the first stage, and the link mapping is the second stage. In this paper, a new algorithm of virtual network embedding based on integer programming is proposed in this paper. First, a set of nodes of a distance close to each virtual node is found as its meta node set. Then, all virtual nodes and its element nodes are focused on each node, so as to build an enhanced network model based on the original physical network. On the basis of this model, the virtual network mapping problem is defined as an objective function and some constraints. A topology dependent factor is added to the objective function. This method solves the problem of virtual network mapping in one step. The simulation results clearly show that the proposed algorithm greatly improves the performance of virtual embedding, improves the 3rd International Conference on Machinery, Materials and Information Technology Applications (ICMMITA 2015) © 2015. The authors Published by Atlantis Press 1 embedding acceptance rate, increases the cost and benefits and reduces the cost of virtual network mapping cost. Integer Programming Form. The goal of the virtual network mapping is to obtain the maximum benefit of the virtual network, which is based on the premise of satisfying the conditions. Suppose the benefit of t is ( ( )) v R G t , then the goal of the virtual network mapping is shown as follows to maximize the long-term average benefit:

Virtual Network Embedding (VNE) is the major challenge in Network Virtualization. Multiple VNE algorithms have been proposed in the literature. Most proposed VNE algorithms belong to the heuristic category. Heuristic algorithms mostly quantify certain node topological attributes and local node resources to rank (substrate and virtual) nodes before embedding each VN. Link topological attributes and global resources have not been quantified and assisted to rank nodes before. Thus leading to local optimum embedding and low VN acceptance ratio in the long run. To deal with this, we propose a new node ranking approach to rank all substrate and virtual nodes, covering multiple (node and link) topological attributes and global resources. Node and Link Topological Attributes based VNE algorithm, labeled as NLTA, is proposed on the basis of new node ranking approach. Extensive simulations demonstrate that NLTA outperforms four latest heuristic algorithms that consider certain node topological attributes and local node resources. For instance, average VN acceptance ratio of NLTA can increase up to 4% over the best behaved heuristic algorithm.

Network virtualization is recognized as an enabling technology for the future Internet. It aims to overcome the resistance of the current Internet to architectural change and to enable a new business model decoupling the network services from the underlying infrastructure. The problem of embedding virtual networks in a substrate network is the main resource allocation challenge in network virtualization and is usually referred to as the Virtual Network Embedding (VNE) problem. VNE deals with the allocation of virtual resources both in nodes and links. Therefore, it can be divided into two sub-problems: Virtual Node Mapping where virtual nodes have to be allocated in physical nodes and Virtual Link Mapping where virtual links connecting these virtual nodes have to be mapped to paths connecting the corresponding nodes in the substrate network. Application of network virtualization relies on algorithms that can instantiate virtualized networks on a substrate infrastructure, optimizing the layout for service-relevant metrics. This class of algorithms is commonly known as VNE algorithms. This thesis proposes a set of contributions to solve the research challenges of the VNE that have not been tackled by the research community. To do that, it performs a deep and comprehensive survey of virtual network embedding. The first research challenge identified is the lack of proposals to solve the virtual link mapping stage of VNE using single path in the physical network. As this problem is NP-hard, existing proposals solve it using well known shortest path algorithms that limit the mapping considering just one constraint. This thesis proposes the use of a mathematical multi-constraint routing framework called paths algebra to solve the virtual link mapping stage. Besides, the thesis introduces a new demand caused by virtual link demands into physical nodes acting as intermediate (hidden) hops in a path of the physical network. Most of the current VNE approaches are centralized. They suffer of scalability issues and provide a single point of failure. In addition, they are not able to embed virtual network requests arriving at the same time in parallel. To solve this challenge, this thesis proposes a distributed, parallel and universal virtual network embedding framework. The proposed framework can be used to run any existing embedding algorithm in a distributed way. Thereby, computational load for embedding multiple virtual networks is spread across the substrate network Energy efficiency is one of the main challenges in future networking environments. Network virtualization can be used to tackle this problem by sharing hardware, instead of requiring dedicated hardware for each instance. Until now, VNE algorithms do not consider energy as a factor for the mapping. This thesis introduces the energy aware VNE where the main objective is to switch off as many network nodes and interfaces as possible by allocating the virtual demands to a consolidated subset of active physical networking equipment. To evaluate and validate the aforementioned VNE proposals, this thesis helped in the development of a software framework called ALgorithms for Embedding VIrtual Networks (ALEVIN). ALEVIN allows to easily implement, evaluate and compare different VNE algorithms according to a set of metrics, which evaluate the algorithms and compute their results on a given scenario for arbitrary parameters.

In Network virtualization, Virtual Network Embedding(VNE) is the process of mapping virtual nodes and links of a virtual network request(VNR) on a substrate network to fulfill the demands of the request. Embedding virtual network requests helps in achieving network virtualization efficiently. This paper presents Vineyard, a set of VN embedding algorithms namely D-Vine and R-Vine, to introduce a finer correlation between the node mapping and link mapping phases. Deterministic Virtual Network Embedding(D-Vine) algorithm is used to embed virtual nodes on to substrate network based on the capacity constraint when it is not satisfied the system takes the Randomised Virtual Network Embedding(R-Vine) algorithm to map the nodes based on location constraint. Subsequently, after the nodes are embedded the link mapping phase is done based on the distance constraint. A window-based virtual embedding algorithm(W-Vine) is also introduced to evaluate the effect of lookahead in Virtual Network Embedding. After the mapping of multiple virtual network requests, analysis is done to compare the node and CPU utilization in both the algorithms and the variations are conserved.

Due to the ossification of the current Internet, it is difficult to launch new service. One of solutions is network virtualization. Numerous virtual network embedding (VNE) algorithms have been proposed in many literatures. But, there is no general methods or frameworks for evaluation of these algorithms. We have analyzed a number of studies, and found appropriate evaluation indexes that can be used in evaluating the functionalities of VNE algorithms. Based on those indexes, we presented a new evaluation method of secure VNE algorithms. To make a virtual network with the various requirements, the infrastructure provider needs effective resource allocation algorithm. The role of VNE is to allocate physical resources to virtual nodes or links to form virtual networks. In order to use the resources of physical networks, appropriate resource allocation algorithms are required. We found a set of evaluation indexes by analyzing the previous proposed researches. Through analysis, we found that our proposed method can be grouped into two functional attributes for classification. One attribute is the basic attributes that mean special features of architecture and the other is the evaluation attributes that perform the assessment of algorithms. We have evaluated the algorithms with our proposed evaluation methods and found to be useful to choose the appropriate algorithm to the infrastructure provider. The proposed method was found to be more convenient to perform the evaluation of the algorithms in real-world simulation. This method helps infrastructure providers to choose the appropriate VNE algorithm.

Virtual Network Embedding for telco-grade network protection and service availability

Network virtualization (NV) is emerged as a key enabler for the success of the future virtualized networks (e.g. 5G networks and smart Internet of Things (IoT)). Virtual Network Embedding (VNE) that addresses the embedding problems of heterogeneous virtual networks (VNs) onto a physical infrastructure is a main challenge in NV. Network topology attributes and network resource-considered (NTANRC) algorithm is a virtual node mapping mechanism that considers essential network features and global network resources for ranking both substrate and virtual nodes prior to embedding each given virtual network request (VNR). In this paper, we propose NTANRC combined with a distributed parallel Genetic Algorithm (GA) for virtual link mapping, namely NTANRC-GA, to solve online VNE problem. Extensive evaluation results show that our proposed solution not only achieves better performance compared to state-of-the-art VNE algorithms, but also challenges the rapid speed of shortest path (SP) method. NTANRC algorithm and the parallel GA-based algorithm are reverse compliments of each other to achieve an efficient VNE solution.

The issue of virtual network (VN) embedding constitutes an important aspect of network virtualization, which is considered to be one of the most crucial techniques to overcome the Internet ossification problem. The main purpose of VN embedding is to efficiently utilize the limited physical network resources to offer the supporting of virtual nodes and virtual links from the VNs. Due to the fact that the VN embedding problem is proved to be NP-hard, previous works have put forward some of heuristic algorithms to solve this VN embedding problem. However, most of the existing research works only consider the local resources of nodes, ignoring the topological attributes of its neighborhood nodes, and lead to lower resource utilization of the substrate network. To address this issue, we proposed an approach of VN embedding algorithm called VNE-DCC , which based on the node degree and the clustering coefficient information, we adopted the technique of node importance metric to rank the substrate nodes aim to select the node with the most embedding potential for every virtual node in each VN requests, and exploited the breadth-first-search algorithm to embed the virtual nodes aiming at reducing the resource utilization of substrate links so as to increase the acceptance ratio of VN requests and increase the revenues of operational providers. Extensive simulations have shown that the efficiency of our algorithm is better than the other state-of-the-art algorithms in terms of Revenue/Cost ratio and acceptance ratio.

A novel reinforcement learning algorithm for virtual network embedding

Network virtualization allows multiple heterogeneous virtual networks (VNs) to coexist on a shared infrastructure. Efficient mapping of virtual nodes and virtual links of a VN request onto substrate network resources, also known as the VN embedding problem, is the first step toward enabling such multiplicity. Since this problem is known to be NP-hard, previous research focused on designing heuristic-based algorithms that had clear separation between the node mapping and the link mapping phases. In this paper, we present ViNEYard-a collection of VN embedding algorithms that leverage better coordination between the two phases. We formulate the VN embedding problem as a mixed integer program through substrate network augmentation. We then relax the integer constraints to obtain a linear program and devise two online VN embedding algorithms D-ViNE and R-ViNE using deterministic and randomized rounding techniques, respectively. We also present a generalized window-based VN embedding algorithm (WiNE) to evaluate the effect of lookahead on VN embedding. Our simulation experiments on a large mix of VN requests show that the proposed algorithms increase the acceptance ratio and the revenue while decreasing the cost incurred by the substrate network in the long run.

Virtual Network Embedding (VNE) problem has been widely considered as an important challenge in Network Virtualization (NV): how to embed virtual networks onto the shared substrate network effectively and efficiently. Previous VNE algorithms, only considering single network topology attribute and local resource of each node, may lead to inefficient resource utilization of the substrate network in the long term. To address this issue, a Topology Attribute and Global Resource-Driven VNE algorithm (VNE-TAGRD), adopting a novel node-ranking approach, is proposed in this paper. The novel node-ranking approach, stimulating from the well-known Google PageRank algorithm, considers three essential topology attributes and global network resource information. Numerical simulation results reveal that the proposed VNE-TAGRD outperforms four related and up-to-date heuristic algorithms that only consider single network topology attribute and local resources of each node, in terms of long-term virtual network request acceptance ratio, average revenue to cost ratio.

A green energy-aware hybrid virtual network embedding approach

At present, the traditional heuristic method to solve the problem of virtual network embedding (VNE) is still the mainstream. In the environment of network virtualization (NV), a more efficient VNE algorithm is needed to serve the construction of smart city. Using heuristic algorithm to solve the problem of VNE does not meet its development requirements. In this paper, a VNE algorithm based on node probability is proposed by using reinforcement learning (RL) algorithm. The algorithm extracts three attributes of each substrate node to form a feature matrix, which is used as the input of the policy network to train the agent. The purpose is to deduce the mapping probability of each node and rank the base nodes according to this probability, then embed the virtual nodes in this order. Finally, the breadth first search (BFS) strategy is used to map the links. Simulation results show that our algorithm is superior to a representative algorithm based on node ranking in terms of the acceptance rate of virtual network requests (VNR), long-term revenue consumption ratio and long-term average revenue.

In Cloud Computing, the tenants opting for the Infrastructure as a Service (IaaS) send the resource requirements to the Cloud Service Provider (CSP) in the form of Virtual Network (VN) consisting of a set of inter-connected Virtual Machines (VM). Embedding the VN onto the existing physical network is known as Virtual Network Embedding (VNE) problem. One of the major research challenges is to allocate the physical resources such that the failure of the physical resources would bring less impact onto the users' service. Additionally, the major challenge is to handle the embedding process of growing number of incoming users' VNs from the algorithm design point-of-view. Considering both of the above-mentioned research issues, a novel Failure aware Semi-Centralized VNE (FSC-VNE) algorithm is proposed for the Fat-Tree data center network with the goal to reduce the impact of the resource failure onto the existing users. The impact of failure of the Physical Machines (PMs), physical links and network devices are taken into account while allocating the resources to the users. The beauty of the proposed algorithm is that the VMs are assigned to different PMs in a semi-centralized manner. In other words, the embedding algorithm is executed by multiple physical servers in order to concurrently embed the VMs of a VN and reduces the embedding time. Extensive simulation results show that the proposed algorithm can outperform over other VNE algorithms.