VN-SLA: A Virtual Network Specification Schema for Virtual Network Provisioning

The design, management, and operation of network infrastructure have evolved during the last few years, leveraging on innovative technologies and architectures. With such a huge trend, due to the flexibility and significant economic potential of these technologies, software-defined networking (SDN) and network functions virtualization (NFV) are emerging as the most indispensable key catalysers. SDN/NFV enhancing the infrastructure agility, thus network operators and service providers are able to program their own network functions (e.g., gateways, routers, load balancers) on vendor-independent hardware substrate. One of the most important considerations in NFV deployment is how to allocate the virtual resources that are needed to provide flexible virtual network services in an NFV-based network infrastructure. Thus, the most important prerequisite for NFV deployment is achieved fast, scalable and dynamic composition and allocation of networks functions (NFs) to implement network services (NSs). We have proposed a revised RA algorithm that integrates embedding and scheduling of virtual network functions (VNFs) simultaneously. In this paper, performance evaluation of the revised RA algorithm is performed and the proposed algorithm is applied more effectively in NFV environment where the demand for resources is flexible and concentrated.

Virtual networks provide their services by aggregating groups of virtual devices that use an existing physical network to interact with one another. These services, unlike the underlying network, often need a large number of distinct resources (bandwidth, processing power, servers, etc.) to operate. The adaptability of the virtual network architecture is what makes it so appealing. A server is required for each virtual service; hence computers are categorized as virtual service resources. It is common practice to either supply resources for the virtual network or replace the virtual service resource by migrating the service to another node that offers the most suitable number of resources to do so (QoS) when a given resource is unable to meet quality of service requirements due to traffic variation caused by mobile users. Our flow-splitting technique allows for the dynamic redistribution of virtual service resources across several virtual connections. We take a different approach than the prevalent tree-based approaches in the current body of literature by basing our method on graph topology instead. The simulation results from this study demonstrate that our solution drastically decreases the time needed to replace virtual service resources when compared to existing methods.

As a long term solution to the gradual Internet ossification problem, virtual networks can guarantee on-demand requirements of diverse end user on a global level. Clearly it is a major challenge to achieve the automation and optimization of network management like virtual resources or QoS to ensure the personalized user satisfaction. In this article, we propose a novel cognitive management architecture to meet the future needs under a heterogeneous network environment. The architecture introduces a cognitive management cycle to the virtual network resources to ensure flexibility. We also focus on the efficient virtual network resource allocation and scheduling algorithms. Based on the complex network centrality analysis, we solve the virtual network resources allocation problem with consideration of the topology structure and statistical analysis. Finally we evaluate the efficiency and effectiveness of our algorithm on common benchmarks including the acceptance, revenue and cost.

Network virtualization (NV) provides a feasible mechanism for operating numerous diverse virtual networks concurrently on a shared physical infrastructure network. The key issue in NV is virtual network embedding (VNE), which efficiently and effectively maps virtualized networks (VNs) with multiple resource needs for nodes and links to the underlying physical network with limited resources. A multiple topological attributes (MTA) based embedding algorithm is proposed to address the issue of providing different virtual request ser-vices delivered in a wireless network environment, leading to an unstable utilization of physical network resources and a low access rate for subsequent requests. It is emphasized that machine learning (ML) should be integrated into the process of network slicing in order to properly classify the received wireless virtual request. In this work, virtual request services are categorized automatically using support vector machine (SVM), and resources are allocated accordingly. The proposed technique organizes nodes in the embedding process according to their priority based on multiple topological properties of virtual and physical networks. According to the findings of the simulations, the SVM-MTA algorithm enhances both the acceptance rate and the resource efficiency of the network.

In this paper, we present the main challenges for offering end‐to‐end virtual network services under user control. This work is motivated by the growing need for end‐to‐end virtual services to support high‐volume data transfer applications such as large data Center, grid applications and storage area networks over multi‐domain networks. The virtualization of network services has to deal with some important issues, such as dynamic provisioning, sharing control, isolation and security. We have developed and implemented a solution called user control Virtual Service that allows the user to deploy and share different virtual services over a multi‐domain network. UCVS is a network‐driven virtual service solution that enables the coordinated provisioning and reuse of physical and virtualized computing, storage and network resources from shared pools. UCVS ensures that applications are dynamically supported throughout multi‐domain infrastructure. UCVS also achieves cross‐technology orchestration, helping further progress towards a service‐oriented infrastructure. Copyright © 2008 John Wiley & Sons, Ltd.

Trusted detection of ransomware in a private cloud using machine learning methods leveraging meta-features from volatile memory

In recent cloud computing research, the allocation of virtual resources has become a major concern. The Virtual Machine (VM) dynamic deployment technique is one of the key virtual resource management techniques. Virtual resource management predicts the resources on the virtual machine and manages accordingly. The study of the Virtual Machine resource dynamic deployment focuses on the fine-grained resource adjustment strategy. So, there is a need for predicting the usage of resources for the virtual machine to avoid delays, overprovisioning and under provisioning. Time series forecasting models are used because predictions of cloud resources are dependent on time. In this paper, different time series models and ensemble techniques have been fitted to predict resource usage for successive hours. Different time series analysis techniques were implemented to study the dataset and to choose the appropriate model. Various types of resources such as CPU usage, disk read times, disk write times and memory usage are predicted using the above models.

In order to solve the problem of cross-domain resource allocation in IoT in Smart Grid, this paper proposes a virtual network resource allocation mechanism based on particle swarm algorithm. Its goal is to minimize mapping overhead under planning request. In this paper, VN request is divided into multiple virtual subnets according to matching set of virtual resource matching phase, virtual library resource type price information and border node information. We also propose a cross-domain virtual network mapping algorithm based on particle swarm optimization. It can be used to improve the efficiency of cross-domain virtual network mapping. Finally, the execution time, mapping cost and performance of the algorithm in different environments are tested by simulation experiment, which verifies its efficiency and stability performance in virtual network partitioning.

Introduction The design of a mobile network architecture aims at defining network elements (e.g. Base Stations [BSs], switches, routers, user devices) and their interaction in order to ensure a consistent system operation. This chapter discusses basic considerations and provides an overview of current research activities. Network architecture can be considered from different angles that are needed in order to fulfill objectives like integration of technical components into an overall system, proper interworking of multi-vendor equipment and efficient design of physical networks from cost and performance point of view. As 5G systems have to integrate a plethora of partly contradicting requirements, enablers such as Network Function Virtualization (NFV) and Software Defined Networking (SDN) are to be applied in order to provide the needed flexibility of future networks, especially for the core network. Applying these tools may require a rethinking of some traditional aspects of network architecture design. This chapter will give the reader an impression of the most important topics influencing architecture design of future networks. NFV and SDN Today's operator networks include a large and increasing variety of hardware appliances. Launching new services often requires integration of complex hardware dedicated to the service including costly procedure design and is associated with lengthy time to market. On the other hand, hardware life cycles become shorter as technology and service innovation accelerates. At the end of 2012, network operators have started an initiative on NFV [1]. NFV aims at consolidating the variety of network equipment onto industry-standard high-volume servers. These servers can be located at the different network nodes as well as end-user premises. In this context, NFV relies upon but differs from traditional server virtualization. Unlike server virtualization, Virtualized Network Functions (VNF) may consist of one or more virtual machines running different software and processes in order to replace custom hardware appliances (Figure 3.1). As a rule, multiple VNFs are to be used in sequence in order to provide meaningful services to the customer. NFV requires an orchestration framework that enables proper instantiation, monitoring and operation of VNFs and Network Functions (NFs) (e.g. modulation, coding, multiple access, ciphering, etc.).

Network virtualization is seen as a key networking paradigm for building diverse network services and architectures over a shared network infrastructure. Assigning network resources to virtual links and, more generally to virtual network topologies, efficiently and on-demand is one of the most challenging components of any network virtualization solution. This paper addresses the problem of on-line resource allocation of multiple virtual links on a Software Defined Network (SDN) infrastructure. The application context that is targeted is primarily the online provisioning of virtual overlay networks even if it can be broadened to address more general virtual networks. Considering an SDN physical infrastructure allows a complete freedom in choosing the optimal physical paths and the associated resources that support the virtual links with no interference from any other network function (such as routing). However, for the time being, forwarding in an SDN network is resource consuming with a noticeable impact on the size of the flow tables. Hence, forwarding (i.e. switching) resources should be carefully considered by the network resource allocation algorithm. This paper proposes a novel Integer-Linear formulation of the above cited problem by taking into account: (1) point-to-point as well as point-to-multipoint virtual links, each with an associated bandwidth requirement and a maximum transfer delay requirement, (2) two types of network resources, namely network links' bandwidth and nodes' switching resources, and (3) optionally, path splitting which allows a virtual link to be established on multiple physical paths. We report preliminary experimental results on a real network topology in an overloaded scenario (bandwidth requests largely exceed network capacity); they show that our algorithm outperforms shortest path heuristics with a gain on the admission rate (of virtual links requests) that ranges from 5 to 15% (compared to the most efficient heuristic) and with a computation time less than a few seconds.

In the context of 5G network slicing, in order to solve the problem of low service reliability, a high-reliability virtual network resource allocation algorithm based on service priority is proposed in this paper. The algorithm includes four steps: priority ranking of virtual network request and virtual nodes, reliability ranking of underlying nodes, resource allocation for virtual nodes, and resource allocation for virtual links. The priority ranking of virtual network request and virtual nodes mainly include two processes of ordering the virtual network based on service priority and ordering the virtual node based on centrality. In the step of allocating resources to virtual nodes, the main work is to allocate resources to virtual nodes based on the resources and reliability of the underlying nodes. In the simulation experiment, the algorithm in this paper is compared with the traditional algorithm, which verifies that the algorithm in this paper has achieved good results in terms of key business reliability indicators.

In the network slicing environment, how to improve the success rate of virtual network resource allocation has become an urgent problem to be solved. To solve this problem, this paper proposes a virtual network resource allocation algorithm based on the coefficient of variation and the order relationship under network slicing. The algorithm includes four steps: sorting the importance of the attributes of the underlying nodes, sorting the importance of the attributes of the virtual nodes, calculating the comprehensive attribute values of the underlying nodes and sorting them in descending order, fetching virtual network requests one by one and performing resource allocation. To improve the success rate of resource allocation, first allocate resources for important virtual nodes. When calculating the importance of a virtual node, first calculate the attribute value of the virtual node. After the attribute value is normalized by the min-max method, the comprehensive attribute value of each virtual node is calculated according to the weight and arranged in descending order. When allocating resources to virtual nodes one by one, nodes with large attribute values are preferentially allocated. In the performance analysis part, the algorithm of this paper is compared with the traditional algorithm, and it is verified that the algorithm of this paper has achieved good results in the two dimensions of the success rate of virtual network resource allocation and the utilization of underlying network resources.KeywordsNetwork slicingResource allocationCoefficient of variationOrder relationship

Cloud Computing offers elasticity and enhances resource utilisation. This is why its success strongly depends on the efficiency of the physical resource management. This paper deals with dynamic resource reconfiguration to achieve high resource utilisation and to increase Cloud providers income. We propose a new adaptive virtual network resource reconfiguration strategy named VNR-GA to handle dynamic users' needs and to adapt virtual resource allocation according to the applications' requirements. The proposed algorithm VNR-GA is based on Genetic metaheuristic and takes advantage of resources migration techniques to recompute the resource allocation of instantiated virtual networks. In order to optimally adapt the resource allocation according to customers' needs growth, the main idea behind the proposal is to sequentially generate populations of reconfiguration solutions that minimise both the migration and mapping cost and then select the best reconfiguration solution. VNR-GA is validated by extensive simulations and compared to the most prominent related strategy found in literature (i.e., SecondNet). The results obtained show that VNR-GA reduces the rejection rate of i) virtual networks and ii) resource upgrade requests and thus enhances Cloud Provider revenue and customer satisfaction. Moreover, reconfiguration cost is minimised since our proposal reduces both the amount of migrated resources and their new mapping cost.

Virtual network provisioning across multiple substrate networks

Network virtualization is becoming a fundamental building block of future Internet architectures. Although the underlying network infrastructure needed to dynamically create and deploy custom virtual networks is rapidly taking shape (e.g., GENI), constructing and using a virtual network is still a challenging and labor intensive task, one best left to experts.