To establish a network service in network functions virtualization (NFV) networks, the orchestrator addresses the challenge of service chaining and virtual network function placement (SC-VNFP) by mapping virtual network functions (VNFs) and virtual links onto physical nodes and links. Unlike traditional networks, network operators in NFV networks must contend with both hardware and software failures in order to ensure resilient network services, as NFV networks consist of physical nodes and software-based VNFs. To guarantee network service quality in NFV networks, the existing work has proposed an approach for the SC-VNFP problem that considers VNF diversity and redundancy. VNF diversity splits a single VNF into multiple lightweight replica instances that possess the same functionality as the original VNF, which are then executed in a distributed manner. VNF redundancy, on the other hand, deploys backup instances with standby mode on physical nodes to prepare for potential VNF failures. However, the existing approach does not adequately consider the tradeoff between resource efficiency and service availability in the context of VNF diversity and redundancy. In this paper, we formulate the SC-VNFP problem with VNF diversity and redundancy as a two-step integer linear program (ILP) that adjusts the balance between service availability and resource efficiency. Through numerical experiments, we demonstrate the fundamental characteristics of the proposed ILP, including the tradeoff between resource efficiency and service availability.

Network Functional Virtualization (NFV) is a high-performance network interconnection technology that allows access to traditional network transport devices through virtual network links. It is widely used in cloud computing and other high-concurrent access environments. However, there is a long delay in the introduction of software NFV solutions. Other hardware I/O virtualization solutions don't scale very well. Therefore, this paper proposes a virtualization implementation method on 100Gbps high-speed Field Programmable Gate Array (FPGA) network accelerator card, which uses FPGA accelerator to improve the performance of virtual network devices. This method uses the single root I/O virtualization (SR-IOV) technology to allow 256 virtual links to be created for a single Peripheral Component Interconnect express (PCIe) device. And it supports data transfer with virtual machine (VM) in the way of Peripheral Component Interconnect (PCI) passthrough. In addition, the design also adopts the shared extensible queue management mechanism, which supports the flexible allocation of more than 10,000 queues on virtual machines, and ensures the good isolation performance in the data path and control path. The design provides high-bandwidth transmission performance of more than 90Gbps for the entire network system, meeting the performance requirements of hyperscale cloud computing clusters.

Through the concept of network slicing, a single physical network infrastructure can be split into multiple logically-independent Network Slices (NS), each of which is customized for the needs of its respective individual user or industrial vertical. In the beyond 5G (B5G) system, this customization can be done for many targeted services, including, but not limited to, 5G use cases and beyond 5G. The network slices should be optimized and customized to stitch a suitable environment for targeted industrial services and verticals. This paper proposes a novel Quality of Service (QoS) framework that optimizes and customizes the network slices to ensure the service level agreement (SLA) in terms of end-to-end reliability, delay, and bandwidth communication. The proposed framework makes use of network softwarization technologies, including software-defined networking (SDN) and network function virtualization (NFV), to preserve the SLA and ensure elasticity in managing the NS. This paper also mathematically models the end-to-end network by considering three parts: radio access network (RAN), transport network (TN), and core network (CN). The network is modeled in an abstract manner based on these three parts. Finally, we develop a prototype system to implement these algorithms using the open network operating system (ONOS) as a SDN controller. Simulations are conducted using the Mininet simulator. The results show that our QoS framework and the proposed resource allocation algorithms can effectively schedule network resources for various NS types and provide reliable E2E QoS services to end-users.

In this paper, a novel method of resource abstraction and an abstracted-resource model for dynamic resource control in optical access networks are proposed. Based on this proposal, an implementation assuming application to 5G mobile fronthaul and backhaul is presented. Finally, an evaluation of the processing time for resource allocation using this method is performed using a software prototype of the control function. From the results of the evaluation, it is confirmed that the proposed method offers better characteristics than former approaches, and is suitable for dynamic resource control in 5G applications.

Network function virtualization (NFV) enables network operators to flexibly provide diverse virtualized functions for services such as Internet of things (IoT) and mobile applications. To meet multiple quality of service (QoS) requirements against time-varying network environments, infrastructure providers must dynamically adjust the amount of computational resources, such as CPU, assigned to virtual network functions (VNFs). To provide agile resource control and adaptiveness, predicting the virtual server load via machine learning technologies is an effective approach to the proactive control of network systems. In this paper, we propose an adjustment mechanism for regressors based on forgetting and dynamic ensemble executed in a shorter time than that of our previous work. The framework includes a reducing training data method based on sparse model regression. By making a short list of training data derived from the sparse regression model, the relearning time can be reduced to about 57% without degrading provisioning accuracy.

In this paper, a Proof-of-Concept (PoC) architecture is constructed, and the effectiveness of mmWave overlay heterogeneous network (HetNet) with mesh backhaul utilizing route-multiplexing and Multi-access Edge Computing (MEC) utilizing prefetching algorithm is verified by measuring the throughput and the download time of real contents. The architecture can cope with the intensive mobile data traffic since data delivery utilizes multiple backhaul routes based on the mesh topology, i.e. route-multiplexing mechanism. On the other hand, MEC deploys the network edge contents requested in advance by nearby User Equipment (UE) based on pre-registered context information such as location, destination, demand application, etc. to the network edge, which is called prefetching algorithm. Therefore, mmWave access can be fully exploited even with capacity-limited backhaul networks by introducing the proposed algorithm. These technologies solve the problems in conventional mmWave HetNet to reduce mobile data traffic on backhaul networks to cloud networks. In addition, the proposed architecture is realized by introducing wireless Software Defined Network (SDN) and Network Function Virtualization (NFV). In our architecture, the network is dynamically controlled via wide-coverage microwave band links by which UE's context information is collected for optimizing the network resources and controlling network infrastructures to establish backhaul routes and MEC servers. In this paper, we develop the hardware equipment and middleware systems, and introduce these algorithms which are used as a driver of IEEE802.11ad and open source software. For 5G and beyond, the architecture integrated in mmWave backhaul, MEC and SDN/NFV will support some scenarios and use cases.

The need of telecommunications operators to reduce Capital and Operational Expenditures in networks which traffic is continuously growing has made them search for new alternatives to simplify and automate their procedures. Because of the different transport network segments and multiple layers, the deployment of end-to-end services is a complex task. Also, because of the multiple vendor existence, the control plane has not been fully homogenized, making end-to-end connectivity services a manual and slow process, and the allocation of computing resources across the entire network a difficult task. The new massive capacity requested by Data Centers and the new 5G connectivity services will urge for a better solution to orchestrate the transport network and the distributed computing resources. This article presents and demonstrates a Network Slicing solution together with an end-to-end service orchestration for transport networks. The Network Slicing solution permits the co-existence of virtual networks (one per service) over the same physical network to ensure the specific service requirements. The network orchestrator allows automated end-to-end services across multi-layer multi-domain network segments making use of the standard Transport API (TAPI) data model for both l0 and l2 layers. Both solutions will allow to keep up with beyond 5G services and the higher and faster demand of network and computing resources.

Network functions virtualization (NFV) enables telecommunications service providers to realize various network services by flexibly combining multiple virtual network functions (VNFs). To provide such services, an NFV control method should optimally allocate such VNFs into physical networks and servers by taking account of the combination(s) of objective functions and constraints for each metric defined for each VNF type, e.g., VNF placements and routes between the VNFs. The NFV control method should also be extendable for adding new metrics or changing the combination of metrics. One approach for NFV control to optimize allocations is to construct an algorithm that simultaneously solves the combined optimization problem. However, this approach is not extendable because the problem needs to be reformulated every time a new metric is added or a combination of metrics is changed. Another approach involves using an extendable network-control architecture that coordinates multiple control algorithms specified for individual metrics. However, to the best of our knowledge, no method has been developed that can optimize allocations through this kind of coordination. In this paper, we propose an extendable NFV-integrated control method by coordinating multiple control algorithms. We also propose an efficient coordination algorithm based on reinforcement learning. Finally, we evaluate the effectiveness of the proposed method through simulations.

The static deployment of Virtualized Network Functions (VNFs) introduces 1) significant degradation of Quality of Service (QoS), 2) inefficiency in the network and computing resource utilization, and 3) Network Function Virtualization (NFV)-based services with insufficient scalability, optimality, and flexibility. Caching VNFs is a promising solution to satisfy the dynamic demand to deploy a variety of VNFs and to maximize the performance as well as cost effectiveness. Although the concept of Content Delivery Network (CDN) is popular for efficiently caching and distributing contents, VNF deployment does not realize the benefit of CDN-based caching approaches. The challenges to caching VNFs are 1) to cover the large variety of VNFs and their properties, including the necessity of service chaining, and 2) to achieve high acceptance ratio given the limited availability of resources. This paper proposes Function Delivery Network (FDN), which is a cluster of distributed edge hypervisors for caching VNFs over a Software-Defined Network (SDN). The deployment and quality of the network function can be significantly improved by serving them closer to the end-users from the cached VNFs. FDN introduces a new strategy called Value-based caching that considers 1) the locality of reference and performance parameters of network and edge hypervisors together and 2) a partial deployment of service chains across multiple edge hypervisors for further efficient utilization of hypervisors resources. Evaluations on different patterns of input requests confirm that Value-based caching introduces significant improvement on both QoS and resource utilization in NFV.

In the coming smart-home era, more and more household electrical appliances are generating more and more sensor data and transmitting them over the home networks, which are often connected to Internet through Point-to-Point Protocol over Ethernet (PPPoE) for desirable authentication and accounting. However, according to our knowledge, high-speed commercial home PPPoE router is still absent for a home network environment. In this paper, we first introduce and evaluate our programmable platform FLARE-DPDK for ease of programming network functions. Then we introduce our effort to build a compact 10Gbps software FLARE PPPoE router on a commercial mini-PC. In our implementation, the control plane is implemented with Linux PPPoE software for authentication-like signaling control. The data plane is implemented over FLARE-DPDK platform, where we get packets from physical network interfaces directly bypassing Linux kernel and distribute packets to multiple CPU cores for data processing in parallel. We verify our software PPPoE router in both lab and production network environment. The experimental results show that our FLARE software PPPoE router can achieve much higher throughput than a commercial PPPoE router tested in a production environment.

Recently, Network Function Virtualization (NFV) has drawn attentions of many network researchers with great deal of flexibilities, and various network service chains can be used in an SDN/NFV environment. With the flexibility of virtual middlebox placement, how to place virtual middleboxes in order to optimize the performance of service chains becomes essential. Some past studies focused on placement problem of consolidated middleboxes which combine multiple functions into a virtual middlebox. However, when a virtual middlebox providing only a single function is considered, the placement problem becomes much more complex. In this paper, we propose a new heuristic method, the gradual switch clustering based virtual middlebox placement method, in order to improve the performance of service chains, with the constraints of end-to-end delay, bandwidth, and operation cost of deploying a virtual middlebox on a switch. The proposed method gradually finds candidate places for each type of virtual middlebox along with the sequential order of service chains, by clustering candidate switches which satisfy the constraints. Finally, among candidate places for each type of virtual middlebox, the best places are selected in order to minimize the end-to-end delays of service chains. The evaluation results, which are obtained through Mininet based extensive emulations, show that the proposed method outperforms than other methods, and specifically it achieves around 25% less end-to-end delay than other methods.

Network function virtualization (NFV) flexibly provides servoces by virtualizing network functions on a general-purpose server, and attracted research interest in recent years. In NFV environment, providing service chaining, which dynamically connects each network function (virtual network function: VNF), is critical issue. However, as it is challenging to select the optimal sequence of VNF services in the service chain in a decentralized manner, the distances between the VNFs tend to increase, leading to longer communication and processing delays. Furthermore, it has never considered that certain VNFs that can be exchange the order of services with one another. To address this problem, in this paper, we propose a distributed search method for ordered VNFs to reduce delays while considering the load on control server, by exploiting an in-network guidance technology, called Breadcrrmubs, for query messages.

Service function chain (SFC) is a series of ordered virtual network functions (VNFs) for processing traffic flows in the virtualized networking environment of future networks. In this paper, we present a mathematical model and dynamic programing based scheme for solving the problem of SFC placement on substrate networks equipped with network function virtualization (NFV) capability. In this paper, we first formulate the overall cost of SFC placement as the combination of setup cost and operation cost. We then formulate the SFC placement problem as an integer linear programing (ILP) model with the objective of minimizing the overall cost of setup and operation, and propose a delay aware dynamic programming based SFC placement scheme for large networks. We conduct numeric simulations to evaluate the proposed scheme. We analyze the cost and performance of network under different optimization objectives, with and without keeping the order of VNFs in SFC. We measure the success rate, resources utilization, and end to end delay of SFC on different topologies. The results show that the proposed scheme outperforms other related schemes in various scenarios.

Accounting for the exponential increase in security threats, the development of new defense strategies for pervasive environments is acquiring an ever-growing importance. The expected avalanche of heterogeneous IoT devices which will populate our industrial factories and smart houses will increase the complexity of managing security requirements in a comprehensive way. To this aim, cloud-based security services are gaining notable impetus to provide security mechanisms according to Security-as-a-Service (SECaaS) model. However, the deployment of security applications in remote cloud data-centers can introduce several drawbacks in terms of traffic overhead and latency increase. To cope with this, Edge Computing can provide remarkable advantages avoiding long routing detours. On the other hand, the limited capabilities of edge node introduce potential constraints in the overall management. This paper focuses on the provisioning of virtualized security services in resource-constrained edge nodes by leveraging lightweight virtualization technologies. Our analysis aims at shedding light on the feasibility of container-based security solutions, thus providing useful guidelines towards the orchestration of security at the edge. Our experiments show that the overhead introduced by the containerization is very light.

5G infrastructures will heavily rely on novel paradigms such as Network Function Virtualization and Service Function Chaining to build complex business chains involving multiple parties. Although virtualization of security middleboxes looks a common practice today, we argue that this approach is inefficient and does not fit the peculiar characteristics of virtualized environments. In this paper, we outline a new paradigm towards autonomous security assurance in 5G infrastructures, leveraging service orchestration for semi-autonomous management and reaction, yet decoupling security management from service graph design. Our work is expected to improve the design and deployment of complex business chains, as well as the application of artificial intelligence and machine learning techniques over large and intertwined security datasets. We describe the overall concept and architecture, and discuss in details the three architectural layers. We also report preliminary work on implementation of the system, by introducing relevant technologies.

Increasing hardware resources, such as multi-core and multi-socket CPUs, memory capacity and high-speed NICs, impose significant challenges on Network Function Virtualization (NFV) backends. They increase the potential numbers of per-server NFs or tenants, which requires a packet switching architecture that is not only scalable to large number of virtual ports, but also robust to attacks on the data plane. This is a real problem; a recent study has reported that Open vSwitch, a widely used software switch, had a buffer-overflow bug in its data plane that results the entire SDN domain to be hijacked by worms propagated in the network. In order to address this problem, we propose REdge. It scales to thousands of virtual ports or NFs (as opposed to hundreds in the current state-of-the art), and protect modular, flexible packet switching logic against various bugs, such as buffer overflow and other unexpected operations using static program checking. When 2048 NFs are active and packets are distributed to them based on the MAC or IP addresses, REdge achieves 3.16 Mpps or higher packet forwarding rates for 60 byte packets and achieves the wire rate for 1500 byte packets in the 25 Gbps link.

Softwarized networks are expected to be utilized as a core network for the 5th Generation (5G) mobile services. For the mobile core network architecture, service chaining is expected to be utilized for dynamically steering traffic across multiple network functions. In this paper, for dynamic multipath service chaining, we propose a greedy-based VNF placement algorithm. This method can provide multipath service chaining so as to utilize the node resources such as CPU effectively while decreasing the cost about bandwidth and transmission delay. The proposed algorithm consists of four difference algorithms, and VNFs are placed appropriately with those algorithm. Our proposed algorithm obtains near optimal solution for the formulated optimization problem with a greedy algorithm, and hence multipath service chains can be provided dynamically. We evaluate the performance of our proposed method with simulation and compare its performance with the performances of other methods. In numerical examples, it is shown that our proposed algorithm can provide multipath service chains appropriately so as to utilize the limited amount of node resources effectively. Moreover, it is shown that our proposed algorithm is effective for providing service chaining dynamically in large-scale network.

In this paper, we posit that, in future mobile network, network softwarization will be prevalent, and it becomes important to utilize deep machine learning within network to classify mobile traffic into fine grained slices, by identifying application types and devices so that we can apply Quality-of-Service (QoS) control, mobile edge/multi-access computing, and various network function per application and per device. This paper reports our initial attempt to apply deep machine learning for identifying application types from actual mobile network traffic captured from an MVNO, mobile virtual network operator and to design the system for classifying it to application specific slices.

The 5G mobile network environment has been studied and developed, and the concept of a vEPC (Virtualized Evolved Packet Core) has been introduced as a framework for Network Functions Virtualization (NFV). Machine-to-Machine (M2M) communications in 5G networks require much faster response than are possible in 4G networks. However, if both the control plane (C-plane) and the data plane (D-plane) functions of the EPC are migrated into a single vEPC server, M2M devices and other user equipments (UEs) share the same resources. To accommodate delay-sensitive M2M sessions in vEPC networks, not only signaling performance on the C-plane but also packet processing performance on the D-plane must be optimized. In this paper, we propose a method for optimizing resource assignment of C-plane and D-plane Virtualized Network Functions (VNFs) in a vEPC server, called the vEPC-ORA method. We distinguish the communications of M2M devices and smartphones and model the vEPC server by using queueing theory. Numerical analysis of optimal resource assignment shows that our proposed method minimizes the blocking rates of M2M sessions and smartphone sessions. We also confirmed that the mean packet processing time is kept within the allowable delay for each communication type, as long as the vEPC server has enough VM resources. Moreover, we study a resource granularity effect on the optimal resource assignment. Numerical analysis under a fixed number of hardware resources of MME and S/P-GW is done for various resource granularities of the vEPC server. The evaluation results of numerical analyses showed that the vEPC-ORA method derives the optimal resource assignment in practical calculation times.

One of the key objectives of 5G is to evolve the current mobile network architecture from “one-fit-all” design model to a more customized and dynamically scaling one that enables the deployment of parallel systems, tailored to the service requirements on top of a shared infrastructure. Indeed, the envisioned 5G services may require different needs in terms of capacity, latency, bandwidth, reliability and security, which cannot be efficiently sustained by the same network infrastructure. Coming to address these customization challenges, network softwarization expressed through Software Defined Networking (SDN) programmable network infrastructures, Network Function Virtualization (NFV) running network functions as software and cloud computing flexibility paradigms, is seen as a possible panacea to addressing the variations in the network requirements posed by the 5G use cases. This will enable network flexibility and programmability, allow the creation and lifecycle management of virtual network slices tailored to the needs of 5G verticals expressed in the form of Mobile Virtual Network Operators (MVNOs) for automotive, eHealth, massive IoT, massive multimedia broadband. In this vein, this paper introduces a potential 5G architecture that enables the orchestration, instantiation and management of end-to-end network slices over multiple administrative and technological domains. The architecture is described from both the management and the service perspective, underlining the common functionality as well as how the response to the diversified service requirements can be achieved through proper software network components development.