Dynamic Flow Migration for Embedded Services in SDN/NFV-Enabled 5G Core Networks

Service-oriented virtual network deployment is based on statistical resource demands of different services, while data traffic from each service fluctuates over time. In this paper, a delay-aware flow migration problem for embedded services is studied to meet end-to-end (E2E) delay requirement with time-varying traffic. A non-convex multi-objective mixed integer optimization problem is formulated, addressing the trade-off between maximum load balancing and minimum reconfiguration overhead due to flow migrations, under processing and transmission resource constraints and QoS requirement constraints. Since the original problem is non-solvable in optimization solvers due to unsupported types of quadratic constraints, it is transformed to a tractable mixed integer quadratically constrained programming (MIQCP) problem. The optimality gap between the two problems is proved to be zero, so we can obtain the optimum of the original problem through solving the MIQCP problem with some post-processing. Numerical results are presented to demonstrate the aforementioned trade-off, as well as the benefit from flow migration in terms of E2E delay performance guarantee.

The discovery process of OPC Unified Architecture (OPC-UA) is well defined but lacks flexibility when implemented in large networks. While the standard provides concepts for local and global discovery services, the core assumption is that all clients and servers are aware of the discovery server(s) in the network. This dependency is a barrier for Plug-and-Play installations and is overcome by self announces via the multicast Domain Name System (mDNS), however, this scales badly with an increasing number of network nodes and deployed servers. We propose a discovery approach based on Software-Defined Networking (SDN) and Network Function Virtualization (NFV), that installs a plug-and-play mechanism for global OPC-UA discovery in modern virtualized networks.

Attack-aware Security Function Management

Software middlebox-based services can be flexibly managed by software defined networking (SDN) and network function virtualization (NFV). Meanwhile, traffic routing can be simplified and the number of routing rules in the SDN-enabled switches can be reduced through the consolidated middlebox model. However, different network functions in middleboxes may alter the volume of processed traffic, so high congestion may occur in specific bottleneck links if middlebox selection and traffic routing are not well jointly planned. Besides, in a statically switch-controller configured SDN, traffic dynamics will not only affect the link load in the data plane, but also pose a challenge to controller load balancing. Therefore, it’s necessary to achieve better quality-of-service (QoS) performance in both control and data planes. In this article, we first formulate this problem as a joint traffic-aware consolidated middleboxes selection and routing (JTMSR) problem and prove its NP-hardness. Then, we design a two-phase algorithm to achieve the controller and link load balancing where the first phase is to redirect selected flows by applying wildcard rules and the second phase is to find fine-grained routing path by a rounding-based algorithm with bounded approximation factor. Finally, compared with the existing algorithms through extensive simulations, it demonstrates that our method has near-optimal controller load balancing and link load balancing performance and can improve response time by 9.7% compared with static scheme.

In order to meet the traffic demand from diverse next generation wireless network applications and exponentially increasing mobile subscriptions, various 5G network architectures are proposed by leveraging Software Defined Networking (SDN) and Network Function Virtualization (NFV) technologies. Network slicing will be one of the 5G technologies that would support next-generation wireless applications over a shared network infrastructure. However, improper network slicing may lead to either over-provisioning or under-utilization of the underlying network infrastructure resources, especially the 5G core network. Over-provisioning of data plane components such as Serving Gateway (SGW) and Packet Data Network Gateway (PGW) can lead to higher CAPEX and OPEX to mobile operators. In this paper, we propose a novel auto-scaling approach called Bit rate Aware Auto Scaling (BAAS) that maintains a precise UE bit rate requirement in the network slices without over-provisioning of data plane resources.

It is our great pleasure to welcome you to the 2018 ACM International Workshop on Security in Software Defined Networks & Network Function Virtualization - SDN-NFV Security'18. SDN-NFV Security'18 offers a unique opportunity by bringing together researchers from academic, industry, as well as government agencies to discuss security challenges faced by two emerging networking paradigms, Software-Defined Networking (SDN) and Network Function Virtualization (NFV). SDN-NFV Security'18 solicits the submission of original unpublished ideas in 6-page long papers and 4-page short papers that address the latest practices, experiences, and lessons learned on SDN and NFV security. The call for papers attracted submissions from Europe and the United States. SDN-NFV Security'18 accepted 3 full papers be presented in this workshop, after a rigorous review process (at least 4 reviews for each paper) and discussion phase. In addition, we have invited 8 papers (5 full papers and 3 short papers) from domain experts to be presented in the workshop.

Software-defined networking (SDN) and network function virtualization (NFV) together form a promising paradigm that enables the slicing of heterogeneous network resources for agile and efficient service customization. Among other techniques, virtual network function (VNF) mapping and scheduling are crucial to the deployment of SDN/NFV-enabled network services. In this paper, to enhance the performance of service provisioning, dynamic VNF mapping and scheduling are jointly investigated. Specifically, to achieve load balancing with QoS guarantee, we first formulate the VNF mapping and scheduling problem as a mixed integer linear programming (MILP). We then propose a two-stage online algorithm to address the NP-hardness of the MILP. In particular, when new service arrives, we map and schedule the VNFs on a service function chain (SFC) by greedily minimizing the waiting time of VNFs. If the delay requirement cannot be satisfied after the first stage, a delay-aware rescheduling scheme is triggered, in which selected existing VNFs are remapped and rescheduled. The proposed dynamic approach achieves flexible function placement and increases service acceptance ratio. Simulation results are provided to validate the effectiveness of the proposed algorithm.

In this era wireless technology gaining more popularity, people prefer wireless communication over wired communication due to mobility, scalability and ease of access. However, the traditional wireless network is complex, vendor dependent and inadequate feasibility to incorporate new services, protocols, and APIs. The rapid growth in wireless network raises many thoughtful challenges for network and telecom operators. On such grounds, Innovation of Software Defined Network (SDN) and network function virtualization (NFV) for mobile network opens a new arena of research. This research article presents future mobile Internet architecture (FMIA) with the integration of twin technologies, SDN and NFV that enable rapid access to Internet services. Moreover, the network manager can control services provided by ISPs through proposed architecture.

The integration of software-defined networking (S-DN) and network function virtualization (NFV) is expected to achieve the convenient management and flexible deployment of various sophisticated network functions, and support user applications with guaranteed quality of service (QoS). In this paper, we jointly study route selection and network function placement problem. To stress the service sensitivity on delay, we formulate an optimization problem which minimizes the total end-to-end delay subject to data transmission, service requirement and various available resource constraints. As the formulated problem is an NP-hard problem, which cannot be solved easily, we transform it into three subproblems, i.e., route selection subproblem, network function placement subproblem and resource sharing subproblem of user flows, and solve the three subproblems by applying the K-shortest paths algorithm, Kuhn-Munkres (K-M) algorithm and Lagrangian dual method, respectively. Numerical results demonstrate the effectiveness of the proposed algorithm.

SummaryIn 5G networks, mobile network and service functions run as software on general‐purpose servers that are distributed across remote sites. Dedicated networks, such as Internet of Things (IoT) network, disaster network, and immersive media network, are virtualized as software solutions on common hardware equipment. This means a new network can be easily provisioned by modifying the configurations of the network based on software‐defined networking (SDN) and network functions virtualization (NFV). Furthermore, the 5G core network architecture toward flat architecture with distributed cloud is a key solution for providing service flexibility and scalability in order to handle an explosive increase in mobile data traffic. To create heterogeneous networks and services on 5G networks, we propose a location‐aware network virtualization method that aims at minimizing the network load and resource cost by appropriately selecting the relevant cloud (including Virtual Machines (VMs), virtual network function [VNF] nodes, applications, and resources) and mobile core entities to provide the required virtual networking services that facilitate secure isolation of the virtual networks with automated resource convergence. In addition, we propose a selective mobility management scheme for service continuity in cases of user equipment (UE) handover. The numerical results show the trade‐off between the different cost factors, ie, network cost and service resources cost in terms of the number of deployed clouds on 5G core networks.

Software Defined Networking (SDN) and Network Function Virtualization (NFV) are transforming Data Center (DC), Telecom, and enterprise networking. The programmability offered by P4 enables SDN to be more protocol-independent and flexible. Data Centers are increasingly adopting SmartNICs (sNICs) to accelerate packet processing that can be leveraged to support packet processing pipelines and custom Network Functions (NFs). However, there are several challenges in integrating and deploying P4 based SDN control as well as host and sNIC-based programmable NFs. These include configuration and management of the data plane components (Host and sNIC P4 switches) for the SDN control plane and effective utilization of data plane resources. P4NFV addresses these concerns and provides a unified P4 switch abstraction framework to simplify the SDN control plane, reducing management complexities, and leveraging a host-local SDN Agent to improve the overall resource utilization. The SDN agent considers the network-wide, host, and sNIC specific capabilities and constraints. Based on workload and traffic characteristics, P4NFV determines the partitioning of the P4 tables and optimal placement of NFs (P4 actions) to minimize the overall delay and maximize resource utilization. P4NFV uses Mixed Integer Linear Programming (MILP) based optimization formulation and achieves up to 2. 5X increase in system capacity while minimizing the delay experienced by flows. P4NFV considers the number of packet exchanges, flow size, and state dependency to minimize the delay imposed by data transmission over PCI Express interface.

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.

In this chapter, we present a comprehensive end-to-end (E2E) network slicing framework for fifth-generation (5G) wireless and core networks and provide our vision on network slicing for beyond 5G (B5G) networks using artificial intelligence (AI) technologies. We first give an introduction on the 5G communication networks by specifying distinct 5G networking characteristics. As the key features of 5G pose challenges on quality-of-service (QoS)-oriented resource provisioning, a new concept of network slicing is presented, based on software-defined networking (SDN) and network function virtualization (NFV) technologies, to enhance the overall resource utilization while guaranteeing QoS isolation among services. We focus on developing specific network slicing solutions for both 5G wireless networks and core networks. In addition, the evolution of B5G networks is discussed in terms of a potential networking paradigm shift with new service key performance indicators (KPIs). To satisfy B5G service requirements, a framework of AI-assisted network slicing lifecycle is developed to automate the slice creation with reduced slice management complexity. A case study is presented to demonstrate the effectiveness of the proposed E2E network slicing solutions.Keywords5G communication networksNetwork slicingWireless networksRadio resource slicingCore networksComputing and transmission resource slicingBeyond 5G networksAI-assisted network slicingQoSE2E delay

The recent trends in software-defined networking (SDN) and network function virtualization (NFV) are boosting the advance of software-based packet processing and forwarding on commodity servers. Although performance has traditionally been the challenge of this approach, this situation changes with modern server platforms. High performance load balancers, proxies, virtual switches and other network functions can be now implemented in software and not limited to specialized commercial hardware, thus reducing cost and increasing the flexibility. In this paper we design a lossless software-based switch for high bandwidth data acquisition (DAQ) networks, using the ATLAS experiment at CERN as a case study. We prove that it can effectively solve the incast pathology arising from the many-to-one communication pattern present in DAQ networks by providing extremely high buffering capabilities. We evaluate this on a commodity server equipped with twelve 10 Gbps Ethernet interfaces providing a total bandwidth of 120 Gbps.

Software Defined Network (SDN) and Network Function Virtualization (NFV) are attracting the attention of telecom operators. The telecom environment requires a set of Carrier Grade requirements such as high availability, high throughput and low latency. SDN faces challenges to meet the Carrier Grade. Based on the JAIN SLEE component model and the Libfluid OpenFlow driver, this work proposes the CREDENCE SDN controller. Our evaluation shows that CREDENCE controller offers a higher throughput and a lower latency when is compared to ONOS and OpenDayLight.