Towards delay-optimized and resource-efficient network function dynamic deployment for VNF service chaining

Efficient service reconfiguration with partial virtual network function migration

Network Slicing (NS) technique is comprehensively reshaping the next-generation communication networks (e.g. 5G, 6G). Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) predominantly control the flow of service functions on NS to incorporate versatile applications as per user demands. In the virtualized-Software Defined Networking vSDN environment, a chain of well-defined virtual network functions (VNFs) are installed on Service Function Chains (SFCs) by multiple Internet Service Providers (ISPs) concurrently. Generation, allocation, re-allocation, release and destroying associative VNFs on SFC is an extremely difficult task while keeping high selection accuracy. Towards solving this fundamental issue, in this work, we have proposed a multi-layered SFC formation for adaptive VNF allocation on dynamic slices. We have formulated an ILP to address the VNF-EAP (VNF-Embedding and Allocation Problem) over real network topology (AT&T Topology). Leveraging machine learning techniques we have shown an intelligent VNF selection mechanism to optimize resource utilization. The performance evaluation shows remarkable efficiency on ML-driven dynamic VNF selections over static allocations on SFCs by halving resource usage. Further, we have also studied a VNF typecasting technique for service backup on outage slices in the field of disaster management activities.

Service Function Chaining (SFC) serves the traffic of a specific service along an ordered set of Service Functions (SFs). SFC uses Software Defined Networking (SDN) and Network Function Virtualization (NFV) technologies to reach the deployment and removal of SFC in an appropriate time with minimal costs. However, during the life time of deployed SFCs, the SFs are exposed to the risk of overloading, which results in an end-to-end packets drop and high delay at the application level. This paper presents the concept of a Scalable SFC Orchestrator capable of deploying SF Chains following the ETSI NFV architectural model, as well as orchestrating the runtime phase by rerouting the traffic to a different path in case of overload of certain SF instances. Moreover, a selection algorithm is presented to build the updated paths after scaling. The Scalable Orchestration feature is implemented and added as a main feature to an SFC Orchestrator. Furthermore, it is integrated into a current NFV architecture and evaluated in an NFV environment making use of the Fraunhofer FOKUS Open Baton toolkit in an OpenStack and Open Daylight based environment.

Attack-aware Security Function Management

Network function virtualization (NFV) is a key networking concept whose benefits include scalability, flexibility, and cost-effective service provisioning. In NFV, service function chains (SFCs) adaptable to customers' needs are created by chaining virtualized network functions (VNFs). VNFs and SFCs are sensitive elements that, if compromised, would affect network security. The detection of compromised VNFs and SFCs is imperative, and although anomaly detection can be used in such a context, there is a lack of research work on the use of anomaly detection in NFV. In this article, we exploit the use of anomaly detection mechanisms to identify suspicious VNFs and SFCs. We introduce, into a widely accepted NFV architecture, an NFV security module (NSM) that, by analyzing VNFs and SFCs' operations, detects anomalies possibly resulting from security attacks. To prove the concept, three mechanisms have been implemented and deployed in NSM to observe how anomaly detection performs, given quantitative and qualitative information. We found out that anomaly detection is effective for VNF and SFC security and, in the case of using entropy as anomaly detection technique, it presents accuracy of up to 98 percent without harming NFV environment operations.

The increasing demand in Mobile Edge Computing (MEC) networks has led to the emergence of Network Function Virtualization (NFV) technology. Through the virtualization technique, NFV can decouple each Virtual Network Function (VNF) from the hardware and deploy it flexibly in the MEC. Each service request in NFV assembles a Service Function Chain (SFC) by tying different VNFs and processes the traffic in the chained order. How to allocate resources and deploy SFC in MEC to meet Quality of Service (QoS) requirements is an important challenge for NFV. An increase in the number of VNFs with traffic passing one after the other causes high delay in SFC assembly. Also, the demanded resources may change during SFC assembly, which is clearly neglected in existing works. Therefore, high delay and resource demand uncertainty during SFC assembly affect QoS. With this motivation, we solve the SFC deployment problem by Parallelizing VNFs under Resource Demand Uncertainty (PVRDU) in MEC. We use the dependency between VNFs to transform the original SFC into a parallel SFC and then assemble several sub-SFCs based on a Deep Reinforcement Learning (DRL) approach. Meanwhile, PVRDU uses a Markov-based approximation algorithm to handle resource demand uncertainty. We perform extensive trace-driven simulations to verify the effectiveness of the proposed algorithm. The evaluation results of PVRDU are promising in different aspects compared to state-of-the-art methods. Specifically, for the considered scenario, DPC reduced SFC delay by as much as 8.7%.

Network Function Virtualization (NFV) technology facilitates flexible and fast service provisioning by deploying network functions in wished positions and chaining order as Service Function Chain (SFC). Empirically, if the processing inside some Virtual Network Functions (VNFs) does not relate to each other, parallel chaining them can further improve the performance, such as reducing the end-to-end hops of SFCs. However, it is often ignored that parallel deploy VNFs will also introduce additional VNFs to replicate, distribute, and merge data. This will add hops and increase resource consumption. Will parallel deployment of VNFs in SFCs necessarily bring benefits? Regarding multiple parallel forms of an SFC, which one is the best? Further, how does the service process of the NFV network affect the parallel decisions of VNFs? In this paper, we design an NFV orchestrator, DP-NFVO, to maximize the revenue of NFV Service Providers and provide quantitative insights into the impact of parallel VNFs deploy-ment. Precisely, we first refine existing VNF parallelism rules and discuss the impact of VNF parallelism on latency and resource consumption, and analyze the optimal form of parallelism for SFCs. Second, we decompose the service request process in NFV networks into multiple stages and provide a joint model of VNF parallelism and deployment. Further, we design a VNF online deployment and parallelism decision policy for DP-NFVO based on Proximal Policy Optimization (PPO) to maximize revenue. Finally, we demonstrate the effectiveness and adaptability of DP-NFVO on parallel processing and the requests service process with extensive experiments.

To reduce the processing delay from the sequentially running virtual network functions (VNFs) in a service function chain (SFC), network function parallelism (NFP) is introduced that allows VNFs of the SFC to run in parallel. Existing NFP solutions only focused on improving parallelism benefits without paying much attention to resource utilization while deploying VNFs of SFCs. We take advantage of resource-delay dependency to propose a flexible and efficient parallelized SFC placement mechanism called FlexSFC which determines the optimal SFC placement while reducing resource usage and meeting end-to-end delay guarantees of the SFCs deployed. Initial results show that FlexSFC guarantees the end-to-end delay requirement with better resource utilization and SFC acceptance rate than the state-of-the-art approaches.

Service Function Chain (SFC) integrates Network Function Virtualization (NFV) for flexible deployment of Virtualized Network Function (VNF) and utilizes Software-Defined Networking (SDN) for establishing dynamic network connections, enabling customized service chains for specific applications. Security risks in NFV/SDN networks directly impact SFC, making them vulnerable to attacks. Traditional security solutions rely on passive defense mechanisms, which are insufficient against the uncertainties brought by vulnerabilities and backdoors in open networks. As an emerging network security technology, Moving Target Defense (MTD) is expected to break through traditional security protection mechanisms such as “enhanced”, “plug-in” and “passive” defense to achieve active defense against both known and unknown threats. Features of NFV/SDN networks, such as dynamic resource scheduling, microservices, and centralized control, also offer critical guidance for implementing MTD strategy. Therefore, in this paper, we propose a novel security enhancement mechanism by equipping SFC and VNFs with MTD to inherently protect SFC and VNFs during the deployment process, using a sub-pool partitioning algorithm to enhance heterogeneity across sub-pools. Additionally, to address Quality of Service (QoS) requirements, we formulate an optimization problem with three criteria: latency, cost, and defense success rate. Following that, we propose a dynamic deployment algorithm based on Deep Reinforcement Learning (DRL). Finally, to validate the effectiveness of the proposed algorithm, we conduct extensive experiment which have demonstrated that our proposed algorithm is good in increasing the security gains of both SFC and VNFs, provided that this improvement comes at the expense of modest cost and latency.

A comprehensive survey of service function chain provisioning approaches in SDN and NFV architecture

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.

Network Function Virtualization (NFV) pushes the hardware-based network functions to generic servers as software and brings a highly flexible for deployment. The availability of Virtual Machines (VMs) enables the dynamic placement of Virtual Network Functions (VNFs) on demand, and it can reduce a large number of manual configuration processes that increase deployment efficiency. However, some services require more than one VNF to process. Therefore, the network flows need to traverse a set of sequential network functions called Service Function Chain (SFC). How to efficiently route traffic along service function chain and place VNFs in a network under operational constraints is a crucial issue. In this paper, we must overcome two challenges: (1) determining a flow path that traverses suitable network functions in the required order to meet the requirement of services, and (2) considering network loading and other dynamic characteristics when traffic is routed through existing VNFs. Thus, we present methods to solve the routing and placement problems for the service function chain. Our solutions transform the network representation to a virtual layered graph that considers NFV processing latency and allows conventional shortest path algorithms to solve the problem. We are not only pursuing high success rates to serve more flows but also taking into account the execution time of the algorithms.

With the emergence of Network Function Virtualization (NFV) and Software-Defined Networks (SDN), Service Function Chaining (SFC) has evolved into a popular paradigm for carrying and fulfilling network services. However, the implementation of Mobile Edge Computing (MEC) in sixth-generation (6G) mobile networks requires efficient resource allocation mechanisms to migrate virtual network functions (VNFs). Deep learning is a promising approach to address this problem. Currently, research on VNF migration mainly focuses on how to migrate a single VNF while ignoring the VNF sharing and concurrent migration. Moreover, most existing VNF migration algorithms are complex, unscalable, and time-inefficient. This paper assumes that each placed VNF can serve multiple SFCs. We focus on selecting the best migration location for concurrently migrating VNF instances based on actual network conditions. First, we formulate the VNF migration problem as an optimization model whose goal is to minimize the end-to-end delay of all influenced SFCs while guaranteeing network load balance after migration. Next, we design a Deep Learning-based Two-Stage Algorithm (DLTSA) to solve the VNF migration problem. Finally, we combine previous experimental data to generate realistic VNF traffic patterns and evaluate the algorithm. Simulation results show that the SFC delay after migration calculated by DLTSA is close to the optimal results and much lower than the benchmarks. In addition, it effectively guarantees the load balancing of the network after migration.

Complex services composed of middleboxes are mainly used to meet the requirements of advanced services such as security services, monitoring systems, and personalized enterprise services. However, forwarding a traffic across middleboxes is a tedious and error-prone task. Service function chaining (SFC) presents different solutions to steer the traffic among intermediate service functions. However statically managing, deploying, and configuring an SFC remains a challenging task. Dynamic SFC has recently gained significant interest from big networking operators motivated by the emerging technologies of Software Defined Networking (SDN) and Network Function Virtualization (NFV). In this paper, we present a new SDN based architecture and a novel header for traffic steering for dynamic SFC. Our results show that the header we describe in this paper does not increase the packet size nor the overhead.

Network Function Virtualization (NFV) offers service delivery flexibility and reduces overall costs by running service function chains (SFCs) on commodity servers with many cores. Existing solutions for placing SFCs in one server treat all CPU cores as equal and allocate isolated CPU cores to network functions (NFs). However, advanced servers often adopt Non-Uniform Memory Access (NUMA) architecture to improve the scalability of many-core systems. CPU cores are grouped into nodes, incurring performance degradation due to cross-node memory access and intra-node resource contention. Our evaluation shows that randomly selecting cores to place NFs in an SFC could suffer from 39.2 percent lower throughput comparing to an optimal placement solution. In this article, we propose Octans, an NFV orchestrator to achieve maximum aggregate throughput of all SFCs in many-core systems. Octans first formulates the optimization problem as a Non-Linear Integer Programming (NLIP) Model. Then we identify the key factor for problem solving as evaluating the throughput drop of an NF caused by other NFs in the same SFC or different SFCs, i.e., performance drop index, and propose a formal and accurate prediction model based on system level performance metrics. Finally, we propose two online algorithms to quickly find near-optimal placement solutions for one-time and incremental deployment. Extensive evaluation on a prototype implementation shows that Octans significantly improves the aggregate throughput comparing to two state-of-the-art placement solutions by 27.1 ~ 45.2 percent for one-time deployment and by 20.9 ~ 38.1 percent for incremental deployment, with very low prediction errors. Moreover, Octans could quickly find a near-optimal placement solution with tiny optimality gap.