Proactive Service Orchestration with Deadline

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%.

Efficient service reconfiguration with partial virtual network function migration

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.

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.

In this paper, we propose the use of reinforcement learning to deploy a service function chain (SFC) of cellular network service and manage the VNFs operation. We consider that the SFC is deployed by the reinforcement learning agent considering a scenario with distributed data centers, where the virtual network functions (VNFs) are deployed in virtual machines in commodity servers. The VNF management is related to create, delete, and restart the VNFs. The main purpose is to reduce the number of lost packets taking into account the energy consumption of the servers. We use the Proximal Policy Optimization (PPO2) algorithm to implement the agent and preliminary results show that the agent is able to allocate the SFC and manage the VNFs, reducing the number of lost packets.

Network virtualization has become a promising paradigm for supporting diverse vertical services in Software Defined Networks (SDNs). Each vertical service is carried by a virtual network (VN), which normally has a chaining structure. In this way, a Service Function Chain (SFC) is composed by an ordered set of virtual network functions (VNFs) to provide tailored network services. Such new programmable flexibilities for future networks also bring new network management challenges: how to collect and analyze network measurement data, and further predict and diagnose the performance of SFCs? This is a fundamental problem for the management of SFCs, because the VNFs could be migrated in case of SFC performance degradation to avoid Service Level Agreement (SLA) violation. Despite the importance of the problem, SFC performance analysis has not attracted much research attention in the literature. In this current paper, enabled by a novel detailed network debugging technology, In-band Network Telemetry (INT), we propose a learning based framework for early SFC fault prediction and diagnosis. Based on the SFC traffic flow measurement data provided by INT, the framework firstly extracts SFC performance features. Then, Long Short-Term Memory (LSTM) networks are utilized to predict the upcoming values for these features in the next time slot. Finally, Support Vector Machine (SVM) is utilized as network fault classifier to predict possible SFC faults. We also discuss the practical utilization relevance of the proposed framework, and conduct a set of network emulations to validate the performance of the proposed framework.

Service function chaining (SFC), based on the network function virtualization (NFV) technology, is a promising candidate for realizing agile and elastic network service provisioning. This paper tackles a SFC deployment across multiple domains where only abstracted intra-domain resource information is available. For the multi-domain SFC deployment, a privacy preserving deployment mechanism called PPDM has been proposed. To conceal privacy of domains, PPDM abstracts intradomain resource information to a binary matrix, and adopts deep reinforcement learning (DRL) for flexible SFC deployment. However, because PPDM deploys all Virtual Network Functions (VNFs) included in a single SFC in a batch manner, substrate nodes need to redundantly reserve the resources required by the deployable VNFs until the construction of the SFC is completed. As a result, the deployment of the SFC may fail because of the lack of the resources on substrate nodes. In this paper, to improve acceptance ratio of SFC requests, we propose a privacy-preserving sequential deployment mechanism (PPSDM) for SFC deployment across multiple domains. The key idea behind PPSDM is to deploy VNFs in a single SFC one by one in a sequential manner to avoid redundant reservation of resources. Comprehensive simulations demonstrate that PPSDM achieves up to 22% higher acceptance ratio of SFC requests compared to the baseline approaches such as PPDM.

Network Functions Virtualization (NFV) is an emerging initiative where virtualization is used to consolidate Network Functions (NFs) onto high volume servers (HVS), switches, and storage. In addition, NFV provides flexibility as Virtual Network Functions (VNFs) can be moved to different locations in the network. One of the major challenges of NFV is the allocation of demanded network services in the network infrastructures, commonly referred to as the Network Functions Virtualization - Resource Allocation (NFV-RA) problem. NFV-RA is divided into three stages: (i) Service Function Chain (SFC) composition, (ii) SFC embedding and (iii) SFC scheduling. Up to now, existing NFV-RA approaches have mostly tackled the SFC embedding stage taking the SFC composition as an assumption. Few approaches have faced the composition of the SFCs using heuristic approaches that do not guarantee optimal solutions. In this paper, we solve the first stage of the problem by characterizing the service requests in terms of NFs and optimally building the SFC using an Integer Linear Programming (ILP) approach.

Along with the increasing demand for latencysensitive services and applications, Deterministic Network (DetNet) concept has been recently proposed to investigate deterministic latency assurance for services featured with bounded latency requirements in 5G edge networks. The Network Function Virtualization (NFV) technology enables Internet Service Providers (ISPs) to flexibly place Virtual Network Functions (VNFs) achieving performance and cost benefits. Then, Service Function Chains (SFC) are formed by steering traffic through a series of VNF instances in a predefined order. Moreover, the required network resources and placement of VNF instances along SFC should be optimized to meet the deterministic latency requirements. Therefore, it is significant for ISPs to determine an optimal SFC deployment strategy to ensure network performance while improving the network revenue. In this paper, we jointly investigate the resource allocation and SFC placement in 5G edge networks for deterministic latency assurance. We formulate this problem as a mathematic programming model with the objective of maximizing the overall network profit for ISP. Furthermore, a novel Deterministic SFC deployment (Det-SFCD) algorithm is proposed to efficiently embed SFC requests with deterministic latency assurance. The performance evaluation results show that the proposed algorithm can provide better performance in terms of SFC request acceptance rate, network cost reduction, and network resource efficiency compared with benchmark strategy.

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.

Network function virtualization (NFV) utilizes IT virtualization technology to realize the functions of various network devices and realize the decoupling of hardware and software functions. Typically, a service request specifies the virtual network functions (VNFs) it needs and the order between them. A network flow needs to traverse a set of sequential VNFs called a service function chain (SFC). Although SFC can increase the flexibility of service orchestration to meet the needs of different users, network providers face many challenges due to the need to ensure service reliability and some constraints. Therefore, how orchestrating SFC and designing a suitable network architecture is a critical issue in this field at present. We propose an efficient network layer-based SFC orchestration method, called SFC-hierarchical orchestration (SFC-HO). Our method separates virtual and physical networks into layers and computes the availability of resources in each layer. We filter the VNFs of each layer and deploy them to the physical layer that meets the greatest benefit. To this end, we formulate the problem as an integer linear programming (ILP) problem to minimize the total deployment cost. In order to further optimize the layering strategy, we innovatively use the Benders decomposition method to decompose the SFC-HO problem into two sub-problems, which are the hierarchical mapping problem of VNFs and the routing problem between nodes. We propose two algorithms for the two problems respectively. The simulation results show that compared with the SFC orchestration process in the traditional network model. Our research can effectively improve the reliability of the SFC, reduce the delay of the routing process, and effectively reduce the cost consumption. Our research effectively solves the problem of difficult service orchestration for operators in the face of diverse service demands and a large number of service requests.

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

The research community has been moving attention from Virtual Network Function (VNF) to Cloud-native Network Function (CNF) since cloudification has brought the Network Function Virtualization (NFV) to an advanced level. It has already been demonstrated that cloud-native technology brings high flexibility and efficiency to large-scale network service deployment compared to the traditional VNF with Virtual Machines (VMs). However, more work is needed to provide a flexible and reliable Service Function Chaining (SFC) development solution in a cloud-native environment. This paper proposes a web-based orchestrator system to deploy an SFC use case consisting of multiple CNFs in a multi-node Kubernetes cluster using Network Service Mesh (NSM). We demonstrate a cloud-native SFC framework that allows users to dynamically create container-based SFC rather than the traditional VMs with NFV/SDN controller approach. Further, additional work is presented with the support of an open-source monitoring system, Prometheus, to validate the SFC path.

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.