SFC-HO: Reliable Layered Service Function Chaining

Efficient service reconfiguration with partial virtual network function migration

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 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.

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.

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.

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

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.

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.

Joint optimization of Service Chain Graph Design and Mapping in NFV-enabled networks

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.

Owing to the Network Function Virtualization (NFV) and Software-Defined Networks (SDN), Service Function Chain (SFC) has become a popular service in SDN and NFV-enabled network. However, as the Virtual Network Function (VNF) of each type is generally multi-instance and flows with SFC requests must traverse a series of specified VNFs in predefined orders, it is a challenge for dynamic SFC formation to optimally select VNF instances and construct paths. Moreover, the load balancing and end-to-end delay need to be paid attention to, when routing flows with SFC requests. Additionally, fine-grained scheduling for traffic at flow level needs differentiated routing which should take flow features into consideration. Unfortunately, traditional algorithms cannot fulfill all these requirements. In this paper, we study the Differentiated Routing Problem considering SFC (DRP-SFC) in SDN and NFV-enabled network. We formulate the DRP-SFC as a Binary Integer Programming (BIP) model aiming to minimize the resource consumption costs of flows with SFC requests. Then a novel routing algorithm, Resource Aware Routing Algorithm (RA-RA), is proposed to solve the DRP-SFC. Performance evaluation shows that RA-RA can efficiently solve the DRP-SFC and surpass the performance of other existing algorithms in acceptance rate, throughput, hop count and load balancing.

Attack-aware Security Function Management

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.

In this paper, we study how to provide optimal performance guarantee in multi-resource scenarios, such as Network Function Virtualization (NFV), where Virtual Network Function (VNF) resource demands of one Service Function Chain (SFC) are elastic and correlated with the end-to-end throughput of SFC. Previous works such as Dominant Resource Fairness (DRF) achieve this goal only if the demands are already orchestrated to dedicated resource instances. However, how to orchestrate SFC to VNF instances is a key issue in NFV. Few previous works have investigated the relationship between them. In this paper, we consider the problem of SFC orchestration subject to DRF allocation scheme so as to find the orchestration scheme with optimal performance guarantee. We analyze it and model it as SFC Performance Guarantee (SFC-PG) problem, followed by a Mixed Integer Linear Programming (MILP) formulation. We design a heuristic Performance Guarantee Aware Orchestration (PGAO) to solve it in large scale cases. We conduct simulations with a variety of circumstances and parameters, and the results demonstrate that PGAO achieves better performance compared to existing solutions.