Distributed Service Function Chaining

With the increasing number of providers at a rapid speed in the cloud computing market. Technologies are developing into more complex and interconnected entities. Virtual Network Functions (VNFs), Service Function Chains (SFC), and multi-cloud environments have appeared as emerging developments, which enable Virtual services to be hosted dynamically over multiple clouds, it can be done with greater flexibility in efficiency and cost optimization. On the other hand, the growing demand for low-latency network services is progressively demanding new and creative placement techniques. The problem of optimal Network Function (NF) provision, which involves deciding the optimal position of VNF, on which nodes these functions must be located, and how VNF instances are assigned to Service Function Chains (SFCs), is considered in this work. to active optimal utilization of the resource while meeting the end-to-end latency of the service chain. With large-scale web server workloads, we test our algorithm in the simulation environment by comparing it with the existing state-of-the-art solutions [1], that are unaware of the critical application. The result shows that the proposed algorithm has more advantages than the state-of-the-art as around 85.9% of critical requests are served from a single cloud data center.

With the emerging paradigm of Virtual Network and Network Function Virtualization (NFV), the data center operator can flexibly manage the network to reduce Operating Expenditures (OPEX) and Capital Expenditures (CAPEX). Some new issues including the assignment of Virtual Network Functions (VNFs) and Service Function Chain (SFC) scheduling should be considered to apply in the communication. Mathematically, the SFC scheduling process can be formulated as a queuing system, and every server installed with VNF instances can be regarded as a service node, where batched requests are parallel submitted and processed one after another. In the management of SFC, the cloud service provider needs to resolve the problem of network congestion. Latency and throughput are two important but contradictory indexes in the network management, and both of them are deserved to be optimized during the scheduling of SFC requests. In this paper, different from most existing studies, we focus on the service rate control problem in the scheduling of SFC requests. Firstly, we formulate this problem as an integer programming problem. Through a cooperative game approach, a Nash bargaining based model is proposed to jointly optimize the latency and throughput, which is proven to provide fair performance guarantees by both theoretical analysis and simulation. To improve the scalability of the proposed algorithm, we also design a polynomial two-phase heuristic to perform Pareto optimization. Simulation evaluation shows that the proposed algorithm can implement balanced traffic scheduling and avoid excessive server latency caused by network congestion.

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.

Middleboxes have become an integral part of Internet infrastructure, providing additional flow processing for policy control, security, and performance optimization. Network Function Virtualisation (NFV) proposes the deployment of software-based middleboxes on top of commercial off-the-shelf (COTS), enabling the dynamic adjustment of Virtual Network Functions (VNFs), both in terms of instance numbers and computational power. The performance of Data center and Enterprise networks depend strongly on efficient scaling of VNFs and the traffic load balance across VNF instances. To this end, we present Name enhanced SDN framework for service function chaining of elastic Network functions (NSN) that extends the Function-Centric Service Chaining (FCSC) with load balancing functionalities to achieve efficient network utilization while reducing the switch flow rules by 2–4× compared to traditional SDN approaches.

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.

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.

The rapid development of network function virtualization (NFV) enables a communication network to provide in-network services using virtual network functions (VNFs) deployed on general IT hardware. While existing studies on NFV focused on how to provision VNFs from the provider's perspective, little is done about how to validate the provisioned resources from the user's perspective. In this work, we take a first step towards this problem by developing an inference framework designed to “look into” the NFV network. Our framework infers the structure and state of the overlay formed by VNF instances, ingress/egress points of measurement flows, and critical points on their paths (branching/joining points). Our solution only uses external observations such as the required service chains and the end-to-end performance measurements. Besides the novel application scenario, our work also fundamentally advances the state of the art on topology inference by considering (i) general topologies with general measurement paths, and (ii) information of service chains. Our evaluations show that the proposed solution significantly improves both the reconstruction accuracy and the inference accuracy over existing solutions, and service chain information is critical in revealing the structure of the underlying topology.

For flexible and elastic management of network function virtualization (NFV)-enabled service function chaining (SFC), the resource demands of virtual network functions (VNFs) need to be predicted appropriately. Even though several prediction schemes have been introduced in the literature, they do not work well in multiple SFC environments. To overcome this limitation, we propose a novel prediction model called the deep spatiotemporal correlation network (DSCN). DSCN learns temporal correlation in the VNF load from the past resource usage patterns. At the same time, DSCN defines upstream/downstream VNF instances across multiple SFCs and learns spatial resource usage patterns by extracting features of them. Evaluation results demonstrate that DSCN can improve the prediction accuracy by up to 13.2% and 10.9% compared to the existing models.

The Mobile Edge Computing (MEC) paradigm emerges as a promising technology to provide services for various mobile applications at edges of core networks while meeting stringent service delay requirements of users. Orthogonal with the MEC, Network Function Virtualization (NFV) provides the network resource management with flexibility and scalability, where Virtual Network Functions (VNFs) are deployed over edge servers in a chained manner as Service Function Chains (SFCs) for enabling service applications. Provisioning reliable SFC-enabled services in MEC thus is fundamentally important. However, the VNF instances deployed usually are not reliable and affected by multiple factors, including the software implementation, the request execution duration, and so on. Empowered by digital twin techniques that can maintain the states of VNF instances by digital twins in a real-time manner and predict the reliability of VNF instances in edge servers, in this paper we study SFC-enabled reliable service provisioning in an MEC network by exploring the dynamics of VNF instance placement reliability. We first formulate a novel SFC-enabled reliable service problem in MEC networks - the online throughput maximization problem, and show its NP-hardness. We then propose an Integer Linear Programming (ILP) solution to the offline version of the problem, and develop an online algorithm with a provable competitive ratio for the problem. We finally evaluated the performance of the proposed algorithm through experimental simulations, and the results demonstrate that the proposed algorithm is promising.

In this paper, we study end-to-end service reliability in Data Center Networks (DCN) with flow and Service Function Chains (SFCs) parallelism. In our approach, we consider large flows to i) be split into multiple parallel smaller sub-flows; ii) SFC along with their VNFs are replicated into at least as many VNF instances as there are sub-flows, resulting in parallel sub-SFCs; and iii) all sub-flows are distributed over multiple shortest paths and processed in parallel by parallel sub-SFCs. We study service reliability as a function of flow and SFC parallelism and placement of parallel active and backup sub-SFCs within DCN. Based on the probability theory and by considering both server and VNF failures, we analytically derive for each studied VNF placement method the probability that all sub-flows can be successfully processed by the parallelized SFC without service interruption. We evaluate the amount of backup VNFs required to protect the parallelized SFC with a certain level of service reliability. The results show that the proposed flow and SFC parallelism in DCN can significantly increase end-to-end service reliability, while reducing the amount of backup VNFs required, as compared to traditional SFCs with serial traffic flows.

Enabling elastic scaling is the crucial advantage of Network Function Virtualization (NFV), of which the common usage is Service Function Chain (SFC). But how to scale SFC elastically on an NFV platform while ensuring performance and cost-effectiveness, remains to be vague and challenging. We propose ESFC, a user space service chain management framework to reallocate computing resource of each NF components dynamically based on real-time statistics, while minimizing performance overhead. The ESFC framework monitors load on a service chain at high frequency (1000Hz) and employs proactive resource allocation policy to enable elastic scaling. ESFC utilizes an asynchronous mechanism to notify the to-be-scaled NF, which will be reallocated with computing resources via thread. Moreover, ESFC adopts a perfect distribution algorithm to reduce cache invalidation and state migration while scaling, which are crucial to performance. The results show that ESFC could adjust the devoted resources dynamically and achieve up to 3x higher cost-effectiveness without compromising performance. Comparing with the latest popular SFC platform, ESFC achieves higher performance up to 1.3x under the same condition.

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.

Efficient service reconfiguration with partial virtual network function migration

Attack-aware Security Function Management

In Network Function Virtualization (NFV), traditional purpose-built proprietary middle-boxes are replaced with virtual network function (VNF) instances that are deployed over generic hardware; network traffic flows are steered through a sequence of VNF instances (i.e., that forms a service function chain) after resolving the VNF Forward Graph Embedding (FGE) problem. NFV eases the network management and fosters the agile deployment of network services, thus enabling a foremost solution to address issues of traditional enterprise networks. With the everlasting Internet traffic growth, in this work, we extend the provision of NFV upon Elastic Optical Networks (EONs) that deliver abundant elastic and fine-granular bandwidth for connections between VNFs. We systematically examine the integrated NFV-EON architecture, and investigate one key enabler, namely the FGE-EON problem, where one has to coordinate the allocation of computing resources (as in a typical FGE problem) and the assignment of optical bandwidth (i.e., spectrum allocation in EONs). The contributions of this work are multi-fold. The FGE-EON problem is proven to be NP-Complete for the first time. An Integer Linear Programming (ILP) model is developed to address FGE-EON problem of small instances. Despite its complexity, with novel modeling techniques, the proposed ILP model maintains a compact structure even after accounting for VNF sharing and VNF co-location together for the first time. For large scale problems, we design a heuristic algorithm based on layered graph that is shown to be near-optimal in our evaluations.