Balanced resource allocation for VNF service chain provisioning in inter-datacenter elastic optical networks

Recently, Network Function Virtualization (NFV) as a promising technology has attracted great interest of the academia as well as industry. Most of the service providers are planning to use Virtual Network Functions (VNFs) to provide agile and flexible network services. In this paper, the problem of allocating both IT resources and optical resources for deploying VNF Service Chains (VNF-SCs) in inter-Datacenter Elastic Optical Networks (inter-DC EONs) is investigated. In this particular, we propose a heuristic algorithm named Resource Balancing Algorithm (RBA) for efficient joint placement of service chains and routing/spectrum assignment (RSA) in inter-DC EONs. The simulation results demonstrate improved performance of the proposed algorithm over the existing algorithm.

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.

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.

We study how to allocate spectrum and IT resources jointly for realizing efficient virtual network function (VNF) service chaining in inter-datacenter elastic optical networks. We first formulate an integer linear programming model to solve the problem exactly, and then a longest common subsequence-based heuristic is proposed. The simulation results indicate that the proposed algorithms can reuse the deployed VNFs efficiently and arrange the spectrum utilization in a much more load-balanced manner.

Middleboxes have become a vital part of modern networks by providing service functions such as content filtering, load balancing and optimization of network traffic. An ordered sequence of middleboxes composing a logical service is called service chain. Service Function Chaining (SFC) enables us to define these service chains. Recent optimization models of SFCs assume that the functionality of a middlebox is provided by a single software appliance, commonly known as Virtual Network Function (VNF). This assumption limits SFCs to the throughput of an individual VNF and resources of a physical machine hosting the VNF instance. Moreover, typical service providers offer VNFs with heterogeneous throughput and resource configurations. Thus, deploying a service chain with custom throughput can become a tedious process of stitching heterogeneous VNF instances. In this paper, we describe how we can overcome these limitations without worrying about underlying VNF configurations and resource constraints. This prospect is achieved by distributed deploying multiple VNF instances providing the functionality of a middlebox and modeling the optimal deployment of a service chain as a mixed integer programming problem. The proposed model optimizes host and bandwidth resources allocation, and determines the optimal placement of VNF instances, while balancing workload and routing traffic among these VNF instances. We show that this problem is NP-Hard and propose a heuristic solution called Kariz. Kariz utilizes a tuning parameter to control the trade-off between speed and accuracy of the solution. Finally, our solution is evaluated using simulations in data-center networks.

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 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 paradigm that virtualizes traditional network functions and instantiates Virtual Network Functions (VNFs) as software instances separate from hardware appliances. Service Chaining (SC), seen as one of the major NFV use cases, provides customized services to users by concatenating VNFs. A VNF placement model for SC that relaxes the visit order constraints of requested VNFs has been considered. Relaxing the VNF visit order constraints reduces the number of VNFs which need to be placed in the network. However, since the model does not permit any loop within an SC path, the efficiency of utilization of computation resources deteriorates in some topologies. This paper proposes a VNF placement model for SC which minimizes the cost for placing VNFs and utilizing link capacity while allowing both relaxation of VNF visit order constraints and configuration of SC paths including loops. The proposed model determines routes of requested SC paths, which can have loops, by introducing a logical layered network generated from an original physical network. This model is formulated as an Integer Linear Programming (ILP) problem. A heuristic algorithm is introduced for the case that the ILP problem is not tractable. Simulation results show that the proposed model provides SC paths with smaller cost compared to the conventional model.

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

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 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.

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.

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%.