Network Function Virtualization in Elastic Optical Networks

Network Function Virtualization (NFV) together with Software Defined Networking (SDN) offers the potential for enhancing service delivery flexibility and reducing overall costs. Based on the capability of dynamic creation and destruction of network function (NF) instances, NFV provides great elasticity in NF control, such as NF scaling out, scaling in, load balancing, etc. To realize NFV elasticity control, network traffic flows need to be redistributed across NF instances. However, deciding which flows are suitable for migration is a critical problem for efficient NFV elasticity control. In this paper, we propose to build an innovative flow migration controller, OFM Controller, to achieve optimized flow migration for NFV elasticity control. We identify the trigger conditions and control goals for different situations, and carefully design models and algorithms to address three major challenges including buffer overflow avoidance, migration cost calculation, and effective flow selection for migration. We implement the OFM Controller on top of NFV and SDN environments. Our evaluation results show that OFM Controller is efficient to support optimized flow migration in NFV elasticity control.

This paper focus on the routing, modulation level and spectrum allocation (RMLSA) problem in the next generation datacentre networks, which own the advantages of both Elastic Optical Networks (EON) and Software Defined Networking (SDN). We proposes an Integer Linear Programming (ILP) model for this problem. Simulation results show the ILP model achieves an outstanding load balancing performance.

Fault tolerant placement of stateful VNFs and dynamic fault recovery in cloud networks

To satisfy the various quality-of-service (QoS) re-quirements with minimum network costs, network functions virtualization (NFV) is proposed as an emerging wireless architecture that migrates network functions from dedicated hardware appliances to software instances running in virtual computing platforms. One crucial issue in NFV is to solve the orchestration of virtualized network functions (VNFs) to reduce costs and to improve the management flexibility of telecommunications service providers (TSPs). Particular, multiple objectives are required to be considered for orchestrating VNFs in order to achieve overall system performance. This can be optimally solved in small scale using integer linear programming (ILP) algorithms with high accuracy but low time efficiency. On the other hand, heuristic algorithms can be applied for solving part of the objectives in NFV resource allocation with high time efficiency but low accuracy. To tackle the above challenges, QMORA, a ${Q}$-learning based multi-objective resource allocation approach, is proposed to solve multi-objective optimization in NFV orchestration (NFVO) efficiently and accurately. Particularly, the approach includes reinforcement learning module and VNFs placement module. Reinforcement learning module is responsible for generating the “best” candidate paths. VNFs placement module is responsible for selecting optimal nodes on the generated candidate paths to host VNFs required for flows. The simulation results in the real ISP topology show that the proposed QMORA can balance the multi-objective including maximizing number of flows admitted to the network, minimizing path stretch, balancing the load among VNF instances and minimizing link occupation rate compared with other heuristic approaches.

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.

Efficient service reconfiguration with partial virtual network function migration

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

Acting as a promising technology to bring revolutionary changes to how the networks are architected, network function virtualization (NFV) leverages IT virtualization technologies to instantiate various types of virtual network functions (vNFs) flexibly and dynamically on commodity hardware, which can be easily found in a variety of NFV infrastructure points-of-presence, e.g., datacenters. In this paper, we investigate how to cost-effectively provision vNF graphs (vNFGs) with arbitrary topologies in a multidomain elastic optical network that consists of two domains, i.e., the private and public ones. We first formulate an integer linear programming (ILP) model with the objective to minimize the total resource cost of vNFG provisioning, and show that it can solve the problem exactly. With the ILP model, we analyze the complexity of the problem and prove that it is an $\mathcal {NP}$ -hard one. Then, we leverage the minimum $k$ -cut problem to design two time-efficient heuristics. The results from extensive simulations verify the performance of our proposed algorithms, and indicate that they can balance the IT and spectrum resource usages intelligently according to the resource price setting in multidomain environment.

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 integration of software defined networking (SDN) and network function virtualization (NFV) is a promising approach to support flexible service deployment and provisioning. In particular, NFV enables a network service to be represented as one network service function chain, which is composed of several virtual network functions in a specific order. However, with regard to service provisioning in multidomain SDN networks, virtual network functions are deployed in several different SDN domains due to limited resource capacity of each SDN domain. It results in the network service deployment complexity increase and causes additional operating cost. On the other hand, most current network service deployment proposals are limited to static user request scenarios. How to effectively deploy network service across multiple SDN domains to satisfy dynamic service demands of end users is a challenging problem. To address the aforementioned challenge, in this paper, we study the problem of dynamic network service deployment across multiple SDN domains. Specifically, we first design a novel service deployment framework to support dynamic deployment and optimization of virtual network function in multidomain SDN networks. Then, we formulate the problem as a multiobjective optimization problem with the target of jointly minimizing resource consumption cost and operating cost. Furthermore, we transform the multiobjective optimization problem into a single objective optimization model and present an efficient heuristic service deployment approach to solve it by leveraging Dijkstra algorithm. The simulation results demonstrate that the proposed service deployment method is efficient and outperforms comparison methods in terms of service rejection ratio, resource cost, and operating cost.

Diverse proprietary network appliances increase both the capital and operational expense of service providers, meanwhile causing problems of network ossification. Network function virtualization (NFV) is proposed to address these issues by implementing network functions as pure software on commodity and general hardware. NFV allows flexible provisioning, deployment, and centralized management of virtual network functions. Integrated with SDN, the software-defined NFV architecture further offers agile traffic steering and joint optimization of network functions and resources. This architecture benefits a wide range of applications (e.g., service chaining) and is becoming the dominant form of NFV. In this survey, we present a thorough investigation of the development of NFV under the software-defined NFV architecture, with an emphasis on service chaining as its application. We first introduce the software-defined NFV architecture as the state of the art of NFV and present relationships between NFV and SDN. Then, we provide a historic view of the involvement from middlebox to NFV. Finally, we introduce significant challenges and relevant solutions of NFV, and discuss its future research directions by different application domains.

The discovery process of OPC Unified Architecture (OPC-UA) is well defined but lacks flexibility when implemented in large networks. While the standard provides concepts for local and global discovery services, the core assumption is that all clients and servers are aware of the discovery server(s) in the network. This dependency is a barrier for Plug-and-Play installations and is overcome by self announces via the multicast Domain Name System (mDNS), however, this scales badly with an increasing number of network nodes and deployed servers. We propose a discovery approach based on Software-Defined Networking (SDN) and Network Function Virtualization (NFV), that installs a plug-and-play mechanism for global OPC-UA discovery in modern virtualized networks.

Network Function Virtualization (NFV) enables flexible deployment of network services as applications. Network operators expect to use a limited number of Network Function (NF) instances to handle the fluctuating traffic load and provide network services. However, it is a big challenge to guarantee the Quality of Service (QoS) under the unpredictable network traffic while minimizing the processing resources. One typical solution is to realize NF scale-out, scale-in and load balancing by elastically migrating the related traffic flows with SoftwareDefined Networking (SDN). However, it is difficult to optimally migrate flows since many real-time statuses of NF instances should be considered to make accurate decisions. In this paper, we propose DeepMigration to solve the problem by efficiently and dynamically migrating traffic flows among different NF instances. DeepMigration is a Deep Reinforcement Learning (DRL)-based solution coupled with Graph Neural Network (GNN). By taking advantages of the graph-based relationship deduction ability from our customized GNN and the self-evolution ability from the experience training of DRL, DeepMigration can accurately model the cost (e.g., migration latency) and the benefit (e.g., reducing the number of NF instances) of flow migration among different NF instances and generate dynamic and effective flow migration policies to improve the QoS. Experiment results show that DeepMigration requires less migration cost and saves up to 71.6{%} of the computation time than existing solutions.

To accommodate heterogeneous and sophisticated network services, Network Function Virtualization (NFV) is invented as a hopeful networking technology. The most distinct feature of NFV is that it separates network functions from physical hardware. In the NFV architecture, various types of Virtual Network Functions (VNFs) are placed on specific software-based middleboxes by telecom providers. Traffic traverses through a sequence of Virtual Network Functions (VNFs) in pre-defined order, which is named as Service Function Chain (SFC). However, how to effectively place VNFs at different locations and steer SFC requests while minimizing energy consumption is still an open problem. Accordingly, we investigate on the joint optimization of VNF placement and traffic steering for energy efficiency in telecom networks. We first present the power consumption model in NFV-enabled telecom networks, and then formulate the studied problem as an Integer Linear Programming (ILP) model. Since the problem is proved as NP-hard, we design a polynomial algorithm that can achieve near-optimal performances based on the Markov approximation technique. In addition, our algorithm can be extended to an online version to serve dynamic arriving SFC requests. The online algorithm achieves a near-optimal long-term averaged performance. Extensive simulation results show that compared with the benchmark algorithms, in the offline and online scenario, our algorithm can reduce up to 14.08 and 13.72 percent power consumption in telecom networks, respectively.

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.