Resource Balanced Service Chaining in NFV-enabled Inter-Datacenter Elastic Optical Networks

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

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.

Network function virtualization (NFV) decouples software implementations of network functions from their hosts (or hardware). NFV exposes a new set of entities, the virtualized network functions (VNFs). The VNFs can be chained with other VNFs and physical network functions to realize network services. This flexibility introduced by NFV allows service providers to respond in an agile manner to variable service demands and changing business goals. In this context, the efficient establishment of service chains and their placement becomes essential to reduce capital and operational expenses and gain in service agility. This paper addresses the placement aspect of these service chains by finding the best locations and hosts for the VNFs and to steer traffic across these functions while respecting user requirements and maximizing provider revenue. We propose a novel eigendecomposition-based approach for the placement of virtual and physical network function chains in networks and cloud environments. A heuristic based on a custom greedy algorithm is also presented to compare performance and assess the capability of the eigendecomposition approach. The performance of both algorithms is compared to a multi-stage-based method from the state of the art that also addresses the chaining of network services. Performance evaluation results show that our matrix-based method, eigendecomposition of adjacency matrices, has reduced complexity and convergence times that essentially depend only on the physical graph sizes. Our proposal also outperforms the related work in provider’s revenue and acceptance rate.

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.

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 design, management, and operation of network infrastructure have evolved during the last few years, leveraging on innovative technologies and architectures. With such a huge trend, due to the flexibility and significant economic potential of these technologies, software-defined networking (SDN) and network functions virtualization (NFV) are emerging as the most indispensable key catalysers. SDN/NFV enhancing the infrastructure agility, thus network operators and service providers are able to program their own network functions (e.g., gateways, routers, load balancers) on vendor-independent hardware substrate. One of the most important considerations in NFV deployment is how to allocate the virtual resources that are needed to provide flexible virtual network services in an NFV-based network infrastructure. Thus, the most important prerequisite for NFV deployment is achieved fast, scalable and dynamic composition and allocation of networks functions (NFs) to implement network services (NSs). We have proposed a revised RA algorithm that integrates embedding and scheduling of virtual network functions (VNFs) simultaneously. In this paper, performance evaluation of the revised RA algorithm is performed and the proposed algorithm is applied more effectively in NFV environment where the demand for resources is flexible and concentrated.

VNF Performance modelling: From stand-alone to chained topologies

Network Function Virtualization (NFV) brings flexibility and dynamics to the construction of service chain. However, the software and virtualization may cause security risks such as vulnerabilities and backdoors, which may have impact on Service Chain (SC) security. Thus, a Virtual Network Function (VNF) scheduling method is proposed. Firstly, heterogeneous images are built for every virtual network function in service chain, avoiding widespread attacks using common vulnerabilities. Then, one network function is selected dynamically and periodically. The executor of this network function is replaced by loading heterogeneous images. Finally, considering the impact of scheduling on the performance of network functions, Stackelberg game is used to model the attack and defense process, and the scheduling probability of each network function in the service chain is solved with the goal of optimizing the defender’s benefit. Experiments show that this method can reduce the rate of attacker’s success while controlling the overhead generated by the scheduling within an acceptable range.

To accelerate the implementation of network functions/middle boxes and reduce the deployment cost, recently the concept of Network Function Virtualization (NFV) has emerged and became a topic of much interest attracting the attention of researchers from both industry and academia. Unlike the traditional implementation of network functions, a software-oriented approach for network functions create more flexible and dynamic network services to meet a more diversified demand. In this paper, we study the Virtual Network Function (VNF) chaining scheduling problem with limited network resources. We consider VNF transmission and processing delays, and formulate the VNFs chaining scheduling as a new Mixed Integer Linear Programming (MILP) problem. Our objective is to minimize the latency of the overall VNFs' schedule. Reducing the scheduling latency enables cloud operators to service (and admit) more customers, thereby increasing operators' revenues. Owing to the complexity of the problem, we develop a Genetic Algorithm (GA) based method for solving the problem efficiently. Finally, the effectiveness of our heuristic algorithm is verified through numerical results.

By leveraging deep reinforcement learning (DRL), we design Deep-NFVOrch as a novel service framework with resource pre-deployment to achieve adaptive virtual network function (vNF) service chaining in inter-datacenter elastic optical networks (IDC-EONs).

Network Function Virtualization (NFV) is an emerging technology decouples network functions from hardware. Network service in NFV is deployed as a service chain, also known as Service Function Chain (SFC). SFC consists of an ordered set of Virtual Network Functions (VNFs). However, VNFs bring new challenges in providing network services with availability guarantee. In addition, in a customizable and dynamic NFV-enabled network, the composition and mapping of service chain are different from that of a traditional network. In this paper, we define an availability model that takes both hardware and VNF failures into consideration. Then we propose Joint Path-VNF backup model to combine path and VNF backup in a joint way. And a priority-based algorithm is designed for service chain composition and mapping. Simulation results show that our proposed solutions can reduce resource consumption while guaranteeing availability.

By using Network Function Virtualization (NFV), several kinds of network functions can be provided in commercial off-the-shelf (COTS) servers. Such network function is called Virtual Network Function (VNF). In terms of the utilization of VNFs, an emerging technology called service chaining enables network operators to provide network services dynamically and flexibly. Here, the number of VNF instances should be managed dynamically according to the amount of traffic. In this paper, we propose a dynamic management of VNF instances with Model Predictive Control (MPC) for multiple service chains. In the proposed method, the number of VNF instances is changed dynamically according to the predicted amount of traffic. We evaluate the performance of the proposed method and threshold method with simulation. In numerical examples, it is shown that the proposed method can avoid data loss with a small number of VNF instances.

Network Function Virtualization (NFV) has promoted the implementation of network functions from expensive hardwares to software middleboxes. These software middleboxes, also called Virtual Network Functions (VNFs), are executed on switch-connected servers. Efficiently deploying such VNFs on servers is challenging because the traffic rate of flows must be fully processed by their requested VNFs when they reach destinations, and the deployed positions of VNFs are restricted by the server capacity. In addition, each network function offers non-uniform VNF models (types) with different configurations of processing volumes and costs. This paper focuses on minimizing the total cost of deploying VNFs for providing a specific network function to all flows in tree-structured networks. First, we prove the NP-hardness of non-uniform VNF deployment in a tree topology and propose a dynamic programming based solution with a pseudo-polynomial time complexity. Then we narrow it down to three simplified cases by focusing on either uniform VNFs or the linear line topology. Specifically, three algorithms are introduced: an improved dynamic programming based algorithm for deploying uniform VNFs in a tree topology, a performance-guaranteed algorithm for deploying non-uniform VNFs in a linear line topology, and an optimal greedy algorithm for deploying uniform VNFs in a linear line topology. Additionally, we generalize our approach to a case of deploying a service chain, which consists of multiple network functions applied to flows in a specific order. We propose two solutions: one is optimal but time-consuming while another is heuristic but efficient. Extensive simulations are conducted to evaluate our algorithms.

Metropolitan networks are undergoing a major technological breakthrough leveraging the capabilities of software-defined networking (SDN) and network function virtualization (NFV). NFV permits the deployment of virtualized network functions (VNFs) on commodity hardware appliances which can be combined with SDN flexibility and programmability of the network infrastructure. SDN/NFV-enabled networks require decision-making in two time scales: short-term online resource allocation and mid-to-long term offline planning. In this paper, we first tackle the dimensioning of SDN/NFV-enabled metropolitan networks paying special attention to the role that latency plays in the capacity planning. We focus on a specific use-case: the metropolitan network that covers the Murcia - Alicante Spanish regions. Then, we propose a latency-aware multilayer service-chain allocation (LA-ML-SCA) algorithm to explore a range of maximum latency requirements and their impact on the resources for dimensioning the metropolitan network. We observe that design costs increase for low latency requirements as more data center facilities need to be spread to get closer to the network edge, reducing the economies of scale on the IT infrastructure. Subsequently, we review our recent joint computation of multi-site VNF placement and multilayer resource allocation in the deployment of a network service in a metro network. Specifically, a set of subroutines contained in LA-ML-SCA are experimentally validated in a network optimization-as-a-service architecture that assists an Open-Source MANO instance, virtual infrastructure managers and WAN controllers in a metro network test-bed.

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