Chain Of Virtual Network Functions Research Articles

Dual: Deploy stateful virtual network function chains by jointly allocating data-control traffic

Network Function Virtualization (NFV) allows to deploy network functions at low cost and high flexibility. Usually traffic needs to pass through several network functions in a particular order, which is known as network function chaining. Due to capacity limitation, one instance of a particular network function in the chain usually needs to be scaled to multiple instances when the traffic load increases. Recent studies show that network functions maintain rich internal states, which correlate with flows and determine their processing actions. In order to guarantee network functions to behave the same as before the scaling, these states on multiple instances need to be synchronized. As a result, communication requirement for synchronizing these states arises. Our experimental investigations show that such communication traffic for state synchronization is non-negligible. Unfortunately, to our best knowledge, no existing work for network function chain deployment considered such traffic. In this paper, we consider the problem of allocating both flow traffic and synchronization traffic with the goal to minimize the consumption of network interface card’s bandwidth, which is considered as the bottleneck resource of the network. The main challenge is that such two kinds of traffic are correlated and even conflicting. To deal with this challenge, a new graph-based model named Dual (Deploy Stateful Virtual Network Function Chains by Jointly Allocating Data-Control Traffic) is proposed, which characterizes the interaction of data-control traffic in an elegant manner. Based on Dual, the problem mentioned above is formulated through ILP and an approximation algorithm is designed. Extensive simulations show that the proposed approach is effective in terms of reducing server bandwidth usage.

Joint Energy Efficient and QoS-Aware Path Allocation and VNF Placement for Service Function Chaining

Service function chaining (SFC) allows the forwarding of traffic flows along a chain of virtual network functions (VNFs). Software defined networking (SDN) solutions can be used to support SFC to reduce both the management complexity and the operational costs. One of the most critical issues for the service and network providers is the reduction of energy consumption, which should be achieved without impacting the Quality of Service. In this paper, we propose a novel resource allocation architecture which enables energy-aware SFC for SDN-based networks, considering also constraints on delay, link utilization, server utilization. To this end, we formulate the problems of VNF placement, allocation of VNFs to flows, and flow routing as integer linear programming (ILP) optimization problems. Since the formulated problems cannot be solved (using ILP solvers) in acceptable timescales for realistic problem dimensions, we design a set of heuristic to find near-optimal solutions in timescales suitable for practical applications. We numerically evaluate the performance of the proposed algorithms over a real-world topology under various network traffic patterns. Our results confirm that the proposed heuristic algorithms provide near-optimal solutions (at most 14% optimality-gap) while their execution time makes them usable for real-life networks.

Adaptive placement & chaining of virtual network functions with NFV-PEAR

The design of flexible and efficient mechanisms for proper placement and chaining of virtual network functions (VNFs) is key for the success of Network Function Virtualization (NFV). Most state-of-the-art solutions, however, consider fixed (and immutable) flow processing and bandwidth requirements when placing VNFs in the Network Points of Presence (N-PoPs). This limitation becomes critical in NFV-enabled networks having highly dynamic flow behavior, and in which flow processing requirements and available N-PoP resources change constantly. To bridge this gap, we present NFV-PEAR, a framework for adaptive VNF placement and chaining. In NFV-PEAR, network operators may periodically (re)arrange previously determined placement and chaining of VNFs, with the goal of maintaining acceptable end-to-end flow performance despite fluctuations of flow processing costs and requirements. In parallel, NFV-PEAR seeks to minimize network changes (e.g., reallocation of VNFs or network flows). The results obtained from an analytical and experimental evaluation provide evidence that NFV-PEAR has potential to deliver more stable operation of network services, while significantly reducing the number of network changes required to ensure end-to-end flow performance.

End-to-End Delay Modeling for Embedded VNF Chains in 5G Core Networks

In this paper, an analytical end-to-end (E2E) packet delay modeling is established for multiple traffic flows traversing an embedded virtual network function (VNF) chain in fifth generation communication networks. The dominant-resource generalized processing sharing is employed to allocate both computing and transmission resources among flows at each network function virtualization (NFV) node to achieve dominant-resource fair allocation and high resource utilization. A tandem queueing model is developed to characterize packets of multiple flows passing through an NFV node and its outgoing transmission link. For analysis tractability, we decouple packet processing (and transmission) of different flows in the modeling and determine average packet processing and transmission rates of each flow as approximated service rates. An M/D/1 queueing model is developed to calculate packet delay for each flow at the first NFV node. Based on the analysis of packet interarrival time at the subsequent NFV node, we adopt an M/D/1 queueing model as an approximation to evaluate the average packet delay for each flow at each subsequent NFV node. The queueing model is proved to achieve more accurate delay evaluation than that using a G/D/1 queueing model. Packet transmission delay on each embedded virtual link between consecutive NFV nodes is also derived for E2E delay calculation. Extensive simulation results demonstrate the accuracy of our proposed E2E packet delay modeling, upon which delay-aware VNF chain embedding can be achieved.

Automated Function Placement and Online Optimization of Network Functions Virtualization

This paper proposes a new fully decentralized approach to online placement and optimization of virtual machines (VMs) for network functions virtualization (NFV). The approach is of practical value, as network services comprising a chain of virtual network functions (VNFs) are proposed to be queued on the basis of leading unexecuted VNFs at every server, rather than on the typical basis of services, reducing queues per server and facilitating queue management and signaling. It is also non-trivial because the VNFs of network services must be executed correctly in order at different VMs, coupling the optimal decisions of VMs on processing or offloading. Exploiting Lyapunov optimization techniques, we decouple the optimal decisions by deriving and minimizing the instantaneous upper bound of the NFV cost in a distributed fashion, and achieve the asymptotically minimum time-average cost. We also reduce the queue length by allowing individual VMs to (un)install VNFs based on local knowledge, achieving stable redeployment of VNFs, adapting to the network topology and the temporal and spatial variations of services. Simulations show that the proposed approach is able to reduce the time-average cost of NFV by 71% and reduce the queue length (or delay) by 74%, as compared with existing approaches.

Virtual network function placement and resource optimization in NFV and edge computing enabled networks

Network function virtualization (NFV) and edge computing (EC) are two promising and innovative technologies to accelerate 5G networks. However, placing the service function chains (SFC), each of which consists of a series of ordered virtual network functions (VNFs), into the EC enabled networks is an intractable issue and some new challenges shall arise. Firstly, EC is a hierarchical and geo-distributed structure, which will influence the form of SFCs and make the VNF placement location-related. Secondly, the data processing in EC is hierarchical too, which incurs different latency requirements. In this paper, we study the VNF placement problem considering users’ SFC requests (SFCr) in NFV and EC enabled networks. Apart from the above new challenges, the implementation method and chaining of VNFs are also considered, which will raise the need of tradeoff between node resource consumption and bandwidth consumption when placing VNFs. Then the above problem is formulated as an integer linear programming (ILP) model mathematically aiming to minimize the total resource consumption, which is proven to be NP-hard. We get the optimal results when the number of SFCrs is small taking advantage of optimization solver and propose a polynomial time heuristic when the problem scale is large. Simulation results show that the resource consumption derived by our heuristic solution is near to the optimal solution and its performance is very much superior to the contrastive schemes.

Resource Aware Chaining and Adaptive Capacity Scaling for Service Function Chains in Distributed Cloud Network

With the development of network technology such as software-defined network (SDN) and network function virtualization (NFV), Internet service providers (ISPs) are increasingly placing the virtual network function(VNF) instances at the network edge to provide network service. However, there are some issues to be tackled in the distributed SDN/NFV enabled cloud. Firstly, VNF instances require to be chained in predefined order to provide network services. It is a challenge to optimally select and chain VNF instances from the multi-instances. Moreover, due to the capacity limitation of the distributed edge nodes. The capacity of the Virtual Machines (VMs) that host VNFs should be proactively adjusted to cope with traffic demands. Since most existing works ignore the vertical capacity scaling problem in routing commodities with Service Function Chain (SFC) requests. In this paper, a fine-grained scheduling scheme at VM-level is proposed. Firstly, we formulate the SFC chaining problem as an Integer Linear Programming (ILP) model aiming to embed SFC requests with minimum estimated latency cost. Furthermore, we formulate the adaptive VNF resource allocation (VNF-AR) problem as a convex optimization. The theoretical optimal capacity for each VM can be derived from the Karush-Kuhn Tucker (KKT) conditions. At last, a novel joint optimization approach of VNF chaining and adaptive scaling (VNF-CAS) is proposed to efficiently embed the SFC requests. Performance evaluation shows that VNF-CAS can achieve better performance in SFC requests acceptance rate, average effective throughput, average load utilization and VM load balancing when it is compared with other algorithms in existing works.

On efficient virtual network function chaining in NFV-based telecommunications networks

Network function virtualization enables the softwarization of network functions on standardized commodity hardware, which is promising to help the network carriers to achieve a lower investment cost, a shorter time to deployment, as well as a more flexible and dynamic way of network configuration and management. One of the key challenges is how to efficiently place and chain the software-based virtual network functions in the physical substrate to provision the requested network service while minimizing the physical network cost. In this paper, we mathematically formulate the virtual network function chaining problem using Integer Linear Programming (ILP), in order to facilitate an optimal solution. We propose a set of centralized algorithms to efficiently minimize the physical network cost. The proposed Close To Destination (CTD) algorithm can achieve a near-optimal physical network cost that is close to the optimal result obtained from the ILP solution, but with a very low computational complexity. In addition, we propose a distributed algorithm, called Look Ahead (LA), which plans ahead and jointly considers multiple next-hops VNF requirements to facilitate an efficient VNF chain forwarding decision. Comprehensive simulations are conducted to evaluate the proposed approaches, and the results demonstrate the effectiveness of the proposed CTD algorithm and the distributed LA algorithm.

VNF placement for service chaining in a distributed cloud environment with multiple stakeholders

The adoption of virtualization technologies in networking is promoting a radical innovation in the way network services are managed and delivered. Indeed, some network services may be provisioned to cope with complex and unpredictable traffic demands by dynamically creating a sequence of Virtual Network Functions (VNFs) and steering traffic flows through them. In this context, the optimized deployment of network services, composed of VNFs that may be instantiated in multiple Data Centers (DCs), is one of the most challenging orchestration target. VNF placement is the problem of choosing the set of optimal locations for a chain of VNFs according to the service request and the current characteristics of available computing resources and network links. With respect to the state of the art, our original contribution reflects a multi-stakeholder perspective (subscriber, service providers, infrastructure providers) in a multi-DC environment. We thus consider the problem of placing VNFs to maximize primarily the number of accepted requests from a set of incoming requests and secondarily the satisfaction of subscribers’ preferences. Our model also allows to differentiate service requests in priority levels and guarantees that Quality of Service objectives for accepted service requests are fulfilled, including also a requirement on network service instantiation time. We provide an integer linear programming formulation of this problem that leverages a layered auxiliary graph built for each request in a set. Experimental evaluation is described in detail and an assessment of the proposed placement approach is performed along three main directions: (i) service acceptance ratio in online and offline placement, (ii) preferences’ satisfaction, and (iii) scalability expressed in terms of computational time. The performance of the approach is also compared to a greedy heuristic.

Optimal orchestration of virtual network functions

The emergence of Network Functions Virtualization (NFV) is bringing a set of novel algorithmic challenges in the operation of communication networks. NFV introduces volatility in the management of network functions, which can be dynamically orchestrated, i.e., placed, resized, etc. Virtual Network Functions (VNFs) can belong to VNF chains, where nodes in a chain can serve multiple demands coming from the network edges. In this paper, we formally define the VNF placement and routing (VNF-PR) problem, proposing a versatile linear programming formulation that is able to accommodate specific features and constraints of NFV infrastructures, and that is substantially different from existing virtual network embedding formulations in the state of the art. We also design a math-heuristic able to scale with multiple objectives and large instances. By extensive simulations, we draw conclusions on the trade-off achievable between classical traffic engineering (TE) and NFV infrastructure efficiency goals, evaluating both Internet access and Virtual Private Network (VPN) demands. We do also quantitatively compare the performance of our VNF-PR heuristic with the classical Virtual Network Embedding (VNE) approach proposed for NFV orchestration, showing the computational differences, and how our approach can provide a more stable and closer-to-optimum solution.

CPVNF: Cost-Efficient Proactive VNF Placement and Chaining for Value-Added Services in Content Delivery Networks

Value-added services (e.g., overlaid video advertisements) have become an integral part of today’s content delivery networks (CDNs). To offer cost-efficient, scalable, and more agile provisioning of new value-added services in CDNs, network functions virtualization paradigm may be leveraged to allow implementation of fine-grained services as a chain of virtual network functions (VNFs) to be placed in CDN. The manner in which these chains are placed is critical as it both affects the quality of service (QoS) and provider cost. The problem is however, very challenging due to the specifics of the chains (e.g., one of their end-points is not known prior to the placement). We formulate it as an integer linear program and propose a cost efficient proactive VNF placement and chaining algorithm. The objective is to find the optimal number of VNFs along with their locations in such a manner that the cost is minimized while QoS is met. Apart from cost minimization, the support for large-scale CDNs with a large number of servers and end-users is an important feature of the proposed algorithm. Through simulations, the algorithm’s behavior for small-scale to large-scale CDN networks is analyzed.

Sustainable green data center: Guaranteeing flow deadlines in chains of virtual network functions with MRouting

Power consumption has always been a challenging problem in data centers. Green data center has been proposed to alleviate the growing tendency of power consumption from the aspects of architecture and resource management. For improving the network performance and guaranteeing the quality of service (QoS) requirements of data flows, various kinds of network function virtualization (NFV) devices, which are equipped with multiple hardware resources, are ubiquitously deployed in data centers. Meanwhile, a chain of virtual network functions (VNFs) is usually needed to be sequentially executed on flows. Existing queueing schemes used in multi-resource environments are insufficient to guarantee the QoS requirements of flows in this situation. Based on this observation, we modeled and analyzed the flow scheduling problem in chains of VNFs. Further, we proposed a scheduling framework, named MRouting, to guarantee the deadline requirement of flows. MRouting determines the scheduling sequence of flows according to their urgency degree. It also implements appropriate data rate control and routing selection for flows, so that they can finish their transmissions before their deadlines. We have conducted extensive experiments to evaluate the performance of MRouting. The results verified that MRouting efficiently guaranteed the deadlines of flows, and also achieved obvious improvements on flow completion time and resource utilization.

VSPACE: VNF Simultaneous Placement, Admission Control and Embedding

In future wireless networks, network functions virtualization lays the foundations for establishing a new dynamic resource management framework to efficiently utilize network resources. In this paper, a network service can be viewed as a chain of virtual network functions (VNFs), called a service function chain (SFC), served via placement, admission control (AC), and embedding into network infrastructure, based on the resource management objectives and the state of network. To fully exploit such a potential and reach higher network performance, resource management stages should be jointly performed. To this end, two main challenges are: how to present a system model that formulates the desired resource allocation problem for different types of SFCs as well as different features, and how to tackle the computational complexity of the problem and solve it in a tractable manner. In this paper, we address these two issues and solve the joint problem of AC and SFC embedding. We introduce a comprehensive system model, and formulate the joint task as a mixed integer linear programming. This formulation encompasses splittable VNF and multi-path routing scenarios. We employ relaxation, reformulation, and successive convex approximation methods to solve the problem. Simulation results demonstrate that the proposed schemes outperform the earlier works.

Reliability-Aware Service Chaining In Carrier-Grade Softwarized Networks

Network Function Virtualization (NFV) has revolutionized service provisioning in cloud datacenter networks. It enables the complete decoupling of Network Functions (NFs) from the physical hardware middle boxes that network operators deploy for implementing service-specific and strictly ordered NF chains. Precisely, NFV allows for dispatching NFs as instances of plain software called virtual network functions (VNFs) running on virtual machines hosted by one or more industry standard physical machines. Nevertheless, NF softwarization introduces processing vulnerability ( e.g. , failures caused by hardware or software, and so on). Since any failure of VNFs could break down an entire service chain, thus interrupting the service, the functionality of an NFV-enabled network will require a higher reliability compared with traditional networks. This paper encloses an in-depth investigation of a reliability-aware joint VNF chain placement and flow routing optimization. In order to guarantee the required reliability, an incremental approach is proposed to determine the number of required VNF backups. Through illustration, it is shown herein that the formulated single path routing model can be easily extended to support resource sharing between adjacent backup VNF instances. This paper advocates the absolute existence of a share-resource-based VNF assignment strategy that is capable of trading off all of the reliability, bandwidth, and computing resources consumption of a given service chain. A heuristic is proposed to work around the complexity of the presently formulated integer linear programming (ILP). Thorough numerical analysis and simulations are conducted in order to verify and assert the validity, correctness, and effectiveness of this proposed heuristic reflecting its ability to achieve very close results to those obtained through the resolution of the complex ILP within a negligible amount of time. Above and beyond, the proposed resource-sharing-based VNF placement scheme outperforms existing resource-sharing agnostic schemes by 15.6% and 14.7% in terms of bandwidth and CPU utilization respectively.

On the Interplay Between Network Function Mapping and Scheduling in VNF-Based Networks: A Column Generation Approach

Middleboxes (i.e., firewall, cache, proxy, etc.) are hardware appliances designed to enforce security and performance policies. Being an integral part of today’s cloud and enterprise networks, these middleboxes are expensive, hard to manage and to maintain. Network function virtualization has emerged as a promising technology that replaces these hardware appliances by software ones known as virtual network functions (VNFs). Unlike hardware middleboxes, VNFs can be instantiated and deployed on virtual machines running on commodity servers which ensures their flexibility, manageability, cost-efficiency, and reduce their time-to-market. However, efficiently processing services through an ordered chain of VNFs, called service function chaining (SFC), is not trivial. It requires solving three inter-related sub-problems; the network functions (NFs) mapping sub-problem, the traffic routing sub-problem and the service scheduling sub-problem. This paper first highlights the existing interplay between the three sub-problems and then presents a formulation of the SFC scheduling (SFCS) which exploits interactions between NFs mapping onto VNFs, service scheduling and traffic routing. Given the complexity of the SFCS problem, we present a novel primal–dual decomposition using column generation that solves exactly a relaxed version of the problem and can serve as a benchmark approach. We enhance our solution methodology with a diversification technique to help improve the quality of the obtained solutions. We evaluate numerically our method and show that it can attain optimal solutions substantially faster. Finally, we present several engineering insights for improving the network performance.

On the Trade-Off Between Computational Load and Reliability for Network Function Virtualization

Network Function Virtualization (NFV) enables the "softwarization" of network functions, which are implemented on virtual machines hosted on Commercial off-the-shelf (COTS) servers. Both the composition of the virtual network functions (VNFs) into a forwarding graph (FG) at the logical layer and the embedding of the FG on the servers need to take into account the less-than-carrier-grade reliability of COTS components. This work investigates the trade-off between end-to-end reliability and computational load per server via the joint design of VNF chain composition (CC) and FG embedding (FGE) under the assumption of a bipartite FG that consists of controller and regular VNFs. Evaluating the reliability criterion within a probabilistic model, analytical insights are first provided for a simplified disconnected FG. Then, a block coordinate descent method based on mixed-integer linear programming is proposed to tackle the joint optimization of CC and FGE. Via simulation results, it is observed that a joint design of CC and FGE leads to substantial performance gains compared to separate optimization approaches.

VNF-EQ: dynamic placement of virtual network functions for energy efficiency and QoS guarantee in NFV

With the advances of network function virtualization and cloud computing technologies, a number of network services are implemented across data centers by creating a service chain using different virtual network functions (VNFs) running on virtual machines. Due to the complexity of network infrastructure, creating a service chain requires high operational cost especially in carrier-grade network service providers and supporting stringent QoS requirements from users is also a complicated task. There have been various research efforts to address these problems that only focus on one aspect of optimization goal either from users such as latency minimization and QoS based optimization, or from service providers such as resource optimization and cost minimization. However, meeting the requirements both from users and service providers efficiently is still a challenging issue. This paper proposes a VNF placement algorithm called VNF-EQ that allows users to meet their service latency requirements, while minimizing the energy consumption at the same time. The proposed algorithm is dynamic in a sense that the locations or the service chains of VNFs are reconfigured to minimize the energy consumption when the traffic passing through the chain falls below a pre-defined threshold. We use genetic algorithm to formulate this problem because it is a variation of the multi-constrained path selection problem known as NP-complete. The benchmarking results show that the proposed approach outperforms other heuristic algorithms by as much as 49% and reduces the energy consumptions by rearranging VNFs.

Online Stochastic Buy-Sell Mechanism for VNF Chains in the NFV Market

With the recent advent of network functions virtualization (NFV), enterprises and businesses are looking into network service provisioning through the service chains of virtual network functions (VNFs), instead of relying on dedicated hardware middleboxes. Accompanying this trend, an NFV market is emerging, where NFV service providers create VNF instances, assemble VNF service chains, and sell them for the use of customers, using resources (computing, bandwidth) that they own or rent from other resource suppliers. Efficient service chain provisioning and pricing mechanisms are still missing, to charge assembled service chains according to demand and the supply of resources at any time. We propose an online stochastic auction mechanism for on-demand service chain provisioning and pricing at an NFV provider. Our auction takes in buy bids for service chains from multiple customers and sell bids from various resource suppliers to supplement the NFV provider’s geo-distributed resource pool, with resource occupation/contribution durations. We extend online primal-dual optimization framework for handling both buyers and sellers, with a new competitive analysis. The online mechanism maximizes the expected social welfare of the NFV ecosystem (the NFV provider, customers and resource suppliers) with a good competitive ratio as compared with the expected offline optimal social welfare, while guaranteeing truthfulness in bidding, individual rationality for both buyers and sellers, and polynomial time for computation. We evaluate our mechanism through trace-driven simulation studies, and demonstrate a close-to-offline-optimal performance in expected social welfare under realistic settings.

A fix-and-optimize approach for efficient and large scale virtual network function placement and chaining

Network Function Virtualization (NFV) is a novel concept that is reshaping the middlebox arena, shifting network functions (e.g. firewall, gateways, proxies) from specialized hardware appliances to software images running on commodity hardware. This concept has potential to make network function provision and operation more flexible and cost-effective, paramount in a world where deployed middleboxes may easily reach the order of hundreds. In spite of recent research activity in the field, little has been done towards efficient and scalable placement & chaining of virtual network functions (VNFs) – a key feature for the effective success of NFV. More specifically, existing strategies have either neglected the chaining aspect of NFV, focusing on efficient placement only, or failed to scale to hundreds of network functions. In this paper, we approach VNF placement and chaining as an optimization problem, and propose a fix-and-optimize-based heuristic algorithm for tackling it. Our algorithm incorporates a Variable Neighborhood Search (VNS) meta-heuristic, for efficiently exploring the placement and chaining solution space. The goal is to minimize required resource allocation, while meeting network flow requirements and constraints. We provide evidence that our algorithm is able to find feasible, high quality solutions efficiently, even in scenarios scaling to hundreds of VNFs.

Scalable and coordinated allocation of service function chains

Network Functions Virtualization is an emerging initiative where standard IT virtualization evolves to consolidate network functions onto high volume servers, switches and storage that can be located anywhere in the network. In NFV, network services are built by chaining a set of Virtual Network Functions (VNFs) that must be allocated on top of the physical network infrastructure (commodity hardware). This challenge is commonly known as the NFV resource allocation problem, that is divided in two problem stages: 1) service chain composition and 2) service chain embedding. Up to now, existing approaches do not scale with regard to problem size. In this paper, we address this problem and propose CoordVNF, a heuristic method to coordinate the composition of VNF chains and their embedding into the substrate network. Evaluation results show that the heuristic is able to quickly solve the allocation problem even in substrate network topologies with hundreds of nodes.