Chain Of Virtual Network Functions Research Articles

Dynamic Joint VNF Forwarding Graph Composition and Embedding: A Deep Reinforcement Learning Framework

Network Function Virtualization (NFV) is a network service deployment technology that reduces capital and operational costs while yielding flexibility and scalability for service operators. As such, an ordered chain of Virtual Network Functions (VNFs), known as a VNF Forwarding Graph (VNF-FG), should be composed and embedded into the underlying substrate network. In the literature, the composition and embedding stages of VNF-FGs are usually targeted separately, which may result in undesired solutions. In this paper, we propose our joint VNF-FG composition and embedding solution, which considers the variations of service demands while also accounting for dynamic network conditions. Specifically, our proposed solution relies on deep reinforcement learning empowered by two components for estimating dynamic parameters: network resource utilization and service demand analyzers. Moreover, to efficiently explore the problem’s large discrete action space, we utilize a specialized branching Q-network and enhance it with an action filtering mechanism. We evaluated our proposed method against joint and disjoint composition and embedding heuristics as well as versus other deep learning-based methods. Our results show that the proposed method can achieve up to a 95% improvement of embedding cost compared to our benchmarks.

Virtual Network Function Placement for Serving Weighted Services in NFV-Enabled Networks

Network function virtualization (NFV) is a novel network architecture yielding many optimization choices to Internet service providers due to its flexibility. Based on this technique, a network service is constructed from a chain of virtual network functions (VNFs). Although many studies deeply investigate the NFV and propose approaches to address the problem of deploying services in physical networks under different perspectives, they do not consider the importance of services. This issue becomes more critical once the number of network services ever-increasing while the network resources are limited. Here, each service is assigned a weight representing its importance. From this aspect, maximizing the total weight of services accepted to deploy over the physical network is a critical problem and poses many challenges, particularly for networks with limited resources. Therefore, we call this issue the VNF placement for weighted services (VPWS) problem. We then propose three algorithms termed first-fit server algorithm (FSA), best-fit server algorithm (BSA), and service segment-based algorithm (SSBA), respectively, to address the VPWS problem. Eventually, we perform simulations to evaluate the proposed algorithms and factors that affect the VNF placement. The experiment indicates that the total weight yielded by the SSBA outperforms those of the FSA and BSA.

Dynamic Service Provisioning in the Edge-Cloud Continuum With Bounded Resources

We consider a hierarchical edge-cloud architecture in which services are provided to mobile users as chains of virtual network functions. Each service has specific computation requirements and target delay performance, which require placing the corresponding chain properly and allocating a suitable amount of computing resources. Furthermore, chain migration may be necessary to meet the services’ target delay. We model and formalize the problem of finding a feasible chain placement and resource allocation, while minimizing the migration, bandwidth, and computation costs. We tackle this problem by partitioning it into a (i) CPU allocation problem, and a (ii) placement problem. For the CPU allocation problem, we find an optimal solution. For the placement problem, we show that even finding a feasible solution is NP-hard, and envision an algorithm that is guaranteed to find a feasible solution while leveraging a bounded amount of resource augmentation. Our algorithms are incorporated into a solution framework that aims to minimize both the cost and the required resource augmentation. The results, obtained through trace-driven, large-scale simulations, show that our framework can provide a close-to-optimal solution while running several orders of magnitude faster than an ILP solver.

HyScaler: A Dynamic, Hybrid VNF Scaling System for Building Elastic Service Function Chains Across Multiple Servers

Dynamically scaling Virtual Network Function (VNF) is essential for network operators to make their networks accommodate time-varying network traffic. However, current research only applies homogeneous VNF scaling or focuses on mathematical optimizations without implementations in a real-world environment. To build this gap, this paper presents HyScaler, a dynamic, hybrid VNF scaling system implemented in an open-source NFV platform. With HyScaler, we can build elastic Service Function Chains (SFCs) by scaling overloaded VNF Instances (VNFIs) across multiple CPU cores or physical servers. HyScaler implements this by (1) monitoring traffic loads for each deployed VNFI; (2) scaling overloaded VNFIs through all kinds of VNF scaling methods; and (3) placing and chaining the new VNFI using the Global VNF Placement and Chaining (GVPC) algorithm. Extensive experiments are conducted to validate the scalability of our HyScaler and the effectiveness of the GVPC algorithm. Compared with the original NFV platform, experimental results show that HyScaler can improve the performance of VNFIs by about 1.02 times.

Availability‐aware virtual network function placement based on multidimensional universal generating functions

AbstractNetwork function virtualization (NFV) implements network functions as software, which enables flexible, resource‐efficient, and rapid provision of network services. In NFV, network services are realized by the service function chain (SFC), which is a chain of virtual network functions (VNFs) linked in the proper order. Both availability and deployment cost are key concerns for network operators providing network services as SFC. This paper proposes a flexible VNF placement model on a per‐VNF instance basis that minimizes deployment costs while satisfying availability requirements that may be placed on SFC. This paper uses a multidimensional universal generating function (MUGF) method, which is a multistate system analysis method, to compute the availability of a multistate SFC system with multiple VNFs coexisting on a server. The MUGF method calculates the performance of the entire SFC by combining the performance of servers as determined by applying a continuous‐time Markov chain. To reduce the time to compute the SFC availability, we introduce operators to be applied to MUGF and develop an availability computing method. In addition, a heuristic algorithm for determining VNF placement targeting the lowest deployment cost possible while meeting availability requirements is presented. Numerical results show that the proposed model obtains VNF placement with lower cost than the conventional model in all examined cases. The proposed model achieves VNF placement at 58.5%–75.0% of the deployment cost of the conventional model for the same SFC availability requirements.

Double-Agent Reinforced vNFC Deployment in EONs for Cloud-Edge Computing

With an effective service provisioning strategy that relies on Network Function Virtualization (NFV), cloud-edge computing can enhance the Quality of Service (QoS) in Elastic Optical Networks (EONs). NFV emerges as a promising technology to provide flexible services by orchestrating different virtual Network Function Chains (vNFCs). However, the vNFC deployment process is complex and involves two stages, i.e., allocating diverse Virtual Network Functions (VNFs) onto different physical nodes and routing suitable paths for Virtual Links (VLs). How to coordinate the above two stages to deploy vNFCs efficiently in EONs for cloud-edge computing is extremely important. Previous vNFC deployment algorithms are heuristic policies that disregard heterogeneous characteristics in cloud-edge computing. Recently, single-agent Deep Reinforcement Learning (DRL) can provide adaptive allocation schemes by perceptual learning from the environment. Whereas, its performance is not as good as expected since it makes the process of VFNs deployment and VLs deployment independent. Therefore, we propose a Double-Agent Reinforced vNFC Deployment algorithm (DARD) to integrate the VNFs and VLs deployment stages by two cooperative DRL agents. Additionally, we formulate the vNFC deployment problem as a Mixed Integer Linear Programming (MILP) model to achieve optimal solutions. The performance of DARD is evaluated in both static and dynamic scenarios. Simulation results show that DARD can achieve approaching performance with the MILP model. Moreover, it performs better than the other five state-of-art algorithms.

Reliable Dynamic Service Chain Scheduling in 5G Networks

As a key enabler of future 5G network, Service Function Chain (SFC) forwards the traffic flow along a chain of Virtual Network Functions (VNFs) to provide network services flexibility. One of the most important problems in SFC is to deploy the VNFs and schedule arriving requests among computing nodes to achieve low latency and high reliability. Existing works consider a static network and assume that all SFC requests are known in advance, which is impractical. In this paper, we focus on the dynamic 5G network environment where the SFC requests arrive randomly and the computing nodes can redeploy all types of VNF with a time cost. We formulate the problem of SFC scheduling in NFV-enabled 5G network as a mixed integer non-linear programing. The objective is to maximize the number of requests satisfying the latency and reliability constraints. To solve the problem, we propose an efficient algorithm to decide the redundancy of the VNFs while minimizing delay. Then we present a state-of-art Reinforcement Learning (RL) to learn SFC scheduling policy to increase the success rate of SFC requests. The evaluation result shows that our proposed RL solution can increase the success rate by 18.7% over the benchmark algorithms.

DeepMetricCorr: Fast flow correlation for data center networks with deep metric learning

Flow correlation is a crucial task for operators to efficiently manage data center networks, as it provides a holistic perspective of the data center network by correlating ingress flow at each network node with the corresponding egress flow on a hop-by-hop basis. Existing attempts to solve the flow correlation problem involve traditional and feature-based methods, which have major limitations in application scenarios, processing speed and accuracy in the dynamic data center network environment, especially at the presence of chains of Virtual Network Functions (VNFs). Addressing this issue, this paper proposes a novel deep neural network based flow correlation method, called DeepMetricCorr. DeepMetricCorr composes multidimensional flow statistical features, metric learning, and a channel attention mechanism to solve flow correlation problems accurately. It is featured with a lightweight design which reduces computational overhead. The experiments on real-world datasets demonstrate that DeepMetricCorr outperforms other state-of-the-art methods in correlation accuracy, especially on load balancers with an over 2× improvement. Furthermore, the model maintains a low latency (¡0.5 s) as the number of candidate flows increases over 8000.

A Systematic Literature Review of Reliable Provisioning for Virtual Network Function Chaining

The abstraction of the network node functions using virtualization methods introduced an innovative architecture called Network Function Virtualization (NFV). In NFV, every virtualization software hosts a network service recognized as a Virtual Network Function (VNF). In general, the network provider creates a Service Function Chain (SFC) for every sequence of multiple requested VNFs by the customers. Although NFV allows for a more flexible and economical approach, it is more prone to error and failure. Therefore, providing reliable provisioning for VNF chaining is one of the key issues in NFV. In this paper, we present a systematic literature review to study the pioneer research efforts that provide reliable provisioning for VNF chaining by guaranteeing the availability of the service and resource optimization. Our review is the result of the analysis of 21 screened papers. This paper presents the result of our analysis, including different aspects of a reliable provisioning algorithm, various adopted techniques for reliable provisioning, and the superiority and drawbacks of each algorithm based on the proposed criteria for the evaluation of the provisioning algorithms.

Truthful VNFI Procurement Mechanisms With Flexible Resource Provisioning in NFV Markets

With the rapid development of network function virtualization (NFV), more and more enterprises and operators are seeking network service provisioning via service chains of virtual network functions (VNFs), instead of depending on proprietary hardware appliances. Following this trend, an NFV market is emerging, where users can procure different VNF instances (VNFIs) and their combinations from multiple network service providers (NSPs) in a pay-as-you-go way. In such a procurement process, how to guarantee truthfulness while enabling flexible resource provisioning in form of VNFIs is a significant challenge. In this paper, we propose a truthful reverse combinatorial auction-based mechanism to solve the combinatorial VNFI procurement problem. To support flexible resource provisioning, this mechanism allows NSPs to be multi-minded, and determine the provisioning VNFIs according to the auction results. Specifically, we design a heuristic algorithm to determine the winning bids in polynomial time. Furthermore, we devise a critical-payment-based pricing algorithm to induce NSPs to disclose their real costs, aiming to achieve truthfulness. Rigorous theoretical analysis shows the proposed mechanism can guarantee truthfulness, individual rationality and computational efficiency. Simulation results also verify the effectiveness and efficiency of the proposed mechanism.

APVNFC: Adaptive Placement of Virtual Network Function Chains

Abstract Designing efficient and flexible approaches for placement of Virtual Network Function (VNF) chains is the main success of Network Function Virtualization (NFV). However, most current work considers the constant bandwidth and flow processing requirements while deploying the VNFs in the network. The constant (immutable) flow processing and bandwidth requirements become critical limitations in an NFV-enabled network with highly dynamic traffic flow. Therefore, bandwidth requirements and available resources of the Point-of-Presence (PoP) in the network change constantly. We present an adaptive model for placing VNF chains to overcome this limitation. At the same time, the proposed model minimizes the number of changes (i.e., re-allocation of VNFs) in the network. The experimental evaluation shows that the adaptive model can deliver stable network services. Moreover, it reduces the significant number of changes in the network and ensures flow performance.

VNF Orchestration and Power-Disjoint Traffic Flow Routing for Optimal Communication Robustness in Smart Grid With Cyber-Physical Interdependence

We explore the use of software-defined networking (SDN) technology in building a communication network for smart grid. With cyber-physical interdependence, such communication network may suffer from cross-network cascading failures. To prevent the failures, we perform virtual network function (VNF) orchestration jointly with power-disjoint routing. Our work is novel in proposing an efficient scheme to find power-disjoint routes at the same time of performing VNF orchestration. We formulate an optimization to maximize the ratio of power-disjoint route count to VNF orchestration cost. The optimization has a non-linear non-convex objective function. We propose a two-level hierarchical solution approach. At higher level, the scheme converts the problem into a fractional maximum flow circulation, which can be solved using simplex method to find the maximum number of power-disjoint routes. Given a higher level solution, the lower level aims to minimize the VNF orchestration cost while satisfying VNF chaining and placement requirements. This lower level hierarchy uses the Dijkstra’s algorithm in building a sequence of minimum spanning trees, each roots at the current VNF hosting node in a VNF chain. Extensive simulation results confirm that the proposed scheme can find the maximum number of power-disjoint routes and minimize the cost within a second, for a system with 120 communication nodes. The results show that the number of power-disjoint routes can be increased by increasing either the number of nodes or node degree, but only the node degree can keep the cost flat. Therefore, one should build a robust software-defined smart grid communication network by enhancing node connectivity.

FIPAM: Fuzzy Inference Based Placement and Migration Approach for NFV-Based IoTs

The advancement and spread of the Internet-of-Things (IoT) have massively been increased over a decade. With the widespread of IoT networks, it is becoming difficult to acquire and execute real-time data. Network function virtualization (NFV) provides a flexible and efficient solution for IoT-based applications and service management. NFV creates a virtualized environment that can run a large number of micro-services for different IoT applications by using the virtual network functions (VNFs) through placement and chaining. In this paper, we propose a novel fuzzy inference-based placement and migration (FIPAM) approach for placement and migration/chaining of VNFs to ensure that resource allocation is carefully carried out during VNF orchestration and embedding. Firstly, we formulate the VNF chaining and placement problem. Secondly, we propose a lightweight VNF placement solution that considers the underlying network conditions while making the placement decisions. A novel usage of fuzzy inference is proposed to optimize the chaining mechanism along with the dynamic instantiation of VNFs to meet specific service needs. Simulation results are shown to validate the superiority of the proposed algorithm over existing schemes.

Fair Virtual Network Function Mapping and Scheduling Using Proximal Policy Optimization

Network function virtualization redefines a network service as a softwarized chain of virtual network functions (VNFs), which decouples the specific network service from dedicated devices and greatly reduces the hardware cost. The VNFs are generally deployed on common off-the-shelf servers and statistically share computational resources. Due to the inherent integer constraints, the corresponding VNF mapping and scheduling issue is a highly challenging task. In this paper, we consider the mapping and scheduling of VNFs for a given network service, for which a flexible job shop scheduling problem is formulated to optimize the max-min fairness while ensuring the delay requirements of different service chains. Specifically, we propose a deep reinforcement learning method based on offline proximal policy optimization, which dynamically determines the mapping and scheduling decision based on the state of unfinished service chains. The proposed algorithm is scalable to the number of service chains and can be enhanced by Monte Carlo tree search. Numerical results show that the proposed algorithm outperforms the traditional random forest and the greedy algorithms in terms of both service fairness and acceptance ratio.

Truthful auction mechanisms for VNF chain provisioning and allocation across geo-distributed datacenters

With the fast development of network function virtualization (NFV), NFV markets are emerging that allows network service providers (NSPs) to trade different virtual network function (VNF) chains among users. Therefore, efficient mechanisms for VNF chain provisioning and allocation are key to guarantee efficient operations of the NFV markets. One fundamental issue is how to maximize the social welfare of the market such that selfish users have incentives to acquire VNF chains from the markets. To overcome this issue, in this paper, we propose a truthful randomized combinatorial auction mechanism (TRCAM), which integrates the primal–dual approximation algorithm design technique and efficient pricing strategy. This mechanism considers dynamic VNF chain provisioning of the NSP and flexible VNF chain deployment of users. To be specific, we first formulate the social welfare maximization problem as an integer linear program, which is proven to be NP-hard. Then, we propose a primal–dual approximate algorithm which is near-optimal with polynomial-time complexity and employ it as a building block to design the TRCAM. Furthermore, we develop a novel binary-search-based algorithm to boost the average-case performance of the TRCAM. Both strict theoretical analysis and extensive experimental studies demonstrate the effectiveness of our proposed TRCAM, which achieves approximate truthfulness, near-optimal social welfare, individual rationality, and computational efficiency.

A Reinforcement Learning Approach for Virtual Network Function Chaining and Sharing in Softwarized Networks

Cognizant of the ease with which softwarized functions can be dynamically scaled according to real time resource requirements, and the fact that multiple services can have common VNFs in their chaining, this paper tackles the problem of cost effective deployment of online services from the perspective of sharing their VNF instances. First, we formally formulate the deployment problem under VNFs sharing. Secondly, given the NP-hard nature of the above problem, we propose a reinforcement learning (RL) algorithm capable of making intelligent placement decisions while considering multiple conflicting costs. Costs of transmission, VNF instantiation or energy consumption, among others. Thanks to the intelligence of the RL algorithm, simulation results show that the performance of the proposed algorithm is within a 14% margin and similar to an optimal solution in terms of request provisioning cost and acceptance ratio, respectively. Moreover, the algorithm results in more than a 20% and a 70% improvement in terms of request deployment cost and time compared to a state-of-the-art algorithm, and up to more than a 40% improvement in terms of cost compared to an algorithm that greedily minimizes the transmission or VNF activation costs.

A sub-action aided deep reinforcement learning framework for latency-sensitive network slicing

Network slicing is a core technique of fifth-generation (5G) systems and beyond. To maximize the number of accepted network slices with limited hardware resources, service providers must avoid over-provisioning of quality-of-service (QoS), which could prevent them from lowering capital expenditures (CAPEX)/operating expenses (OPEX) for 5G infrastructure. In this paper, we propose a sub-action aided double deep Q-network (SADDQN)-based network slicing algorithm for latency-aware services. Specifically, we model network slicing as a Markov decision process (MDP), where we consider virtual network function (VNF) placements to be the actions of the MDP, and define a reward function based on cost and service priority. Furthermore, we adopt the Dijkstra algorithm to determine the forwarding graph (FG) embedding for a given VNF placement and design a resource allocation algorithm – binary search assisted gradient descent (BSAGD) – to allocate resources to VNFs given the VNF-FG placement. For every service request, we first use the DDQN to choose an MDP action to determine the VNF placement (main action). Next, we employ the Dijkstra algorithm (first-phase sub-action) to find the shortest path for each pair of adjacent VNFs in the given VNF chain. Finally, we implement the BSAGD (second-phase sub-action) to realize this service with the minimum cost. The joint action results in an MDP reward that can be utilized to train the DDQN. Numerical evaluations show that, compared to state-of-the-art algorithms, the proposed algorithm can improve the cost-efficiency while giving priority to higher-priority services and maximizing the acceptance ratio.

Dynamic UPF placement and chaining reconfiguration in 5G networks

Network function virtualization (NFV) and multi-access edge computing (MEC) have become two crucial pillars in developing 5G and beyond networks. NFV promises cost-saving and fast revenue generation through dynamic instantiation and the scaling of virtual network functions (VNFs) according to time-varying service demands. Additionally, MEC provides considerable reductions in network response time and backhaul traffic since network functions and server applications can be deployed close to users. Nevertheless, the placement and chaining of VNFs at the network edge is challenging due to numerous aspects and attendant trade-offs. This paper addresses the problem of dynamic user plane function placement and chaining reconfiguration (UPCR) in a MEC environment to cope with user mobility while guaranteeing cost reductions and acceptable quality of service (QoS). The problem is formalized as a multi-objective integer linear programming model to minimize multiple cost components involved in the UPCR procedure. We propose a heuristic algorithm called dynamic priority and cautious UPCR (DPC-UPCR) to reduce the solution time complexity. Additionally, we devise a scheduler mechanism based on optimal stopping theory to determine the best reconfiguration time according to instantaneous values of latency violations and a pre-established QoS threshold. Our detailed simulation results evidence the efficiency of the proposed approaches. Specifically, the DPC-UPCR provides near-optimal solutions, within 15% of the optimum in the worst case, in significantly shorter times than the mathematical model. Moreover, the proposed scheduling method outperforms two scheduler baseline solutions regarding the number of reconfiguration events and QoS levels.

A joint orchestration of security and functionality services at network edge

Edge computing emerges as a new paradigm to provide low-latency network services in close proximity to end users. Based on the network function virtualization (NFV) technology, network services can be flexibly and scalably provisioned as virtual network function (VNF) chains deployed at edge servers. With such advantages, both the industry and research communities have done extensive studies on deploying VNF chains at network edge. The existing works mainly take an ideal assumption that the network is totally safe and there are no malicious users. Therefore, they leverage all available resources to serve their users. However, such an assumption is impractical in real networks. Security services, such as firewall, deep packet detection, intrusion detection, are always required for production networks. The existing service deployment methods fail to consider the co-existence of security services and functionality services. In this paper, we present the topic of joint deployment of both security and functionality services, wherein the security services are responsible to check the data flows before being processed by the functionality services. To solve this problem, we propose the Secure Deployment Pattern, which aims to simultaneously satisfy the security protection and QoS requirements at network edge. It divides the services into two kinds, i.e., the user-oriented functionality services, and the service provider-oriented security services. In this case, it is very challenging to jointly deploy the security services and functional services with respect to the resource and latency constraints. We formulate this problem as an integer programming model, and propose the heuristic algorithms to solve it. As far as we know, this paper is the first step, which targets at a proper orchestration of security and functionality services in edge computing. Extensive evaluations show that the proposed algorithms are effective and efficient, in terms of the execution time and the average number of served requests.

Mobile Fog Computing by Using SDN/NFV on 5G Edge Nodes

Fog computing provides quality of service for cloud infrastructure. As the data computation intensifies, edge computing becomes difficult. Therefore, mobile fog computing is used for reducing traffic and the time for data computation in the network. In previous studies, software-defined networking (SDN) and network functions virtualization (NFV) were used separately in edge computing. Current industrial and academic research is tackling to integrate SDN and NFV in different environments to address the challenges in performance, reliability, and scalability. SDN/NFV is still in development. The traditional Internet of things (IoT) data analysis system is only based on a linear and time-variant system that needs an IoT data system with a high-precision model. This paper proposes a combined architecture of SDN and NFV on an edge node server for IoT devices to reduce the computational complexity in cloud-based fog computing. SDN provides a generalization structure of the forwarding plane, which is separated from the control plane. Meanwhile, NFV concentrates on virtualization by combining the forwarding model with virtual network functions (VNFs) as a single or chain of VNFs, which leads to interoperability and consistency. The orchestrator layer in the proposed software-defined NFV is responsible for handling real-time tasks by using an edge node server through the SDN controller via four actions: task creation, modification, operation, and completion. Our proposed architecture is simulated on the EstiNet simulator, and total time delay, reliability, and satisfaction are used as evaluation parameters. The simulation results are compared with the results of existing architectures, such as software-defined unified virtual monitoring function and ASTP, to analyze the performance of the proposed architecture. The analysis results indicate that our proposed architecture achieves better performance in terms of total time delay (1800 s for 200 IoT devices), reliability (90%), and satisfaction (90%).