Sequence Of Virtual Network Functions Research Articles

Distributed Service Function Chaining in NFV-Enabled Networks: A Game-Theoretic Learning Approach

In network function virtualization (NFV), Service Function Chaining (SFC) provides an ordered sequence of virtual network functions (VNFs) and subsequent steering of traffic flows through them to cater to end-to-end services. This paper addresses the NP-hard problem of minimum cost SFC deployment to support customer services that access the carrier network’s NFV infrastructure (NFVI) through some edge routers. To determine the mappings of VNFs to physical servers, a challenging aspect would be the inter-server latencies that may fluctuate over time because of the sharing nature of cloud data centers. To construct the SFC, we come up with three different formulations, each corresponding to a different informational assumption about the link latencies: First, a centralized integer linear programming (ILP) formulation is given under the assumption of the non-causal availability of exact and instantaneous inter-server latencies. The solution to this ILP can serve as a lower bound to benchmark more scalable and realistic schemes. Next, we give a distributed game-theoretic formulation (with service broker agents as players) which only requires the statistical knowledge of link latency fluctuations. The game provably admits a pure Nash equilibrium (NE) and can be solved iteratively through the well-known best response dynamics (BRD) algorithm. Our main novelty lies in the third formulation in which each service broker has neither instantaneous nor statistical knowledge of the latencies. Instead, it relies on a game-theoretic learning algorithm to compose its VNF chain only based on its own history of adopted decisions and experienced delays on each logical link. We prove that the proposed learning algorithm asymptotically converges to NE and evaluate its performance through simulations in terms of convergence and the impact of network parameters.

A Lightweight SFC Embedding Framework in SDN/NFV-Enabled Wireless Network Based on Reinforcement Learning

Network function virtualization (NFV) is considered to be a promising paradigm for reducing network infrastructure cost. However, the mapping from network physical resources to service function chain (SFC), which is an ordered sequence of virtual network functions, is challenging, especially when a dynamic wireless network is involved. In this article, we propose a novel lightweight SFC embedding framework based on reinforcement learning in the NFV-enabled wireless network for reducing the end-to-end wired and wireless delay and leveraging the SFC acceptance ratio. We model the embedding problem as a Markov decision process, in which the reward is modeled as an adjustable weighted function, including combined delay (wired and wireless delay) and remaining resource. Accordingly, a lightweight and adjustable <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -learning-based SFC embedding strategy is proposed. To validate the effectiveness of our proposed framework, extensive simulations are performed by using real data traces and compared with a classical genetic algorithm (GA) and a cluster-based method MAPLE. The results of the simulation show that the proposed framework is efficient and performs better than both GA and MAPLE in terms of delay and service acceptance probability. The prototype of the framework is also built for demonstrating the feasibility of the proposed framework.

Online Reliability-Enhanced Virtual Network Services Provisioning in Fault-Prone Mobile Edge Cloud

Fault-Prone Mobile Edge Cloud (FP-MEC) is a new type of distributed network composed of mobile edge computing and network function virtualization, where virtual network services can be provided in the form of service function chains (SFCs) that are a sequence of virtual network functions (VNFs) on-demand deployed on resource-limited edge servers. FP-MEC has a characteristic that the fault probability of each VNF is dynamic and fluctuates with time and workloads, making SFCs temporarily unreliable. To increase the reliabilities, redundant Backup VNFs (BVNFs) need to be deployed near the VNFs and activated when they experience faults. Different mobile users would request different SFCs with reliability and service time demands to process their data. However, workloads of VNFs are dynamic and unpredictable in FP-MEC due to random arrival of user requests. How to optimally deploy VNFs and corresponding BVNFs on a set of edge servers to form expected SFCs that have higher reliabilities than user demand values, meanwhile throughput of receiving requests is maximized while receiving cost is minimized in real-time, is a challenging problem. The receiving cost is composed of deployment cost of instantiating VNFs and BVNFs, and communication cost of routing data among users, VNFs and BVNFs. In this paper, the long-term provisioning problem is first formulated as an integer linear program and proved to be NP-hard. Then, it is discretized into a sequence of one-slot optimization problems to handle practical time-varying fault probability, where a set of SFC requests are given at each time slot, and receiving or rejecting decisions are executed immediately without any future information. Finally, an online approximation scheme with a constant approximation ratio is proposed to solve the one-slot problems in polynomial time. Theoretical analyses and experiments based on real network topology of CERNET in China demonstrate that the scheme is promising compared to existing works.

A reinforcement learning-based approach for availability-aware service function chain placement in large-scale networks

By decoupling network service functions from custom-built hardware, network function virtualization is transforming network service provisioning, making large scale telecommunications more flexible, scalable, and agile. Service function chaining often requires network traffic to flow through a specific sequence of virtual network functions (VNFs) to comply with network policies. The dependencies between VNFs in such a chain can significantly and negatively impact the availability of an end-to-end service function chain (SFC) when one or more VNFs malfunction or fail. While redundancy strategies can be used to reduce the impact of such failure, this can increase operational expenditure and energy consumption. In this paper, we propose a solution for SFC placement based on reinforcement learning (RL) taking into account SFC availability, operational costs, and energy consumption. The Cand-RL algorithm uses RL based on proximal policy optimization (PPO) to select the suitable candidate node and define the redundancy strategy to meet availability requirements. We compare Cand-RL against two greedy algorithms in a variety of simulated scenarios. The results show that the Cand-RL outperforms the greedy algorithms, achieving a higher acceptance rate and a good balance between availability, placement cost, and energy consumption.

Delay-Sensitive and Availability-Aware Virtual Network Function Scheduling for NFV

Network Function Virtualization (NFV) has been emerging as an appealing solution that transforms from dedicated hardware implementations to software instances running in a virtualized environment. In NFV, the requested service is implemented by a sequence of Virtual Network Functions (VNF) that can run on generic servers by leveraging the virtualization technology. These VNFs are pitched with a predefined order, and it is also known as the Service Function Chaining (SFC). Considering that the delay and resiliency are two important Service Level Agreements (SLA) in a NFV service, in this paper, we first investigate how to quantitatively model the traversing delay of a flow in both totally ordered and partially ordered SFCs. Subsequently, we study how to calculate the VNF placement availability mathematically for both unprotected and protected SFCs. After that, we study the delay-sensitive Virtual Network Function placement and routing problem with and without resiliency concerns. We prove that this problem is NP-hard under two cases. We subsequently propose an exact Integer Nonlinear Programming (INLP) formulation and an efficient heuristic for this problem in each case. Finally, we evaluate the proposed algorithms in terms of acceptance ratio, average number of used nodes and total running time via extensive simulations.

Near-Optimal Energy-Efficient Algorithm for Virtual Network Function Placement

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.

Age of Information Aware VNF Scheduling in Industrial IoT Using Deep Reinforcement Learning

In delay-sensitive industrial Internet of Things (IIoT) applications, the age of information (AoI) is employed to characterize the freshness of information. Meanwhile, the emerging network function virtualization provides flexibility and agility for service providers to deliver a given network service using a sequence of virtual network functions (VNFs). However, suitable VNF placement and scheduling in these schemes is NP-hard and finding a globally optimal solution by traditional approaches is complex. Recently, deep reinforcement learning (DRL) has appeared as a viable way to solve such problems. In this paper, we first utilize single agent low-complex compound action actor-critic RL to cover both discrete and continuous actions and jointly minimize VNF cost and AoI in terms of network resources under end-to-end Quality of Service constraints. To surmount the single-agent capacity limitation for learning, we then extend our solution to a multi-agent DRL scheme in which agents collaborate with each other. Simulation results demonstrate that single-agent schemes significantly outperform the greedy algorithm in terms of average network cost and AoI. Moreover, multi-agent solution decreases the average cost by dividing the tasks between the agents. However, it needs more iterations to be learned due to the requirement on the agents' collaboration.

Resource-Ability Assisted Service Function Chain Embedding and Scheduling for 6G Networks With Virtualization

While 5G is being deployed all around the world, the industry and academia start the investigation of 6G. Network function virtualization (NFV) is seen as the key enabler towards the flexible resource management and sharing of 6G networks. The main idea of NFV is to decouple the physical devices from the specific functions that run on them. In NFV, virtual network service (NS) is modeled as a service function chain (SFC). The challenges in SFC description, composition, embedding, and scheduling are key issues in NFV. In this paper, we focus on the joint SFC embedding and scheduling for NFV in 6G networks. With the assistance of sensing the physical node resource abilities, we propose a novel SFC embedding and scheduling algorithm, which is named as RA-SFC-6G . A new service is first composed as an SFC which is an ordered sequence of virtual network functions (VNFs). Afterwards, our RA-SFC-6G algorithm will select the physical nodes with stronger resource abilities and abundant resources to accommodate the SFC. In addition, the NS is guaranteed to be implemented without violating the NS maximum allowed scheduling time. If violated, our RA-SFC-6G can re-embed and re-schedule the violated VNFs to suitable positions. In order to validate our RA-SFC-6G performance, we do the experiment work. Experiment results show that our RA-SFC-6G achieves at least 10 $\%$ higher SFC acceptance ratio than the previous counterparts and the typical heuristics.

Recent Advances of Resource Allocation in Network Function Virtualization

Network Function Virtualization (NFV) has been emerging as an appealing solution that transforms complex network functions from dedicated hardware implementations to software instances running in a virtualized environment. Due to the numerous advantages such as flexibility, efficiency, scalability, short deployment cycles, and service upgrade, NFV has been widely recognized as the next-generation network service provisioning paradigm. In NFV, the requested service is implemented by a sequence of Virtual Network Functions (VNF) that can run on generic servers by leveraging the virtualization technology. These VNFs are pitched with a predefined order through which data flows traverse, and it is also known as the Service Function Chaining (SFC). In this article, we provide an overview of recent advances of resource allocation in NFV. We generalize and analyze four representative resource allocation problems, namely, (1) the VNF Placement and Traffic Routing problem, (2) VNF Placement problem, (3) Traffic Routing problem in NFV, and (4) the VNF Redeployment and Consolidation problem. After that, we study the delay calculation models and VNF protection (availability) models in NFV resource allocation, which are two important Quality of Service (QoS) parameters. Subsequently, we classify and summarize the representative work for solving the generalized problems by considering various QoS parameters (e.g., cost, delay, reliability, and energy) and different scenarios (e.g., edge cloud, online provisioning, and distributed provisioning). Finally, we conclude our article with a short discussion on the state-of-the-art and emerging topics in the related fields, and highlight areas where we expect high potential for future research.

Delay-Aware Virtual Network Function Placement and Routing in Edge Clouds

Mobile Edge Computing (MEC) offers a way to shorten the cloud servicing delay by building the small-scale cloud infrastructures at the network edge, which are in close proximity to the end users. Moreover, Network Function Virtualization (NFV) has been an emerging technology that transforms from traditional dedicated hardware implementations to software instances running in a virtualized environment. In NFV, the requested service is implemented by a sequence of Virtual Network Functions (VNF) that can run on generic servers by leveraging the virtualization technology. Service Function Chaining (SFC) is defined as a chain-ordered set of placed VNFs that handles the traffic of the delivery and control of a specific application. NFV therefore allows to allocate network resources in a more scalable and elastic manner, offer a more efficient and agile management and operation mechanism for network functions and hence can largely reduce the overall costs in MEC. In this paper, we study the problem of how to place VNFs on edge and public clouds and route the traffic among adjacent VNF pairs, such that the maximum link load ratio is minimized and each user's requested delay is satisfied. We consider this problem for both totally ordered SFCs and partially ordered SFCs. We prove that this problem is NP-hard, even for the special case when only one VNF is requested. We subsequently propose an efficient randomized rounding approximation algorithm to solve this problem. Extensive simulation results show that the proposed approximation algorithm can achieve close-to-optimal performance in terms of acceptance ratio and maximum link load ratio.

Embedding Multicast Service Function Chains in NFV-Enabled Networks

In Network Function Virtualization (NFV), a multicast data flow must be traversed through a Service Function Chain (SFC) that is a predefined sequence of Virtual Network Functions (VNFs). In this letter, we formulate the problem of optimal embedding of NFV-Enabled multicast service chains as a mixed binary linear program. Then to solve the problem in a reasonable time, we use the penalty-based successive upper bound minimization algorithm. Also, a heuristic solution based on the decomposition of the problem into smaller sub-problems that can be solved iteratively and sequentially, is proposed. Simulation results demonstrate the effectiveness of the proposed solution methods.

Capacitated Shortest Path Tour Problem-Based Integer Linear Programming for Service Chaining and Function Placement in NFV Networks

Network functions virtualization (NFV) is a new paradigm to achieve flexible and agile network services by decoupling network functions from proprietary hardware and running them on generic hardware as virtual network functions (VNFs). In the NFV network, a network service can be modeled as a sequence of VNFs, called a service chain. Given a connection request (e.g., origin, destination, and a sequence of required functions), we have to solve both the service chaining and function placement problems to find an appropriate service path that optimizes the objective (e.g., minimization of the total path delay) while satisfying the service chain requirements. In this article, focusing on the similarity between the service chaining problem and the shortest path tour problem (SPTP) and developing the novel network model called augmented network, we formulate capacitated SPTP-based integer linear programs (ILPs) for the service chaining and function placement. Through numerical results obtained by the existing solver, we show the proposed ILP for the service chaining can support 1.22–1.90 times as large-scale systems as the existing ILP. Furthermore, we also demonstrate that the proposed ILP for both the service chaining and function placement can shorten the total delay by 15.8% compared with that only for the service chaining. For further scalability, we propose a shortest-path-based heuristic algorithm to solve the ILPs and show the heuristic for service chaining and function placement can calculate the optimal solution with high accuracy in strongly polynomial time.

Traffic routing in stochastic network function virtualization networks

Network Function Virtualization (NFV) is emerging as an attractive solution, which can transform complex network functions from the dedicated hardware implementations into software instances running in a virtualized environment. In NFV, the requested service is implemented by a sequence of Virtual Network Functions (VNF) that can run on generic servers by leveraging the virtualization technology. These VNFs are placed in a predefined order, which is also known as the Service Function Chaining (SFC). While most of the existing work on traffic routing in NFV networks assume deterministic link delay and bandwidth, the real-life network usually behaves in a stochastic manner, due to e.g., inaccurate data, expired exchanged information, insufficient estimation to the network. Motivated by this, we consider the stochastic NFV networks, where the link bandwidth and delay are assumed to be random variables and their cumulative distribution functions are known. We first study how to calculate the delay and bandwidth value in an SFC such that their realizing probabilities are satisfied. Subsequently, we formally define the traffic routing problem in stochastic NFV networks and prove it is NP-hard. We present an exact solution and a tunable heuristic to solve this problem. The proposed heuristic is a sampling-based algorithm, and it leverages the Tunable Accuracy Multiple Constraints Routing Algorithm (TAMCRA) to find a multi-constraint path for each adjacent VNF pair. It dynamically adjusts the link weights as well as delay and bandwidth realizing probability constraint after finding the path for each VNF pair so that the cumulated probabilities will not violate the specified values. Finally, we evaluate the performance of the proposed algorithms via extensive simulations.

Joint Optimization of Delay Guarantees and Resource Allocation for Service Function Chaining

A Service Function Chain (SFC) is an ordered sequence of virtual network functions (VNFs) to provide network service. Most existing SFC orchestration schemes, however, cannot optimize the resources allocation while guaranteeing the service delay constraint. To fulfill this goal, we propose a Layered Graph based SFC Orchestration Scheme (LGOS). LGOS converts both the cost of resource and the related delay into the link weights in the layered graph, which helps abstract the SFC orchestration problem as a shortest path problem. Then a simulated annealing based batch processing algorithm is designed for SFC requests set. Through extensive evaluations, we demonstrated that our scheme can reduce the end-to-end delay and the operational expenditure by 21.6% and 13.7% at least, and the acceptance ratio of requests set can be improved by 22.3%, compared with other algorithms.

Service chain placement in SDNs

We study the allocation problem of a compound request for a service chain in a software defined network that supports network function virtualization. Given a network that contains servers with limited processing power and links with limited bandwidth, a service chain is a sequence of virtual network functions (VNFs) that serve a certain flow request in the network. The allocation of a service chain consists of routing and VNF placement, namely each VNF from the sequence is placed in a server along a path. It is feasible if each server can handle the VNFs that are assigned to it, and if each link on the path can carry the flow that is assigned to it. A request for service is composed of a source and a destination in the network, an upper bound on the total latency, and a specification in the form of a directed acyclic graph (DAG) of VNFs that provides all service chains that are considered valid for this request. In addition, each pair of server and VNF is associated with a cost for placing the VNF in the server. Given a request, the goal is to find a valid service chain of minimum total cost that respects the latency constraint or to identify that such a service chain does not exist.We show that even the feasibility problem is NP-hard in general graphs. Hence we focus on DAGs. We show that the problem is still NP-hard in DAGs even for a very simple network, and even if the VNF specification consists of only one option (i.e., the virtual DAG is a path). On the other hand, we present an FPTAS for the case where the network is a DAG. In addition, based on our FPTAS, we provide algorithms for general instances in which the service chain passes through a bounded number of vertices whose degree is larger than two. Finally, we provide an FPTAS for a fault-tolerant version of the problem.

Service Chain Embedding for Diversified 5G Slices With Virtual Network Function Sharing

We address the problem of embedding service chains consisting of a sequence of virtual network functions (VNF) for 5G slices, considering diversified slice requirements. We develop a fine-grained approach that considers resource requirements and limited traffic processing capacity of VNFs, which can be shared (or not) among slices depending on VNF functionalities. We develop an optimization formulation that minimizes the resources while satisfying the constraints in both the physical infrastructure and logical slices. We also develop a heuristic algorithm based on a goodness function to overcome the problem complexity. The numerical results show that our approach uses 30% less bandwidth and 45% fewer VNF instances, compared with a baseline.

Multiple instances mapping of Service Function Chain with parallel Virtual Network Functions

Network Function Virtualization addresses the defect of traditional middleboxes and enables operators to implement new services through a process named Service Function Chain mapping. Service Function Chain is composed by a sequence of Virtual Network Functions (VNFs) which is deployed in shared platforms. Service Function Chain with parallel VNFs is proposed to reduce the delivery latency. In this paper, a multiple instances mapping scheme named MIM is proposed to resolve the performance bottleneck introduced by the imbalance of parallel VNFs. A integer programing model is established to describe the multiple instances mapping problem based on queuing theory, and a double layer Genetic Algorithm is used to allocate parallel VNFs with multiple instances. Simulation results show that the multiple instances mapping scheme can improve the performance of Service Function Chain with parallel VNFs effectively.

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.