Service Function Chaining Research Articles

Reliability-assured service function chain migration strategy in edge networks using deep reinforcement learning

With the widespread adoption of edge computing and the rollout of 5G technology, the edge network is experiencing rapid growth. Edge computing enables the execution of certain computational tasks on edge devices, fostering more efficient resource utilization. However, the reliability of the edge network is constrained by its network connections. Network instability can significantly compromise service quality. An effective service function chain (SFC) migration algorithm is essential to optimize resource utilization, enhance service quality. This paper begins by analyzing the current research landscape of edge networks and SFC migration algorithms. Subsequently, the challenges associated with edge network and SFC migration are formally articulated, leading to the proposal of a SFC migration algorithm based on deep reinforcement learning (DRL) with a focus on reliability assurance (RA-SFCM). The algorithm leverages multi-agent deep reinforcement learning to dynamically perceive changes in the edge network environment. It introduces an advantage function to evaluate the performance of each agent relative to the average level and incorporates a central attention mechanism with multiple attention heads to better capture the interdependencies and relationships among different agents. Additionally, this paper innovatively defines and quantifies the reliability of the migration process. By introducing a reliability penalty mechanism based on the migration target nodes and link capacity, it enhances the reliability of the migration schemes. The experimental results conclusively demonstrate the remarkable advantages of the RA-SFCM algorithm in terms of real-time performance, resource utilization efficiency, and reliability. Compared to algorithms such as Sa-VNFM, ROVM, and DLTSAC, RA-SFCM exhibits superior performance. For RA-SFCM, the optimized deployment migration strategy enhances real-time performance, precise resource management improves utilization efficiency, and advanced fault tolerance mechanisms strengthen reliability.

Enhancing SFC Placement with Parallelized Functions in MEC Using Deep Reinforcement Learning

This study presents an innovative architecture leveraging Deep Reinforcement Learning (DRL) to optimize online service delivery in Mobile Edge Computing (MEC) networks. Our proposed method, the DRL-based dynamic Service Function Chain (SFC) placement with parallelized Virtual Network Functions (VNFs), aims to maximize Long-Term Cumulative Reward (LTCR). By parallelizing VNFs, our approach achieves computational acceleration, enhancing the efficiency of online service provisioning. Furthermore, we enhance future request processing capabilities by extracting the distribution of initialized VNFs. Through comprehensive simulations, our proposed architecture demonstrates a notable improvement, with approximately a 9.5% increase in the average number of accepted requests.

Correction to “Dynamic service function chain placement in mobile computing: An asynchronous advantage actor‐critic based approach”

Correction to “Dynamic service function chain placement in mobile computing: An asynchronous advantage actor‐critic based approach”

Scalable Solution for Service Function Chaining Embedding in Hybrid Optical-Electronic Networks

Scalable Solution for Service Function Chaining Embedding in Hybrid Optical-Electronic Networks

Reinforcement-Learning-Assisted Service Function Chain Embedding Algorithm in Edge Computing Networks

Edge computing networks are critical infrastructures for processing massive data and providing instantaneous services. However, how to efficiently allocate resources in edge computing networks to meet the embedding requirements of service function chains has become an urgent problem. In this paper, we model the resource allocation problem in edge computing networks as a service function chain embedding problem model, aiming to optimize the resource allocation through reinforcement learning algorithms to achieve the goals of low cost, high revenue, and high embedding rate. In this paper, the basic concepts of edge computing network and service function chain are elaborated, and the resource allocation problem is transformed into a service function chain embedding problem by establishing a mathematical model, which provides a foundation for the subsequent algorithm design. In this paper, a service function chain embedding algorithm based on reinforcement learning is designed to gradually optimize the resource allocation decision by simulating the learning process. In order to verify the effectiveness of the algorithm, a series of simulation experiments are conducted in this paper and compared with other algorithms. The experimental results show that the service function chain embedding algorithm based on reinforcement learning proposed in this paper exhibits superior performance in resource allocation. Compared with traditional resource allocation methods, the algorithm achieves significant improvement in terms of low cost, high revenue, and high embedding rate.

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.

Efficient SFC Protection Method against Network Attack Risks in Air Traffic Information Networks

With the continuous development of the civil aviation industry toward digitalization and intelligence, the closed architecture of traditional air traffic information networks struggles to meet the rapidly growing demands for air traffic services. Network function virtualization (NFV) is one of the key technologies that can address the rigidity of traditional air traffic information networks. NFV technology has facilitated the flexible deployment of air traffic services, but it has also expanded the attack surface of the network. In addressing the network attack risks faced by service function chains (SFCs) in NFV environments, a SFC protection method based on honeypots and backup technology (PBHB) is proposed to reduce the resource cost of protecting air traffic information networks while enhancing network security. Initially, PBHB utilizes the TAPD algorithm to deploy the primary VNFs as closely as possible to the shortest path between the source and destination endpoints, thus aiming to reduce SFC latency and save bandwidth resource costs. Subsequently, the RAHDR algorithm is employed to install honeypot VNFs in each physical platform that is at risk of side-channel attacks, thus updating the deployment status of honeypot VNFs in real time based on the VNF lifecycle in order to offer primary protection for SFCs. Lastly, the BDMPE algorithm was used to calculate the backup scheme with the highest protection efficiency to implement secondary protection for the SFCs that still do not meet the security requirements. Through experiments, the maximum backup limit for SFCs in PBHB was determined, confirming its satisfactory performance across various SFC arrival rates. Furthermore, performance comparisons with other SFC protection methods revealed that PBHB achieves optimizations in resources cost while ensuring SFC security and latency.

Accelerator: an intent-based intelligent resource-slicing scheme for SFC-based 6G application execution over SDN- and NFV-empowered zero-touch network

Zero-touch networks (ZTNs) can provide autonomous network solutions by integrating software-based solutions for various emerging 5G and 6G applications. The current literature does not provide any suitable end-to-end network management and resource-slicing solutions for service function chaining (SFC) and user intent–based (time and cost preference) 6G/non-6G application execution over ZTNs enabled by mobile edge computing, network function virtualization, and software-defined networking. To tackle these challenges, this work initiates an end-to-end network management and user intent–aware intelligent network resource–slicing scheme for SFC-based 6G/non-6G application execution over ZTNs, taking into account various virtual and physical resources, task workloads, service requirements, and task numbers. The results depicted that at least 25.27% average task implementation delay gain, 6.15% energy gain, and 11.52% service monetary gain are realized in the proposed scheme over the compared schemes.

DRL approach for online user-centric QoS-Aware SFC embedding with dynamic VNF placement

In this paper, we tackle online Service Function Chaining (SFC) embedding issue in Software Defined Network (SDN)/Network Function Virtualization (NFV) enabled networks using Deep Reinforcement Learning (DRL) approach. This approach investigates the problem of Quality of Experience (QoE)/Quality of Service (QoS) driven SFC embedding with dynamic Virtual Network Functions (VNF) placement. We particularly focus on video streaming traffic requiring a given Mean Opinion Score (MOS) quantified in terms of QoE. In this regard, we propose a DRL algorithm based on Deep-Q-Network (DQN) (named DQN_QoE/QoS_SFC algorithm) implementing an optimization problem maximizing QoE while meeting QoS requirements. Our algorithm named DQN_QoE/QoS_SFC improves previous work given in Chen et al. (2018) dealing with the same issue through bringing performance evaluation of the training phase by testing the learning quality. This is achieved through using a QoE Threshold Score (QoETh−Sc) to be attained on average by the DQN_QoE/QoS_SFC agent along the last 100 runs of the training phase while maximizing the expected cumulative reward. We conduct simulation experiments through two performance metrics related to the SFC request namely QoE (QoE) and Rejection Ratio (RR). We investigate also how these metrics evolve for the proposed algorithm when compared with two standard baseline algorithms (Violent search, Random search) during learning phase to achieve satisfactory training quality. We highlight how DQN_QoE/QoS_SFC algorithm behaves along the training process and how it attempts reaching a near-optimal solution as closer as possible to the optimal solution provided by Violent search algorithm.

Handling Efficient VNF Placement with Graph-Based Reinforcement Learning for SFC Fault Tolerance

Network functions virtualization (NFV) has become the platform for decomposing the sequence of virtual network functions (VNFs), which can be grouped as a forwarding graph of service function chaining (SFC) to serve multi-service slice requirements. NFV-enabled SFC consists of several challenges in reaching the reliability and efficiency of key performance indicators (KPIs) in management and orchestration (MANO) decision-making control. The problem of SFC fault tolerance is one of the most critical challenges for provisioning service requests, and it needs resource availability. In this article, we proposed graph neural network (GNN)-based deep reinforcement learning (DRL) to enhance SFC fault tolerance (GRL-SFT), which targets the chain graph representation, long-term approximation, and self-organizing service orchestration for future massive Internet of Everything applications. We formulate the problem as the Markov decision process (MDP). DRL seeks to maximize the cumulative rewards by maximizing the service request acceptance ratios and minimizing the average completion delays. The proposed model solves the VNF management problem in a short time and configures the node allocation reliably for real-time restoration. Our simulation result demonstrates the effectiveness of the proposed scheme and indicates better performance in terms of total rewards, delays, acceptances, failures, and restoration ratios in different network topologies compared to reference schemes.

Constrained routing in multi-partite graph to solve VNF placement and chaining problem

Network Functions Virtualization (NFV) and Software-Defined Networks (SDN) empower IT professionals and service providers to strategically deploy Virtual Network Functions (VNFs), resulting in enhanced services and security while minimizing costs. Network services are dynamically provided through the deployment of Service Function Chains (SFCs), which involve selecting and interconnecting physical or virtual servers hosting VNFs.In this paper, we address the online VNF placement and chaining (VNF-PC) problem, aiming to optimize the overall cost while satisfying the resource constraints. We begin by proving the NP-completeness of finding feasible solutions for both fixed-size NFV infrastructures and constant-size SFCs. Subsequently, we formulate the VNF-PC problem using Integer Linear Programming (ILP), which is adept at handling and solving small-scale instances of the problem. However, the efficiency of ILP hinges on the quality and quantity of precomputed paths, a factor that escalates rapidly with the expansion of NFV infrastructure size. To tackle scalability, we initially suggest resource homogenization using a novel multi-partite graph, followed by the introduction of the Constrained Shortest Path-based Heuristic in multi-Partite Graph (CSPH-PG) to solve the VNF-PC problem in polynomial time. Our heuristic focuses on promising solution areas by restricting the exploration to the K shortest paths that meet the constraints at each node, with K acting as a fixed parameter of our heuristic.Extensive simulations demonstrate that our heuristic rapidly converges towards optimal solutions, even with small values of the parameter K.

Post-fault restoration of service function chains in fog networks

The network function virtualization (NFV) paradigm is rapidly being adopted by enterprises and service providers as an alternative to proprietary hardware. It enables virtual network functions (VNFs) to be embedded on commercial edge servers for the delivery of highly-specialized service function chains (SFCs). Here embedding SFC requests on fog computing supports clients with stringent delay requirements and contextual service requirements. Now one of the major challenges is the survivability of embedded service chains in fog computing, which may be compromised due to node, link, and application failures. These failures can disrupt service continuity and degrade the quality-of-service. Therefore, there is a demand for SFC provisioning to be more resilient, incorporating rapid restoration techniques that oppose proactive solutions that reserve backup paths, subsequently compromising network capacity. Hence, this paper presents heuristic methods for post-fault SFC restorations due to multiple failures in fog computing. These methods establish post-provisioning restoration for failed requests, known as end-to-end (E2E) and intermediate (IM) restoration, that aim to restore paths with the least delay and cost.

Mobility-aware SFC migration in dynamic 5G-Edge networks

The fifth-generation mobile networks bring an evolution in the development of novel classes of applications, increasing the demand for performance and flexibility to support new classes of services. Edge Computing and Network Function Virtualization are two key technologies that bring data processing closer to the user and allow the replacement of traditional hardware-based network functions with virtualized counterparts, which reduces costs and permits the provision of low-latency services. In this scenario, services are often implemented as a Service Function Chain (SFC), consisting of a set of ordered Virtual Network Functions (VNFs) that provide the required service functionality. During service execution, users can move from one location to another, which impacts service performance. An approach to reduce the impact of user mobility is to migrate services as users move to different places. However, the SFC migration problem imposes several challenges that require complex decision-making. VNFs should be migrated to nodes that have the necessary resources to execute the VNF instances. Also, the migration should be fast and effective, using as few resources as possible to reduce migration costs. In this paper, we propose a proactive and iterative SFC migration strategy to address the performance impact caused by user mobility. Our proposed solution anticipates the movement of users and estimates the impact of their mobility on service delay, proactively triggering service migrations when needed. The proposed algorithm decides which VNFs of the SFC should be migrated and which compute nodes will host the migrating VNFs, fulfilling service, resource, and migration constraints while maintaining a low migration overhead. Our algorithm also considers the dynamic nature of the edge environment in which other SFC placement and migrations might be occurring when an SFC is being migrated, which also introduces new challenges related to the availability and concurrency of resources required for migration.

Multi-objective optimization of SFC deployment using service aggregation and computing offload

New technologies such as virtualization and Software Defined Networking (SDN) have given traditional networks unprecedented flexibility. A Service Function Chain (SFC) formed by concatenating multiple Virtual Network Functions (VNFs) expands network functions to meet the growing personalized network requirements of applications. However, due to the decentralization of VNF instances and the tight cloud load, how to ensure Quality of Service (Qos) while deploying the SFC brings new challenges. Hence, a SFC deployment strategy based on multi-objective optimization, named MO-SACO is proposed, which regards delay optimization, reliability assurance and cost reduction as the main objectives. Specifically, we first design a VNF aggregation rule to handle raw SFC requests, which effectively reduces the SFC path latency and improves reliability. Then, VNF placement and traffic routing is modeled as a computation offloading decision based on cloud-fog-edge collaboration with the goal of target optimization according to functional, location, and resource constraints. Finally, we also take into account the performance changes of deployed SFCs and handle non-compliant requests in a timely manner. We conduct extensive simulations in networks of varying sizes and the results demonstrate that the proposed MO-SACO strategy not only achieves lower latency and cost, higher reliability compared with the state-of-the-art methods, but also has unexpected performance in terms of the deployment success rate and node load.

Efficient Online Scheduling of Service Function Chains Across Multiple Geo-Distributed Regions

Efficient Online Scheduling of Service Function Chains Across Multiple Geo-Distributed Regions

Cost-efficient flow migration for SFC dynamical scheduling in geo-distributed clouds

Combining Network Function Virtualization (NFV) and Software Defined Network (SDN) enables the delivery of network services in a flexible and cost-efficient manner. NFV allows elasticity scheduling of Virtual Network Function (VNF) instances to adopt the dynamic variation of network flows, such as VNF instance scale out or in and load balancing. To implement the elasticity scheduling, existing flows need to be re-routed through the VNF instances. One major problem is how to select appropriate flows for migration to reduce migration cost and improve network performance after migration. In this paper, we propose a Cost-efficient Flow Migration framework, CFM, to jointly optimize flow migration cost and network performance after migration (including flow routing cost and maximum VNF instance load) for flow migration in Service Function Chain manner. Experimental results demonstrate that CFM can reduce flow routing cost after migration by 43%∼62% compared with previous works, while achieving similar flow migration cost in scale out/in scenario. In load balancing scenario, CFM can also reduce flow migration cost by 65%∼79% and flow routing cost after migration by 43%∼79% compared with previous works, while ensuring that the maximum VNF instance load does not significantly increase.

Towards resources optimization in deploying service function chains with shared protection

Emerging artificial intelligence techniques enable the Internet-connected devices automatically generate massive SFC requests in scenarios with ultra-reliability demands such as unmanned vehicle systems, smart factories. In such scenarios, it is imperative to deploy these huge amounts of SFCs in a fault-tolerant manner. The existing works on fault-tolerant SFC deployment employs the dedicated SFC backup protection with the resources redundancy of at least 100%, which addresses the ratio between the backup and primary resource consumptions. Due to this, the dedicated SFC protection likely prevents these reliable SFCs from being massively deployed. To tackle this challenge, for the first time, this work studies how to enhance the resource utilization by deploying SFCs with shared protection. First, we introduce new concepts of SFC protection set and sharing-risk SFC graph (SCG). Next, we define a new problem called SFCs embedding with SCG-based shared protection. Then, we propose a heuristic algorithm called SCG-based SFC embedding and protecting (SCG-SEP), which is proved to be logarithm-approximate. Extensive simulations show that SCG-SEP outperforms the benchmarks by an average of 12.25% and 43.67%.

Bidirectional Service Function Chain Embedding for Interactive Applications in Mobile Edge networks

Bidirectional service function chain (BSFC) consists of multiple virtual network functions (VNFs). Through VNF deployment and link mapping, BSFCs can be embedded into resource-constrained mobile edge networks to provide low-latency network function services to users participating in interactive applications such as multi-player online games. Data from these users are routed through BSFCs to the edge node where the application is located for interaction and then returned to the users through the BSFCs, thus enabling synchronization among multiple users. However, the edge nodes or links have limited computing or bandwidth resources to serve only a fraction of users simultaneously. Therefore, the embedding decisions among different users can affect each other. In this paper, we propose a novel BSFC embedding strategy for interactive applications with the goal of minimizing computing and bandwidth resources while satisfying users' latency requirements. We first model the BSFC embedding problem as an integer nonlinear programming problem. Then, by closely examining the complexity of the problem, we propose a distributed algorithm based on game theory. We theoretically analyze the properties of the proposed algorithm and show that it can obtain a solution with a worst-case performance bound. Finally, extensive experiments show that the proposed algorithm outperforms several existing algorithms.

Comprehensive fault diagnosis for multiple coupled SFCs based on deep learning

Service Function Chains (SFCs) are deployed and executed in a complex network environment, where multiple SFCs are coupled by sharing various types of network functions and transmission links. As a result, diagnosing the state of these SFCs and accurately locating network fault becomes a challenge and plays a fundamental role in SFC management. In this paper, we introduce a comprehensive fault diagnosis mechanism for multiple coupled SFCs by applying the deep learning technique. By formulating the involved network nodes and transmission links into a heterogeneous graph model, we propose the Rendered Service Path Heterogeneous Graph Attention Network (RSP-HAN) model to comprehensively estimate the state of every network node and link. Typically, RSP-HAN first applies Bi-directional Long Short-Term Memory (Bi-LSTM) to normalize features from different types of network entities, then obtains the embedding results through intra-RSP and inter-RSP attention mechanisms. Finally, Multi-Layer Perceptron (MLP) is utilized to estimate the state of these network entities. We also setup an experiment platform and obtain extensive datasets to evaluate our RSP-HAN model. Experimental results show that RSP-HAN performs better in fault diagnosis for various types of network entities than several traditional methods and two deep learning-based baselines, the Graph Convolutional Network (GCN) and the Graph Attention Network (GAT).

Digital twin-assisted service function chaining in multi-domain computing power networks with multi-agent reinforcement learning

The emerging computing power network (CPN) is believed to undergo the paradigm reformation of network function virtualization (NFV) and service function chaining (SFC). It is prerequisite to explore the performance upper bound of NFV-assisted CPN before truly deploying the NFV and SFC technologies onto physical networks. Inspired by the application of digital twin (DT) in the industry and due to its advantage in synchronizing physical objects with their virtual replicas, we propose to use the DT to assist the SFC deployment in the multi-domain CPN, with the aid of multi-agent deep deterministic policy gradient (MADDPG) framework. First, we build a dynamic SFC mapping problem in the virtual twin network layer, by modeling the computing power, link bandwidth, delay performance and the VNF ordering as DT objects and constraints, to jointly optimize the energy consumption, end-to-end delay and the VNF re-deploying cost. Then, the prioritized experience replay and re-parameterization trick-empowered centralized training and decentralized execution MADDPG framework is utilized to learn the SFC deployment, by taking each domain controller as one agent. Finally, numerical experiments are carried out to validate the effectiveness of MADDPG in the cross-domain SFC deployment. For performance verification, the deployment success rate, number of crossed domains, energy consumption, end-to-end latency and load balancing degree are all taken as metrics, to show the performance of MADDPG compared to other learning frameworks.