Service Function Chain Deployment 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.

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.

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.

Ubiquitous learning models for 5G communication network utility maximization through utility-based service function chain deployment

The main problem of deploying service function chains in virtualized 5G networks is dealt with in wireless 5G communications and effective ubiquitous learning models. The aim is to ensure differentiated network performance for a wide range of services while maximizing the collaborative revenue of infrastructure operators and wireless virtual operators. To achieve this, a utility-based service function chain deployment strategy is introduced, tailored to the specific characteristics of Ubiquitous Learning based 5G-RAN-Architecture. Consideration is given to the virtual operator's maximum tolerable end-to-end latency, minimum service rate requirements, and the infrastructure operator's constraints on computing and link resources. It also looks at how different deployment scenarios for service function chains affect network performance and creates a utility model using a business framework. The ultimate objective is to optimize the collective revenue of infrastructure operators and virtual operators. The approach leverages genetic algorithms and Matlab's Linprog function for iterative problem-solving. The graph clearly shows that the SFC deployment algorithm in this study uses less infrastructure resources for Front Haul links. This implies that the algorithm successfully reduces the load on Front Haul lines, which in turn lowers the cost of SFC deployment and makes it easier to deploy more SFCs across the infrastructure. This work contributes to the evolution of 5G wireless communications and its seamless integration with ubiquitous learning models.

The Optimized Deployment of Service Function Chain Based on Reinforcement Learning

With the rapid development and application of technologies such as artificial intelligence, the Internet of Things, and cloud computing, data is showing explosive growth. In order to address the rising energy consumption due to the increasing number of devices in the traditional network architecture, software-defined networking and network function virtualization have been proposed. In this paper, we propose a reinforcement learning model based on actor-critic architecture. The service function chain deployment problem is mathematically modeled, and minimizing the total service function chain delay is taken as the optimization objective. The experimental results demonstrate that the service function chain deployment algorithm proposed in this paper is improved in terms of total system latency.

Service Function Chain Deployment Using Deep Q Learning and Tidal Mechanism

Service Function Chain Deployment Using Deep <i>Q</i> Learning and Tidal Mechanism

Service Function Chain Deployment Algorithm Based on Deep Reinforcement Learning in Space–Air–Ground Integrated Network

SAGIN is formed by the fusion of ground networks and aircraft networks. It breaks through the limitation of communication, which cannot cover the whole world, bringing new opportunities for network communication in remote areas. However, many heterogeneous devices in SAGIN pose significant challenges in terms of end-to-end resource management, and the limited regional heterogeneous resources also threaten the QoS for users. In this regard, this paper proposes a hierarchical resource management structure for SAGIN, named SAGIN-MEC, based on a SDN, NFV, and MEC, aiming to facilitate the systematic management of heterogeneous network resources. Furthermore, to minimize the operator deployment costs while ensuring the QoS, this paper formulates a resource scheduling optimization model tailored to SAGIN scenarios to minimize energy consumption. Additionally, we propose a deployment algorithm, named DRL-G, which is based on heuristics and DRL, aiming to allocate heterogeneous network resources within SAGIN effectively. Experimental results showed that SAGIN-MEC can reduce the end-to-end delay by 6–15 ms compared to the terrestrial edge network, and compared to other algorithms, the DRL-G algorithm can improve the service request reception rate by up to 20%. In terms of energy consumption, it reduces the average energy consumption by 4.4% compared to the PG algorithm.

A Lean Networking Framework (LeaNet): Potential Technical Space and Approaches for Latency Sensitive Mobile Services

With the development of mobile services such as the Internet of vehicles, industrial Internet, and holographic communication, ultra-low latency, ultra-high reliability, and pervasive mobility have become common indicators of the comprehensive interconnection between human-machine-things. These common indicators are the foundations to expand the application scenarios of 5G communication technologies and are also the key to achieving the 6G vision. This paper summarizes the technologies in various layers of low-latency mobile networks from a panoramic perspective and highlights the limitations of previous works in terms of mobile network architecture, virtualization technology, and service function chaining (SFC) deployment. Also, the potential techniques space from these three aspects is analyzed. Based on this, a technical framework, named lean networking (LeaNet), is proposed in this paper to reduce latency for network services through global optimization. The compact architecture eliminates triangular routing, thus significantly simplifying the network architecture and reducing both link and node latency. Meanwhile, the network functions virtualization with real-time performance provides a feasible solution to hybrid deployment of multi-scenario and diversified services, especially for latency-sensitive applications. Besides, the framework for intelligent and closed-loop SFC deployment with low latency and high resource efficiency is established and analyzed comprehensively. Simulation studies verify that LeaNet is superior in reducing latency over typical 5G core networks.

GCN-Based Multi-Agent Deep Reinforcement Learning for Dynamic Service Function Chain Deployment in IoT

GCN-Based Multi-Agent Deep Reinforcement Learning for Dynamic Service Function Chain Deployment in IoT

Towards Attack-Resistant Service Function Chain Migration: A Model-Based Adaptive Proximal Policy Optimization Approach

Network function virtualization (NFV) supports the rapid development of service function chain (SFC), which efficiently connects a sequence of network virtual function instances (VNFIs) placed into physical infrastructures. Current SFC migration mechanisms usually keep static SFC deployment after finishing certain objectives, and deployment methods mostly provide static resource allocation for VNFIs. Therefore, the adversary has enough time to plan for devastating attacks for in-service SFCs. Fortunately, moving target defense (MTD) was proposed as a game-changing solution to dynamically adjust network configurations. However, existing MTD methods mostly depend on attack-defense models, and lack adaptive mutation period. In this paper, we propose an Intelligence-Driven Service Function Chain Migration (ID-SFCM) scheme. Firstly, we model a Markov decision process (MDP) to formulate the dynamic arrival or departure of SFCs. To remove infeasible actions from the action space of MDP, we formalize the SFC deployment as a constrained satisfaction problem. Then, we design a deep reinforcement learning (DRL) algorithm named model-based adaptive proximal policy optimization (MA-PPO) to enable attack-resistant migration decisions and adaptive migration period. Finally, we evaluate the defense performance by multiple attack strategies and two realistic datasets called CICIDS-2017 and LYCOS-IDS2017 respectively. Simulation results highlight the effectiveness of ID-SFCM compared with representative solutions.

Research on Reliable Deployment Algorithm for Service Function Chain Based on Deep Reinforcement Learning

This paper investigates the reliable deployment algorithm for Service Function Chains (SFC) based on deep reinforcement learning. SFC, as a chained function composition for complex network services, plays a crucial role in improving network efficiency and stability. To address the issue of existing SFC deployment algorithms that overlook the reliability of network functions and links, this paper proposes a deep reinforcement learning-based algorithm that utilizes a virtual network function and virtual link reliability mapping model for optimization. By learning the mapping between system states and actions, the algorithm can optimize the deployment strategy of SFC, thereby enhancing its reliability and performance. Experimental results demonstrate that the proposed algorithm can significantly improve the reliability of SFC and have practical implications for network service deployment.

Intelligent Deployment of Delay-Sensitive Service Function Chain Based on Parallelization and Improved Cuckoo Search Algorithm

As one of the key focuses in 6G research, the space–air–ground integrated network incorporates a variety of technological frameworks. Network function virtualization allows network functions to be deployed on general servers in the form of software and creates a service function chain (SFC) according to user service requirements. In recent years, the deployment of SFC has become popular research due to the increasing demand for low delay in network application scenarios. Low delay is a crucial indicator of the quality of service, especially for delay-sensitive applications. To address this issue, we propose a method for the deployment of delay-sensitive SFC based on parallelization and the improved cuckoo search (ICS) algorithm (DDSSFC-PICS). This method optimizes the composition and deployment of SFC jointly. First, the serial structure of the SFC is transformed into a parallel structure by determining the dependency of virtual network functions, which reduces the length of the SFC and thereby reduces delay. Second, with the optimization goal of minimizing network delay, a parallel SFC deployment model is established under constraints including packet loss rate and resource availability. Finally, the ICS algorithm is applied for optimization, where delay is used as the fitness measure. By improving the Lévy flight step size and drawing inspiration from the whale algorithm, the performance of the cuckoo search (CS) algorithm is enhanced, leading to a further reduction in delay. The simulation results show that using the same CS deployment method, parallelized SFC has a significantly lower delay compared to serial SFC. Furthermore, the DDSSFC-PICS reduces the delay by 22.58% and 19.02%, respectively, compared with the CS deployment and particle swarm optimization SFC deployment methods.

Service Function Chaining to Support Ultra-Low Latency Communication in NFV

Network function virtualization (NFV) has the potential to fundamentally transform conventional network architecture through the decoupling of software from dedicated hardware. The convergence of virtualization and cloud computing technologies has revolutionized the networking landscape, offering a wide range of advantages, including improved flexibility, manageability, and scalability. The importance of network capability in enabling ultra-low latency applications has been greatly amplified in the current era due to the increased demand for emerging services such as autonomous driving, teleoperated driving, virtual reality, and remote surgery. This paper presents a novel and efficient methodology for service function chaining (SFC) in an NFV-enabled network that aims to minimize latency and optimize the utilization of physical network resources, with a specific focus on ultra-low latency applications. In our proposed methodology, we offer flow prioritization and an adjustable priority coefficient factor (µ) to reserve a portion of physical network resources exclusively for ultra-low latency applications in order to optimize the deployment paths of these applications further. We formulate the SFC deployment problem as an integer linear programming (ILP) optimization model. Furthermore, we propose a set of heuristic algorithms that yield near-optimal solutions with minimal optimality gaps and execution times, making them practical for large-scale network topologies. Performance evaluations demonstrate the effectiveness of our proposed methodology in enabling ultra-low latency applications in an NFV-enabled network. Compared to existing algorithms, our proposed methodology achieves notable enhancements in terms of the end-to-end delay (up to 22 percent), bandwidth utilization (up to 28 percent), and SFC acceptance rate (up to 13 percent).

Forecast-Assisted Service Function Chain Dynamic Deployment for SDN/NFV-Enabled Cloud Management Systems

Software-defined network (SDN) and network function virtualization (NFV) are acknowledged as the most promising technologies to effectively allocate resource for network service. A service function chain (SFC), which can deploy virtualized network functions (VNFs) and chain them with associated flows allocation, can be used to represent each network service owing to the introduction of the SDN/NFV technology. Co-hosted applications on multiple Internet of Things terminals have dynamic and time-varying service requirements, in order to allocate network resources optimally and meet the end-to-end delay requirements of services, sufficient strategies are required to satisfy the continuously changing service demands. In this article, a deep learning model that combines the multitask regression layer above the graph neural networks is first presented to predict the future resource requirements of each VNF instance. The SFC deployment problem is then solved using the integer nonlinear programming (INLP) approach, and a novel prediction-assisted Viterbi algorithm is presented to overcome the scalability problem of the INLP approach. According to the simulation findings, the proposed deep model provides a minimum of a 6.2% improvement in prediction accuracy over baseline prediction models, and the proposed SFC deployment strategy has been demonstrated to deliver better performance in terms of acceptance ratio and revenue, compared to the current passive deployment algorithms.

Compiling Service Function Chains via Fine-Grained Composition in the Programmable Data Plane

Service function chains (SFCs) are fundamental services in today's datacenters and ISP networks. Explosive volume of network traffic creates high demands for low latency and high performance. The emergence of programmable data planes has offered a new way to overcome the problem. However, limited by pipeline constraints in hardware architecture, implementing multiple network functions on programmable data planes is challenging. Besides, considering various types of network functions, e.g., stateful network functions, a general model is essential for abstracting distinct network functions. In this article, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pSFC</i> which provides a fine-grained SFCs deployment scheme in programmable data planes. Control flow graph (CFG) is proposed to abstract and analyze various network functions. Then we model pipeline constraints in the hardware architecture using an ILP (Integer Linear Programming), and model the SFCs deployment in the substrate network as a one big switch (OBS) problem. To reduce deployment cost, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pSFC</i> first composes multiple SFCs to a compound CFG for eliminating redundant logics within SFCs, further decomposes the compound CFG based on the resource limitation per stage, and finally maps the OBS into the substrate network. We have implemented <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pSFC</i> in both bmv2 software switch and P4 hardware switch (i.e., Intel Tofino ASIC). Evaluation results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pSFC</i> reduces switch costs by 45.7% and decreases average latency by 22% without compromising throughput.

Towards deploying SFC with parallelized VNFs under resource demand uncertainty in mobile edge computing

The increasing demand in Mobile Edge Computing (MEC) networks has led to the emergence of Network Function Virtualization (NFV) technology. Through the virtualization technique, NFV can decouple each Virtual Network Function (VNF) from the hardware and deploy it flexibly in the MEC. Each service request in NFV assembles a Service Function Chain (SFC) by tying different VNFs and processes the traffic in the chained order. How to allocate resources and deploy SFC in MEC to meet Quality of Service (QoS) requirements is an important challenge for NFV. An increase in the number of VNFs with traffic passing one after the other causes high delay in SFC assembly. Also, the demanded resources may change during SFC assembly, which is clearly neglected in existing works. Therefore, high delay and resource demand uncertainty during SFC assembly affect QoS. With this motivation, we solve the SFC deployment problem by Parallelizing VNFs under Resource Demand Uncertainty (PVRDU) in MEC. We use the dependency between VNFs to transform the original SFC into a parallel SFC and then assemble several sub-SFCs based on a Deep Reinforcement Learning (DRL) approach. Meanwhile, PVRDU uses a Markov-based approximation algorithm to handle resource demand uncertainty. We perform extensive trace-driven simulations to verify the effectiveness of the proposed algorithm. The evaluation results of PVRDU are promising in different aspects compared to state-of-the-art methods. Specifically, for the considered scenario, DPC reduced SFC delay by as much as 8.7%.

Dynamic Service Function Chain Deployment and Readjustment Method Based on Deep Reinforcement Learning

With the advent of Software Defined Network (SDN) and Network Functions Virtualization (NFV), network operators can offer Service Function Chain (SFC) flexibly to accommodate the diverse network function (NF) requirements of their users. However, deploying SFCs efficiently on the underlying network in response to dynamic SFC requests poses significant challenges and complexities. This paper proposes a dynamic SFC deployment and readjustment method based on deep Q network (DQN) and M Shortest Path Algorithm (MQDR) to address this problem. We develop a model of the dynamic deployment and readjustment of the SFC problem on the basis of the NFV/SFC network to maximize the request acceptance rate. We transform the problem into a Markov Decision Process (MDP) and further apply Reinforcement Learning (RL) to achieve this goal. In our proposed method (MQDR), we employ two agents that dynamically deploy and readjust SFCs collaboratively to enhance the service request acceptance rate. We reduce the action space for dynamic deployment by applying the M Shortest Path Algorithm (MSPA) and decrease the action space for readjustment from two dimensions to one. By reducing the action space, we decrease the training difficulty and improve the actual training effect of our proposed algorithm. The simulation experiments show that MDQR improves the request acceptance rate by approximately 25% compared with the original DQN algorithm and 9.3% compared with the Load Balancing Shortest Path (LBSP) algorithm.

Revenue Maximizing Online Service Function Chain Deployment in Multi-Tier Computing Network

Multi-tier computing (MC) is a promising architecture that integrates cloud computing, fog computing, and edge computing to provide users with a consistent experience of computing services by fusing computing devices within the network through virtualization technology. Although MC combines powerful computation and communication resources, the massive demand from Service Function Chain (SFC) deployments continues to make it challenging regarding resource constraints, latency satisfaction, and revenue-cost tradeoffs. To this end, in this article, we study an SFC deployment problem in MC and formulate a problem for maximizing the revenue of online SFC deployment under latency, computation resources, and communication resources constraints. To solve this online problem better, we construct a computation and communication resource cost model and transform the original online problem into a deployment cost minimization problem and a request admission problem by an alternating optimization approach. To solve the two subproblems, we propose an online approximation algorithm with a provable competitive ratio for the particular scenario with no latency requirements. Then, based on the cost model, we propose an online heuristic algorithm that adopts a binary search method for the original problem with latency requirements. Simulation experiments show that our two proposed online algorithms have advantages in total revenue, running time, and load balancing compared with other comparison algorithms.

Service Function Chaining in Industrial Internet of Things With Edge Intelligence: A Natural Actor-Critic Approach

Owing to network function virtualization (NFV), each industrial application is constructed as a service function chain (SFC), concatenating the ordered service functions, to offer applications more flexibly in industrial Internet of Things (IIoT). When it comes to the emerging edge intelligence, the integration of NFV with edge in IIoT would enable more close-proximity services, yet also posing new challenges owing to more complicated environment. Although some efforts have been made to service function chaining in IIoT, the radio resource dynamics are not fully perceived. In this article, we investigate the radio-aware SFC deployment in the edge-enabled IIoT. First, a radio-aware deployment formulation is exhibited, steering the flow traversing both wireless and wired links. Next, Markov decision process is exhibited to track dynamics in both IIoT and radio resources. Afterwards, the natural gradient-based actor-critic SFC paradigm is introduced to adapt to network variation, by incorporating the curvature of parameter space into gradient information. To resolve the high-dimensionality in action space, we then recur to the norm penalty approach, reducing the space size by two orders of magnitude. Finally, numerical experiments are executed to uncover superiority of presented method, disclosing that the latency performance benefits from both the SFC routing between IIoT servers and elaborated wireless resource orchestration.

DRL-D: Revenue-Aware Online Service Function Chain Deployment via Deep Reinforcement Learning

Network function virtualization (NFV) is a promising paradigm where network functions are migrated from dedicated hardware appliances onto software middleboxes to promote service agility and reduce management costs. Benefiting from the NFV, the service function chain (SFC) has emerged as a popular network service form. It allows network traffic to pass through a series of virtual network functions in a specific order required by the business logic to arrange a complex service. However, SFC deployment is facing new challenges in seeking a trade-off between pursuing the objective of high long-term average revenue and making decisions in an online manner. In this paper, we propose DRL-D, a deep reinforcement learning-based approach for the online SFC deployment problem to satisfy different demands of SFC requests within resource constraints of the underlying infrastructure. DRL-D aims to maximize the long-term average revenue by combining the strengths of the graph convolutional network in learning a comprehensive representation of network state and the temporal-difference learning in generating deployment solutions for the SFC requests on the fly. Then a heuristic algorithm and a new prioritized experience replay technique are integrated to optimize the DRL framework and reduce the time complexity. Experimental results demonstrate the superiority of our DRL-D approach when compared with other benchmarks in terms of the long-term average revenue, acceptance ratio, and revenue-to-cost ratio. Performance evaluation shows that DRL-D possesses good robustness under different scales of physical networks and achieves excellent deployment performance within acceptable runtime.