Service Function Chain Embedding Research Articles

Energy-Aware Positioning Service Provisioning for Cloud-Edge-Vehicle Collaborative Network Based on DRL and Service Function Chain

In the collaborative intelligent transportation system, providing precise positioning services is costly. Reducing resource consumption and improving revenue are crucial to the development of positioning services. Therefore, a practical algorithm that combines cloud and edge network environments is necessary to improve the positioning services. Integrating network function virtualization and edge computing can provide users with more flexible and efficient services. Based on the above issues, we use the service function chain (SFC) to improve the positioning services provided in cloud-edge-vehicle collaborative networks (CEVCN). We propose a deep reinforcement learning-assisted SFC embedding algorithm and improve its performance through training. We construct a five-layer policy network to sense the environment of CEVCN and derive the optimal node selection strategy. Finally, we use the breadth-first search algorithm to solve the embedding scheme for virtual links. The simulation results show that our proposed algorithm has excellent performance. The long-term average revenue is improved by 21%, the long-term average revenue-cost ratio is improved by 13%, and the embedding rate is improved by 8%.

Research on Service Function Chain Embedding and Migration Algorithm for UAV IoT

This paper addresses the challenge of managing service function chaining (SFC) in an unmanned aerial vehicle (UAV) IoT, a dynamic network that integrates UAVs and IoT devices for various scenarios. To enhance the service quality and user experience of the UAV IoT, network functions must be flexibly configured and adjusted based on varying service demands and network situations. This paper presents a model for calculating benefits and an agile algorithm for embedding and migrating SFC based on particle swarm optimization (PSO). The model takes into account multiple factors such as SFC quality, resource utilization, and migration cost. It aims to maximize the SFC benefit and minimize the migration times. The algorithm leverages PSO’s global search and fast convergence to identify the optimal or near-optimal SFC placement and update it when the network state changes. Simulation experiments demonstrate that the proposed method improves network resource efficiency and outperforms existing methods. This paper presents a new idea and method for managing SFC in UAV IoT.

Multicast Service Function Chain Orchestration in SDN/NFV-Enabled Networks: Embedding, Readjustment, and Expanding

Multicast is an effective transmission mode to support ever-growing multimedia applications. The introduction of software defined networking (SDN) and network function virtualization (NFV) makes the multicast service operation more flexible and efficient. Nevertheless, one main challenge of SDN/NFV-enabled multicast is optimally orchestrating the service function chain (SFC) to match service and network resources. Compared with unicast, multicast SFC orchestration (MSO) is more challenging due to the features of multicast service like multicast routing and user fluidity (i.e., frequent user arrival and departure). There are still some gaps in the joint optimization of MSO and multicast routing, and very little attention is paid to user fluidity. In this paper, we study the MSO in SDN/NFV-enabled networks encompassing multicast SFC embedding (MSE), multicast SFC readjustment (MSR), and multicast SFC expanding (MSEP), three types of MSO. For each kind of MSO, we simultaneously consider several key optimization factors when jointly optimizing MSO and multicast routing. Besides, aside from MSE, we investigate two new types of MSO: MSR and MSEP, for efficient orchestration under the fluidity of users. Specifically, we define and formulate MSE, MSR, and MSEP problems and develop three novel algorithms to respectively solve them. Simulation results demonstrate that our algorithms outperform benchmark algorithms and achieve near-optimal performance.

A Two-Stage GCN-Based Deep Reinforcement Learning Framework for SFC Embedding in Multi-Datacenter Networks

Network Function Virtualization (NFV), which decouples network functions from hardware and transforms them into Virtual Network Functions (VNFs), is a crucial technology for data center (DC) networks. A service function chain (SFC) is composed of an ordered set of VNFs and virtual links (VLs) connecting them. To optimize the resource allocation in DC networks, we need to efficiently map SFCs onto the physical network. Nevertheless, the dynamics and diversity of SFC requests in multi-datacenter (MDC) networks pose a significant challenge in embedding SFCs. To overcome this challenge, we design a two-stage graph convolutional network (GCN) assisted deep reinforcement learning (DRL) scheme. This framework aims to maximize the overall acceptance ratio of SFC requests while minimizing the total cost in an MDC network. In the first stage, we propose a GCN-based DRL algorithm as a coarse granularity solution to the SFC embedding problem from the macro perspective. This solution outlines a local observation scope (LOS) for each agent in the multi-agent system of the second stage, where all agents simultaneously handle SFC requests from their respective DCs using a multi-agent framework from the micro perspective. Numerical evaluations show that, compared to state-of-the-art methods, the proposed scheme improves the acceptance ratio by approximately 13% compared with the Kolin algorithm and 18% compared with the DQN algorithm and saves the cost by around 28% compared with the Kolin and the DQN.

Service Function Chaining and Embedding With Heterogeneous Faults Tolerance in Edge Networks

In the 5G-and-beyond era, ultra-reliable low latency communication (URLLC) services are ubiquitous in edge networks. To enhance the performance metrics and the quality of service (QoS), URLLC services are delivered via a sequence of software-based network functions, also known as a service function chain (SFC). Towards reliable SFC delivery, it is imperative to incorporate fault-tolerance during SFC deployments. However, deploying an SFC with fault-tolerance is challenging because the protection mechanism needs to jointly consider multiple concurrent physical/virtual network failures and hardware/software failures. Considering these concurrent heterogeneous failures, this work investigates how to effectively deliver an SFC in edge networks with the objective of minimizing bandwidth resource consumption. First, we introduce the concept of k-heterogeneous-faults-tolerance and propose an augmented protection graph, called k-connected service function slices layered graph (KC-SLG). Based on the KC-SLG, we formulate a novel problem called k-heterogeneous-faults-tolerant SFC embedding and propose an effective algorithm, called fault-tolerant service function graph embedding (FT-SFGE). FT-SFGE employs two proposed techniques: k-connected network slicing (KC-NS) and k-connected function slicing (KC-FS). Via thorough mathematical proofs, we show that KC-NS is 2-approximate. Extensive simulations show that KC-FS has the best average cost-efficiency when k = 2, and FT-SFGE outperforms the schemes directly extended from the state-of-the-art.

Intelligent and Resource-Conserving Service Function Chain (SFC) Embedding

Network Function Virtualization (NFV) opens us great opportunities for network processing with higher resource efficiency and flexibility. In this respect, there is an increasing need for intelligent orchestration mechanisms, such that NFV can exploit its potential and live up to its promise. Genetic algorithms have emerged as a promising alternative to the proliferation of heuristic and exact methods for the Service Function Chain (SFC) embedding problem. To this end, we design and evaluate a genetic algorithm (GA), which computes efficient embeddings with runtimes on par with approximate methods. We present a GA model as state-space search in order to clarify the design choices of a GA. Our proposed GA utilizes a heuristic for the generation of the initial population, with the aim of directing the search towards the solution. Given the sensitivity of GAs on their various parameters, we introduce a parameter adjustment framework for GA fine-tuning. A comparative evaluation among a range of GA variants with diverse features sheds light on the impact of these features on SFC embedding efficiency. The GA variant that stands out is further benchmarked against a baseline greedy algorithm and a state-of-the-art heuristic. Our evaluation results indicate that the GA yields notable gains in terms of request acceptance and resource efficiency.

Slice-Based Service Function Chain Embedding for End-to-End Network Slice Deployment

This paper investigates the slice-based service function chain embedding (SBSFCE) problem, which is to embed the service function chains (SFCs) of flows from different slices on a physical network for end-to-end network slice deployment. Compared with regarding slice deployment as complete virtual network embedding (VNE), deploying slices from the perspective of SBSFCE is beneficial for achieving more delicate resource allocation and jointly optimizing virtual network function (VNF) mapping and link mapping without the need for particular virtual topology designs. However, performing effective SBSFCE also faces several key challenges like diversified and differentiated requirements of flows, inter-slice and intra-slice VNF sharing, priority-aware admission control, and VNF placement restrictions, and few existing SBSFCE works have comprehensively considered or solved these challenges. In view of this, we address the SBSFCE problem by jointly considering the above key challenges in this paper. Specifically, we formulate the SBSFCE problem as an integer linear programming (ILP) that aims to maximize flow acceptance ratios and minimize network resource costs. Then, we propose two novel heuristic algorithms, weight-oriented embedding (WOE) and weight-oriented ratio embedding (WORE), to solve the problem. Simulation results demonstrate that our algorithms outperform benchmark algorithms and achieve near-optimal performance.

Service-Aware Resource Orchestration in Ultra-Dense LEO Satellite-Terrestrial Integrated 6G: A Service Function Chain Approach

With the rapid expansion of the scale of deployed low earth orbit (LEO) satellites, the ultra-dense LEO satellite-terrestrial integrated network (LTIN) is envisioned as a promising architecture in the sixth-generation (6G) system to implement seamless connectivity and high-speed data rate service. Especially for ultra-remote real-time services with long transmission distance and high delay requirements, the integrated network can guarantee its end-to-end service continuity. However, many challenges have been posed to the efficient resource orchestration for the service delivery, owing to the large scale, heterogeneity and high mobility of the integrated network. For each service, its data needs to go through a series of on-board processing, before being downloaded to the terrestrial network for further applications. To this end, service function chain (SFC), an ordered concatenation of network functions (NFs), is introduced to support service provision. By allocating the constituent NFs over the LTIN, we propose an efficient multiple service delivery scheme to minimize the overall delivery completion latency, while taking into account resource sharing and competition among multiple SFCs. First, we formulate the multiple SFC embedding problem as a noncooperative game that is further proved as the weighted potential game with at least one Nash equilibrium (NE). With the help of the proposed global coordination mechanism, we design two algorithms to obtain the NE. One is the best response (BR) algorithm with faster convergence, while the other is adaptive play (AP) algorithm with more capacity for best solutions. Then, the stochastic learning (SL) algorithm is proposed to adapt to network dynamics and reduce global information exchange. Finally, extensive simulations validate the convergence and effectiveness of the proposed algorithms.

Efficient embedding of service function chains in space-division multiplexing elastic optical networks

Virtual Network Functions (VNFs) are implemented as software instances on generic servers in Data Centers (DCs). A network service is implemented by an ordered list of VNFs, called Service Function Chain (SFC). SFC embedding decides which DC to place/instantiate a VNF, and how to route traffic through the instantiated VNFs/DCs. Benefits from its high capacity and flexibility, the Space-Division Multiplexing Elastic Optical Networks (SDM-EONs) have been proposed as a promising solution to provide connection service between DCs. In this paper, the problem of SFC embedding in SDM-EONs is studied. We first formulate an Integer Linear Programming (ILP) to minimize the Maximum Index of the Utilized Frequency Slot (MIUFS) for a given set of SFC requests. To achieve scalability, a greedy algorithm is proposed to embed SFCs separately. For each SFC, the lowest cost embedding solution can be found by enumeration would be selected for service embedding. A novel stretch algorithm is then designed to reduce the computing complexity further. The key idea is to place VNFs close to the least-cost path between the source and destination of a request. Three strategies to measure the path cost are evaluated. Simulation results show that our stretch algorithm gives the best overall performance in terms of running time, bandwidth consumption, MIUFS and request blocking probability.

Unsupervised Deep Learning for Distributed Service Function Chain Embedding

Unsupervised Deep Learning for Distributed Service Function Chain Embedding

A reinforcement learning approach based on convolutional network for dynamic service function chain embedding in IoT

SummaryNetwork communication technologies are developing rapidly in various scenarios, such as 6G, SDN/NFV, and IoT. And the demand for dynamic service function chain orchestration is increasing day by day. Due to the dynamic complexity of IoT networks, the service function chain (SFC) embedding problem in IoT scenarios is more difficult. In this paper, a reinforcement learning algorithm based on convolutional neural network is first applied to SFC embedding, combined with DQN's experience pool reply and target network mechanism. The proposed scheme is verified in three typical complex networks: Random network, BA scale‐free network, and small‐world network. The experimental data suggest that the applicability of the approach proposed in IoT scenarios and, on the whole, the proposed algorithm can achieve lower latency and faster convergence performance than the mainstream algorithms with the increase of SFC number and node number.

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.

Service Function Chaining Embedding in Hybrid Optical-Electronic Networks

Network function virtualization (NFV) provides cost-effective network service by means of service function chain (SFC) for users. Meanwhile, billions of terminals are connected to the Internet (Internet of Things), and the vast variability of bandwidth requirements of SFC requests from heterogeneous terminals poses challenges to the network management, then neither only electronic networks nor only optical networks can provide highly efficient transmissions for network services. In this paper, a hybrid optical-electronic network paradigm is considered to provide connection services for SFC requests to benefit from the large bandwidth of optical networks and the flexibility of electronic networks. A profit-based utility function is established, and a mathematical formulation of the SFC embedding problem with the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$log$</tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$sum$</tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$exp$</tex-math></inline-formula> approximation is provided. An approach based on Markov chain modeling is used to adaptively embed SFC requests with the aim to maximize the utility function. Simulation results demonstrate the quality and adaptivity of the proposed approach in terms of the utility function.

Energy-Aware Service Function Chain Embedding in Edge–Cloud Environments for IoT Applications

The implementation of Internet-of-Things (IoT) applications faces several challenges in practice, such as compliance with Quality-of-Service requirements, resource constraints, and energy consumption. In this context, the joint edge-cloud paradigm for IoT applications can resolve some of the issues arising in pure cloud computing scenarios, such as those related to latency, energy, or privacy. Therefore, an edge-cloud environment could be promising for resource and energy-efficient IoT applications that implement virtual network functions (VNFs) bound together into service function chains (SFCs). However, a resource and energy-efficient SFC placement requires smart SFC embedding mechanisms in the edge-cloud environment, as several challenges arise, such as IoT service chain modeling and evaluation, the tradeoff between resource allocation, energy efficiency and performance, and the resource dynamics. In this article, we address issues in modeling resource and energy utilization for IoT applications in edge-cloud environments. A smart traffic monitoring IP camera system is deployed as a use case for a realistic modeling of a service chain. The system is implemented in our testbed, which is designed and developed specifically to model and investigate the resource and energy utilization of SFC embedding strategies. A resource and energy-aware SFC strategy in the edge-cloud environment for IoT applications is then proposed. Our algorithm is able to cope with dynamic load and resource situations emerging from dynamic SFC requests. The strategy is evaluated systematically in terms of the acceptance ratio of SFC requests, resource efficiency and utilization, power consumption, and VNF migrations depending on the offered system load. Results show that our strategy outperforms some existing approaches in terms of resource and energy efficiency, thus it overcomes the relevant challenges from practice and meets the demands of IoT applications.

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.

A Novel Approach for Service Function Chain Embedding in Cloud Datacenter Networks

Network function virtualization (NFV) and software defined networking (SDN) have recently introduced together, which show great potential in openness, innovation, flexibility and scalability of the networks. Embedding SDN/NFV-enabled service function chain (SFC) has been considered as an efficient way to achieve the end-to-end communication. Considering the multiple users’ delay diversities oriented to future networks, from the perspective of network operators, determining the placement of virtual network functions (VNFs) and routing the service paths is a challenging problem. However, the traditional solutions (e.g., exact, heuristic, meta-heuristic, game, etc.) based on the SFC dynamic embedding problem (SFC-DMP) follow the strong assumptions as usual, which might limit their applications in dynamic and complex network scenarios. To address this narrow, we are motivated to present a novel approach for handling SFC-DMP in the cloud datacenter networks that simultaneously combines the quantum genetic algorithm (QGA) and fuzzy logic. Simulation results show the efficient performance of our proposal.

Tabu Search For Service Function Chain Composition In NFV

One of the main challenges for service providers when they try to deploy Network Function Virtualization based networks is to efficiently make use of substrate network resources to facilitate the design, delivery and operation of network services in a dynamic and scalable manner. This challenge is known as Network Function Virtualization Resource Allocation, which can be addressed in two stages called Service Function Chain Composition and Service Function Chain Embedding. The main objective of the first stage is to compose an optimized chain of Virtual Network Functions, which offers a complete network service. The second stage is responsible for the embedding of such network service onto a physical infrastructure based on virtualization technologies. In this paper, we propose a metaheuristic algorithm to face the scalability challenges of exact approaches, when solving the composition stage. The performance of our metaheuristic approach shows that large, complex and heterogeneous service requests can be processed in reasonable running times, finding better results compared with existing heuristics in the state of the art.

GDM: A General Distributed Method for Cross-Domain Service Function Chain Embedding

Emerging technologies such as network function virtualization (NFV) and software defined networking (SDN) provide a promising way to implement service function chain (SFC), a chain-ordered set of network functions, to support heterogeneous network services through a shared substrate network. A major challenge in this respect is the SFC embedding with respect to constraints of physical resources. Furthermore, for practical purposes, SFC embedding across multiple domains becomes essential. This challenge is referred to as the cross-domain SFC embedding problem, which is intractable due to various reasons, such as the confidentiality of intra-domain information and the domain's local autonomy. In this paper, we propose GDM, a general distributed method for cross-domain SFC embedding. Besides preserving the privacy and autonomy of domains, GDM guarantees fair competition among domains while balancing loads among domains. It first partitions SFC by utilizing an algorithm that can be instantiated to support different embedding goals. Then it allows domains to embed their assigned segments following their policies. Finally, to improve the capability of the whole substrate network to accommodate more SFCs, it implements domain-level load balancing by migrating the deployed VNFs while avoiding excessive influence on the SFC embedding solution. Evaluation results demonstrate that our method performs better in improving acceptance ratio and optimizing domains' embedding goals compared to the existing methods, and it has better scalability.

Dynamic Service Function Chain Embedding for NFV-Enabled IoT: A Deep Reinforcement Learning Approach

The Internet of things (IoT) is becoming more and more flexible and economical with the advancement in information and communication technologies. However, IoT networks will be ultra-dense with the explosive growth of IoT devices. Network function virtualization (NFV) emerges to provide flexible network frameworks and efficient resource management for the performance of IoT networks. In NFV-enabled IoT infrastructure, service function chain (SFC) is an ordered combination of virtual network functions (VNFs) that are related to each other based on the logic of IoT applications. However, the embedding process of SFC to IoT networks is becoming a big challenge due to the dynamic nature of IoT networks and the abundance of IoT terminals. In this paper, we decompose the complex VNFs into smaller virtual network function components (VNFCs) to make more effective decisions since VNF nodes and IoT network devices are usually heterogeneous. In addition, a deep reinforcement learning (DRL) based scheme with experience replay and target network is proposed as a solution that can efficiently handle complex and dynamic SFC embedding scenarios in IoT. Our simulations consider different types of IoT network topologies. The simulation results present the efficiency of the proposed dynamic SFC embedding scheme.

ESSO: An Energy Smart Service Function Chain Orchestrator

The rapid development of technologies such as photo-intensive social networks, on-demand video streaming, online gaming, and the Internet of Things (IoT) is causing a tremendous growth of traffic volume. Such large-scale expansion is leading to higher energy consumption and carbon footprint for the telecommunication industry. Governments are trying to minimize the environmental impact by introducing regulations and taxes; driving companies to use renewable energy. However, renewable energy is still not as cost-effective compared to traditional sources of energy ( i.e. , brown energy), and their availability varies significantly across time and geographic locations. Therefore, it is a challenge for telecommunication companies to comply with regulations and minimize carbon footprint without significantly increasing their operational cost. In this context, we propose an Energy Smart Service Function Chain Orchestrator called ESSO. ESSO reduces the overall carbon footprint of a telecommunication network by opportunistically adapting Service Function Chain (SFC) locations to utilize more energy at locations with surplus renewable energy. ESSO minimizes brown energy consumption by migrating SFCs across different locations. In addition, ESSO provisions SFC components in a manner that allows switches, switch ports, and servers to be put into low-power consumption state. Our trace-driven simulations on real ISP topologies show that considering the availability of renewable energy sources during SFC embedding even for a small-scale network can result in 2- $3\times $ reduction in carbon footprint.