Service Function Chain Requests Research Articles

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.

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.

Dynamic service function chains placement based on parallelized requests in edge computing environment

AbstractThe advent of Network Function Virtualization (NFV) technology brings flexible traffic engineering to edge computing environments. Online services in NFV are chained as Service Function Chains (SFCs), which consist of ordered sequences of Virtual Network Functions (VNFs). The SFC Placement (SFCP) problem is solved under Quality of Service (QoS) requirements and limited resource availability by directing traffic to the required VNFs. However, SFC assembly leads to high latency and network congestion by increasing the count of VNFs, which parallelized SFC can overcome this problem. With parallelizing an SFC request, independent VNFs are activated simultaneously and computational acceleration is realized by reducing the SFC length. Any pair of VNFs that do not conflict with traffic can be activated simultaneously. Most VNFs are deployed on distributed servers for load balancing, which makes SFC parallelization challenging. Meanwhile, the cost of merging/duplicating packets for parallelized SFCs between different servers is not negligible. Hence, in this article, Distributed Parallel Chaining (DPC) is proposed which is an algorithm based on Deep Reinforcement Learning (DRL) approaches. The DPC algorithm solves the SFCP problem to maximize the Long‐Term Expected Cumulative Reward (LTECR). DPC incorporates an Asynchronous Advantage Actor‐Critic (A3C) algorithm as a new approach based on DRL to increase the admission ability of future SFC requests by maximizing LTECR. The evaluation results show the effectiveness of the proposed algorithm from different aspects. Specifically, compared to the best existing approaches, DPC can reduce SFC latency by 8.7%.

VNF Migration in Digital Twin Network for NFV Environment

Network Function Virtualization (NFV) allows for the dynamic provisioning of Virtual Network Functions (VNFs), adapting services to the complex and dynamic network environment to enhance network performance. However, VNF migration and energy consumption pose significant challenges due to the dynamic nature of the physical network. In order to maximize the acceptance rate of Service Function Chain Requests (SFCR), and reduce VNF migration and energy consumption as much as possible, we summarize several related factors such as the node hosting state, link hosting state, energy consumption, migrated nodes, and whether the mapping is successful. We define the Markov decision process by considering the factors mentioned above. Next, we design the VNF migration algorithm utilizing actor–critic models, graph convolution networks, and LSTM networks. In order to reduce the risk of trial and error during training and prediction in deep reinforcement learning scenarios, we designed a network architecture based on a digital twin (DT). In simulation experiments, compared with the FF algorithm that greedily selects the first available node, our AC_GCN algorithm significantly improves the acceptance rate of SFC requests by 2.9 times more than the FF algorithm in small topology experiments, and 27 times more than the FF algorithm in large topology experiments. Compared with the deep reinforcement learning (DRL) algorithm, which does not consider all the above factors together, for the small topology experiment, our AC_GCN algorithm outperforms the DRL algorithm in terms of request acceptance rate by 13%, underperforms compared to the DRL algorithm in terms of energy consumption by 3.8%, and underperforms compared to the DRL algorithm in terms of the number of migrated nodes for 22%; for the large topology experiment, our AC_GCN algorithm outperforms the DRL algorithm in terms of the request acceptance rate by 7.7%, outperforms the DRL algorithm in terms of energy consumption by 0.4%, and outperforms the DRL algorithm in terms of the number of migrated nodes by 1.6%.

Secure and Intelligent Service Function Chain for Sustainable Services in Healthcare Cyber Physical Systems

The gradual integration of Internet of Things (IoT) devices has made the healthcare cyber physical systems (CPSs) ubiquitous and complex. Since IoT devices are heterogeneous, it is crucial to virtualize these devices in a general way so as to orchestrate and allocate their equipped functions and resources in a general manner. Network function virtualization (NFV) is the key networking technology towards the device virtualization. Within NFV, service function chain (SFC) is the crucial issue. While in real networking environment, physical device accidentally fails. How to guarantee the SFC, deployed on top of one or more failed devices, recover to work has not been well addressed yet. Hence, we research this issue, aiming at realizing the secure and sustainable SFC placement and scheduling in an intelligent manner. An intelligent SFC placement and scheduling framework, labeled as <i>Sec-SFC-Intell</i>, is proposed in this paper. When receiving a SFC request, the <i>Sec-SFC-Intell</i> will deploy the SFC on top of the healthcare CPSs. When the failure of certain one physical device is detected, the affected SFC will be re-placed and re-scheduled in order to recover to run and serve. In order to prove the feasibility and highlight the merits of <i>Sec-SFC-Intell</i>, simulation work is conducted. Compared with two compared benchmarks, <i>Sec-SFC-Intell</i> has an apparent SFC acceptance advantage of nearly <inline-formula><tex-math notation="LaTeX">$10\%$</tex-math></inline-formula>.

Reliable Dynamic Service Chain Scheduling in 5G Networks

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

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.

Online and reliable SFC protection scheme of distributed cloud network for future IoT application

The implementation of intelligent device-free sensing (IDFS) necessitates a substantial number of edge IoT devices to extend the service coverage. Prior to deploying these IoT applications on a large scale, a reliable service provision scheme is imperative. In recent years, network function virtualization (NFV) technologies enable fast and convenient service provision by instantiating service function chain (SFC) in the distributed cloud network. However, ensuring resilient service provision is a challenge in NFV environment due to the vulnerability of software-defined network functions. Furthermore, the sequential arrangement of virtual network functions (VNFs) in SFC exacerbates the situation, as the malfunction of any VNFs within the SFC could result in the collapse of the associated service. In this paper, we firstly built a mathematical model for SFC online reliability protection and resource consumption optimization. We then propose a multi-mode VNF backup scheme to guarantee the different reliability requirements for various services. Furthermore, a novel SFC Online Reliability Protection (SORP) scheme is introduced to handle the dynamic SFC requests in distributed cloud network. The simulation results evince the efficacy of the SORP scheme in mitigating service failures and achieving a superior request acceptance ratio, while concurrently reducing average bandwidth consumption, which is particularly noteworthy in high latency scenarios.

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.

An Improved Adaptive Service Function Chain Mapping Method Based on Deep Reinforcement Learning

With the vigorous development of the network functions virtualization (NFV), service function chain (SFC) resource management, which aims to provide users with diversified customized services of network functions, has gradually become a research hotspot. Usually, the network service desired by the user is randomness and timeliness, and the formed service function chain request (SFCR) is dynamic and real-time, which requires that the SFC mapping can be adaptive to satisfy dynamically changing user requests. In this regard, this paper proposes an improved adaptive SFC mapping method based on deep reinforcement learning (ISM-DRL). Firstly, an improved SFC request mapping model is proposed to abstract the SFC mapping process and decompose the SFC mapping problem into the SFCR mapping problem and the VNF reorchestration problem. Secondly, we use the deep deterministic policy gradient (DDPG), which is a deep learning framework, to jointly optimize the effective service cost rate and mapping rate to approximate the optimal mapping strategy for the current network. Then, we design four VNF orchestration strategies based on the VNF request rate and mapping rate, etc., to enhance the matching degree of the ISM-DRL method for different networks. Finally, the results show that the method proposed in this paper can realize SFC mapping processing under dynamic request. Under different experimental conditions, the ISM-DRL method performs better than the DDDPG and DQN methods in terms of average effective cost utilisation and average mapping rate.

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.

Low-Latency Service Chaining With Predefined NSH-Based Multipath Across Multiple Datacenters

Service Function Chaining (SFC) provides a method of forwarding traffic flows through one or more service functions (SFs). For service providers, chaining SFs across multiple datacenters to deliver end-to-end services not only provides better utilization of computing resources of datacenters, but also achieves scalability and fault tolerance. However, most telecommunication applications are sensitive to latency, which tends to degrade due to both virtualization and the long distances among datacenters. In this paper, we extend the network service header (NSH) protocol and propose a multipath chaining with the partially-ordered NSH (MCPON) mechanism to achieve low-latency, partially-ordered service function chaining. MCPON adopts a proactive multipath installation for commonly-used service function paths (SFP, a sequence of requisite SFs) to eliminate reactive path decision delays and to reduce end-to-end service latency. To increase multipath diversity for better load balancing, we modify the original NSH encapsulation design so that the multiple paths selected for an SFP are not limited to having the same execution orders of some non-order-constrained SFs. MCPON also utilizes an entry-saving forwarding table design which enables forwarding entries to be shared among different SFC requests. Our evaluations show that proactive k-path computation for an SFC of length l at a scale of n SFFs saves time complexity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm{ O}}(kl^{3}n^{3})$ </tex-math></inline-formula> , and multipath service chaining reduces latency by 33–68% compared to single-path service chaining in our simulation scenarios.

Scalable and Flexible Traffic Steering for Service Function Chains

Network Function Virtualization (NFV) has inspired numerous orchestration algorithms to decide Virtualized Network Function (VNF) placement and routing paths for service requests with Service Function Chain (SFC) demands. With different optimization goals, these algorithms may select various routing paths for request flows. Nevertheless, existing traffic steering solutions either fail to fully support NFV Orchestrator (NFVO) in path selection, or result in low scalability in the underlay Software Defined Network (SDN). In this paper, we propose STAR to tackle both problems simultaneously. STAR divides the entire routing path for SFC request into several path segments, then performs Output-Port-based, Default-Path-based or RSP-ID-based traffic steering on each path segment. With the idea of path division and these traffic steering mechanisms, STAR achieves flexible traffic steering along any paths selected by NFVO and enables different Rendered Service Paths (RSPs) with the same path segments to share the same forwarding rules. In this way, STAR ensures the correct enforcement of path selection decisions from NFVO and significantly reduces forwarding rule consumption and control overhead. We evaluate STAR with the experiments on a real testbed and large-scale simulations. The results show that our framework is scalable in the SDN data plane and control plane (e.g., reducing rules in hardware switches by more than 70% compared with NSH-based solutions) while retaining the flexibility of steering traffic along any SFC routing paths to provide full support for path decision enforcement (e.g., achieving the highest path decision enforcement ratio in 11 of the 12 cases) with acceptable overhead.

Deterministic Latency/Jitter-Aware Service Function Chaining Over Beyond 5G Edge Fabric

Deterministic Networking (DetNet) has recently attracted much attention. It aims at studying the deterministic bounded latency and low latency variation for time-sensitive applications (e.g., industrial automation). To improve the quality of service (QoS) guarantee and make the network management efficient, it is desirable for Internet Service Provider (ISP) to obtain an optimal service function chain (SFC) provision strategy while providing deterministic service performance for the time-sensitive applications. In this paper, we will study the deterministic SFC lifetime management problem in beyond 5G edge fabric with the objective of maximizing the overall profits and ensuring the deterministic latency and jitter of SFC requests. We first formulate this problem as a mathematical model with the maximal profits for ISP. Then, the novel Deterministic SFC Deployment algorithm (Det-SFCD) and SFC Adjustment algorithm (Det-SFCA) due to traffic load variation are proposed to efficiently solve the SFC lifetime management problem. Extensive simulation results show that our proposed algorithms can achieve better performance in terms of SFC request acceptance rates, overall profits and latency variation compared with the benchmark algorithm.

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.

Distributed Orchestration of Service Function Chains for Edge Intelligence in the Industrial Internet of Things

Network virtualization techniques are promising to overcome the obstacle of applying and expanding costly traditional networks in the industrial Internet of things (IIoT). Artificial intelligence (AI)-enhanced distributed resource management in edge networks has aroused researchers’ widespread attention. However, dynamically arrived service requests and limited edge resources complicate the service scheduling issue. In this article, we establish a dynamic network virtualization technique enabled service function chain (SFC) orchestration framework in IIoT, formulate the joint optimization problem to maximize total utility and decompose it into two subproblems, i.e., SFC selection and dynamic SFC orchestration. A dynamic orchestration of SFC (DOS) scheme, consisting of resource-aware matching algorithm and averaged multistep double deep q-network algorithm, is designed to embed SFC requests distributedly on the optimal virtualized network function chains. At last, we validate the superiority of our proposed DOS scheme by experimental results.

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.

Cloud–Edge Collaborative SFC Mapping for Industrial IoT Using Deep Reinforcement Learning

The industrial Internet of Things (IIoT) and 5G have been served as the key elements to support the reliable and efficient operation of Industry 4.0. By integrating burgeoning network function virtualization (NFV) technology with cloud computing and mobile edge computing, an NFV-enabled cloud–edge collaborative IIoT architecture can efficiently provide flexible service for the massive IIoT traffic in the form of a service function chain (SFC). However, the efficient cloud–edge collaboration, the reasonable comprehensive resource consumption, and different quality of services are still key problems to be solved. Thus, to balance the quality of IIoT services, as well as computational and communicational resource consumption, a multiobjective SFC deployment model is designed to characterize the diverse service requirements and specific network environment for the IIoT. Then, a deep- <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 online SFC deployment algorithm is presented, which can efficiently learn the relationship between the SFC deployment scheme and its performance through the iterative training. Simulation results demonstrate that our proposed approach outperforms others in balancing the resource consumption, accepting more SFC requests, as well as providing differentiated services for delay-sensitive IIoT traffic and resource-intensive IIoT traffic.

Distributed federated service chaining: A scalable and cost-aware approach for multi-domain networks

Future networks are expected to support cross-domain, cost-aware and fine-grained services in an efficient and flexible manner. Service Function Chaining (SFC) has been introduced as a promising approach to deliver these services. In the literature, centralized resource orchestration is usually employed to process SFC requests and manage computing and network resources. However, centralized approaches inhibit the scalability and domain autonomy in multi-domain networks. They also neglect location and hardware dependencies of service chains.In this paper, we propose Distributed Federated Service Chaining (DFSC), a framework for orchestrating and maintaining SFC placement in a distributed fashion while sharing only a minimal amount of domain information and control. First, a deployment cost minimization problem is formulated as an Integer Linear Programming (ILP) problem with fine-grained constraints for location and hardware dependencies. We show that this problem is NP-hard. Then, a placement algorithm is devised to use information only on inter-domain paths and border nodes. Our extensive experimental results demonstrate that DFSC efficiently optimizes the deployment cost, supports domain autonomy and enables faster decision-making. The results also show that DFSC finds solutions within a factor 1.15 of the optimal solution on average. Compared to a centralized approach in the literature, DFSC reduces the deployment cost by up to 20% and uses 70% less decision-making time.

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.