Service Function Chain Placement Research Articles

Multiagent Deep-Reinforcement-Learning-Based Virtual Resource Allocation Through Network Function Virtualization in Internet of Things

Resource allocation is a significant task in the emerging area of Internet of Things (IoT). IoT devices are usually low-cost devices with limited computational power and capabilities for long term communication. In this article, the network function virtualization (NFV) technique is used to access resources of the network and a reinforcement learning (RL) algorithm is used to solve the problem of resource allocation in IoT networks. The traffic of the IoT network uses the substrate network which is available through NFV for its data transmission. The data transmission needs of the IoT network are translated to virtual requests and service function chain (SFC) are mapped to the substrate network to serve the requests. The problem of SFC placement while meeting the system constraints of the IoT network is a nonconvex problem. In the proposed deep RL (DRL)-based resource allocation, the virtual layer acts as a common repository of the network resources. The optimization problem of SFC placement under the system constraints of IoT networks can be formulated as a Markovian decision process (MDP). The MDP problem is solved through a multiagent DRL algorithm where each agent serves an SFC. Two $Q$ -networks are considered, where one $Q$ -network solves the SFC placement problem while the other updates weights of the $Q$ -network through keeping track of long-term policy changes. The virtual agents serving SFCs interact with the environment, receive reward collectively and update the policy by using the learned experiences. We show that the proposed scheme can solve the optimization problem of SFC placement through adequate reward design, state, and action space formulation. Simulation results demonstrate that the multiagent DRL scheme outperforms the reference schemes in terms of utility gained as measured through different network parameters.

A Reliability-Aware, Delay Guaranteed, and Resource Efficient Placement of Service Function Chains in Softwarized 5G Networks

Network Functions Virtualization (NFV) allows flexibility, scalability, agility, and easy manageability of networks by leveraging the features of virtualization and cloud computing technologies. However, softwarization of network functions imposes many challenges. Reliability and latency are major challenges in NFV-enabled 5G networks that can lead to customer dissatisfaction and revenue loss. In general, redundancy is used to improve the reliability of communication services. However, redundancy requires the same amount of additional resources and thus increases cost. In this work, we address the reliability-aware, delay guaranteed, and resource efficient Service Function Chain (SFC) placement problem in softwarized 5G networks. First, we propose a novel SFC subchaining method to enhance the reliability of an SFC without backups. If reliability requirement is not met after subchaining method, we add backups to VNFs to meet the reliability requirement. Then, we formulate the reliable SFC placement problem as an Integer Linear Programming (ILP) problem in order to solve it optimally. Owing to high computational complexity of the ILP problem for solving large input instances, we propose a modified stable matching algorithm to provide near-optimal solution in polynomial time. By extensive simulations we show that our proposed solutions consume lesser physical resources compared to state-of-the-art solutions for provisioning reliable communication services.

Predicting Dynamic Network State of SFC Operation With Uncertainties

Validation of network state of Network Function Virtualization Infrastructure (NFVI) under various uncertainties are of primary importance as they directly affect Service Function Chain (SFC) provisioning and its performance. SFC provisioning against the uncertainties, including variable traffic demand and failures of link and switch makes validation challenging because the number of scenarios to validate grows exponentially with the number of such failures. This letter takes advantage of a two-stage validation framework and proposes a validation strategy to verify the underlying NFVI network state that supports SFC provisioning despite various uncertainty in the network. We demonstrate the effectiveness of our strategy through different failure and SFC placement scenarios using realistic network topologies and traffic matrix.

Service Function Chain Composition, Placement, and Assignment in Data Centers

With the development of network function virtualization (NFV), service function chains (SFCs) are deployed via virtual network functions (VNFs). In general, the SFCs are served via composition and then deployed into data center infrastructures. However, most of the existing works neglect SFC composition. Furthermore, they consider that VNF instances are independently deployed for each SFC, which may underutilize the computational power of servers. We consider, for each required VNF in the chain, the operator can either place it on a new instance or assign it to an established instance if the residual resource of that instance is sufficient. Such a deployment scheme can leverage resources more efficiently and we define it as SFC placement and assignment. In this paper, we first combine SFC composition, placement and assignment together to enhance resource allocation. We present the system model and formulate the problem as 0–1 integer programming. We aim to improve the VNF instance utilization as well as reduce the link consumption. A heuristic approach called Jcap is developed to solve the problem in two stages. The simulations show that Jcap achieves competitive performance with the optimal results obtained from mathematical model.

Machine learning-driven service function chain placement and scaling in MEC-enabled 5G networks

5G mobile network technology promises to deliver unprecedented ultra-low latency and high data rates, paving the way for many novel applications and services. Network Function Virtualization (NFV) and Multi-access Edge Computing (MEC) are two of the technologies that are expected to play a pivotal role in 5G to achieve ambitious Quality of Service requirements of such applications. While NFV provides flexibility by enabling network functions to be dynamically deployed and inter-connected to realize Service Function Chains (SFC), MEC brings the computing capability to the edges of the mobile network thus reducing latency and alleviating the transport network load. However, adequate mechanisms are needed to meet the dynamically changing network service demands, to optimally utilize the network resources while, at the same time, making sure that the end-to-end latency requirement of services is always satisfied.In this work, we first propose machine learning models, in particular neural-networks, that can perform auto-scaling by predicting the required number of virtual network function instances based on the traffic demand, using the traffic traces collected over a real-operator commercial network. We then employ Integer Linear Programming (ILP) techniques to formulate and solve a joint user association and SFC placement problem, where each SFC represents a service requested by a user with end-to-end latency and data rate requirements. Finally, we propose a heuristic to address the scalability concern of the ILP model.

An Availability-Enhanced Service Function Chain Placement Scheme in Network Function Virtualization

A service function chain (SFC) is an ordered virtual network function (VNF) chain for processing traffic flows to deliver end-to-end network services in a virtual networking environment. A challenging problem for an SFC in this context is to determine where to deploy VNFs and how to route traffic between VNFs of an SFC on a substrate network. In this paper, we formulate an SFC placement problem as an integer linear programing (ILP) model, and propose an availability-enhanced VNF placing scheme based on the layered graphs approach. To improve the availability of SFC deployment, our scheme distributes VNFs of an SFC to multiple substrate nodes to avoid a single point of failure. We conduct numerical analysis and computer simulation to validate the feasibility of our SFC scheme. The results show that the proposed scheme outperforms well in different network scenarios in terms of end-to-end delay of the SFC and computation time cost.

A Mathematical Model and Dynamic Programming Based Scheme for Service Function Chain Placement in NFV

A Mathematical Model and Dynamic Programming Based Scheme for Service Function Chain Placement in NFV

Low Latency Security Function Chain Embedding Across Multiple Domains

5G network is envisioned to provide massive connectivity for a wide range of applications, such as ultra-clear media, internet of vehicles, and smart home. The traditional way of providing security services is difficult to support these new 5G applications flexibly and effectively. In our previous work, we proposed a SFC-based framework that chains security functions in different domains to provide security services on demand. However, creating cross-domain service function chains will inevitably result in the additional network latency. In this paper, we study this problem of minimizing the end-to-end latency when deploying cross-domain service function chains for 5G applications. First, an exact approach, consisting of service chain partition and service subchain embedding, is proposed to derive an optimal solution for cross-domain service function chain placement. Second, we improve the Viterbi algorithm and propose an efficient heuristic approach to derive near-optimal solutions for large networks. We also compare the performance of the proposed exact approach, the proposed heuristic approach, and the simple greedy approach in different scales of network infrastructures. Simulation results are presented to demonstrate the effectiveness of the proposed approaches.