Virtual Network Function Service Chaining Research Articles

GNN-Based Hierarchical Deep Reinforcement Learning for NFV-Oriented Online Resource Orchestration in Elastic Optical DCIs

Network function virtualization (NFV) in elastic optical datacenter interconnections (EO-DCIs) enables flexible and timely deployment of network services. However, as the service provisioning of virtual network function service chains (vNF-SCs) in an EO-DCI needs to orchestrate the allocations of IT resources in datacenters (DCs) and spectrum resources on fiber links dynamically, it is a complex and challenging problem. In this work, we model the problem as a Markov decision process (MDP), and propose a hierarchical deep reinforcement learning (DRL) model based on graph neural network (GNN), namely, HRLOrch, to tackle it. To ensure its universality and scalability, we design the policy neural network (NN) in HRLOrch based on a GNN. As the GNN-based policy NN can operate on the graph-structured network state of an EO-DCI directly, it can adapt to an arbitrary EO-DCI topology without any structural changes. Then, through analysis, we find that the EO-DCI is a sparse reward environment if we want to train a DRL model to minimize the blocking probability of vNF-SCs in it directly. To address this issue, we design a hierarchical DRL with lower-level and upper-level models to improve the convergence performance of training. Specifically, we make the lower-level DRL optimize the provisioning scheme of each vNF-SC to minimize its resource usage, while the upper-level one coordinates the provisioning of all the active vNF-SCs to minimize the overall blocking probability. Hence, the lower-level and upper-level DRL models operate cooperatively in the training to optimize the dynamic provisioning of vNF-SCs. Our simulations demonstrate the universality and scalability of HRLOrch, and confirm that it can outperform the existing algorithms for vNF-SC provisioning in an EO-DCI.

On establishing and task scheduling of data-oriented vNF-SCs in an optical DCI

The development of network function virtualization (NFV) enables service providers to provision various network services with virtual network function service chains (vNF-SCs). However, most of the existing studies on the service provisioning of vNF-SCs address only the flow-oriented ones, while the provisioning schemes of data-oriented vNF-SCs, each of which needs to process and transfer bulk data through a series of vNFs before a preset deadline, have not been fully explored yet. Therefore, this paper studies how to jointly optimize the establishing and task scheduling of data-oriented vNF-SCs in an optical datacenter interconnection (DCI), such that the probability of scheduling data-oriented vNF-SCs to satisfy their deadlines can be maximized. To make the best use of the resources in the optical DCI for meeting each vNF-SC’s deadline, we leverage dynamic programming (DP) to propose two time-efficient algorithms with deadline-prioritized and conflict-aware approaches. Extensive simulations evaluate the performance of our proposed algorithms in different network scenarios, and confirm their effectiveness. Specifically, compared with a greedy-based benchmark, our DP-based algorithms can reduce the blocking probability of data-oriented vNF-SCs by up to two magnitudes and maintain similar service completion time.

IoT Ecosystem on Exploiting Dynamic VNF Orchestration and Service Chaining: AI to the Rescue?

An efficient automated virtual network function (VNF) deployment and service function chaining (SFC) can induce a significant improvement in the overall performance of various IoT services. Few concerns regarding the latency benefits, energy consumption expenditure, and migration costs are required to be taken into consideration collaboratively for the solution method to accommodate supreme privileges for both users and providers. However, most of the works existing in the literature emphasize these issues exclusively. Additionally, they focus on employing traditional mathematical programming-based approaches to find optimal solutions that are computationally expensive. Thus, state-of-the-art methods are infeasible and not prompt enough to provide real-time solutions for massive IoT services. In this article, we propose the utilization of different deep learning and reinforcement learning techniques (e.g., artificial neural networks, convolutional neural networks, deep Q-networks, and federated learning) for swift VNF orchestration and SFC. Moreover, we identify some challenges and their potential solutions associated with these sophisticated learning models. Then we present some simulation results on a VNF deployment case study demonstrating that deep learning techniques can be a significant breakthrough with promising potential to resolve most of the mentioned concerns incorporated with the VNF orchestration and SFC generation problem.

Application-Driven Provisioning of Service Function Chains Over Heterogeneous NFV Platforms

Although network function virtualization (NFV) has been proven to be beneficial in terms of equipment cost, service delivery flexibility, and time-to-market, most of the studies in this area only addressed homogeneous NFV platforms (e.g., with virtual machines (VMs) only). In this work, we argue that by leveraging heterogeneous NFV platforms such as VMs, docker containers, and programmable hardware accelerators (e.g., SmartNICs), one could achieve better flexibility and cost-effectiveness to support virtual network function service chains (vNF-SCs) with various quality-of-service (QoS) requirements. Therefore, we study application-driven provisioning of vNF-SCs over heterogeneous NFV platforms, and design a polynomial-time approximation algorithm to tackle the problem for near-optimal solutions. We first introduce a layered auxiliary graph (LAG) based approach to model the problem of vNF-SC provisioning, and then formulate a novel integer linear programming (ILP) model based on it. Specifically, the ILP model minimizes the total cost of vNF-SC deployment while ensuring that the QoS requirements of all the vNF-SCs are satisfied. To solve the ILP time-efficiently, we propose an approximation algorithm based on linear programming (LP) relaxation and randomized rounding. Extensive simulations confirm that with significantly improved time-efficiency, our proposed algorithm can provide near-optimal solutions whose gaps to the exact ones are bounded.

Deep-NFVOrch: leveraging deep reinforcement learning to achieve adaptive vNF service chaining in DCI-EONs

Due to the rise of cloud computing, how to realize adaptive and cost-effective virtual network function service chaining (vNF-SC) in a datacenter interconnection based on an elastic optical network (DCI-EON) has become an interesting but challenging problem. In this work, we tackle this problem by optimizing the design of a deep reinforcement learning (DRL)-based adaptive service framework, namely, Deep-NFVOrch. Specifically, Deep-NFVOrch works in service cycles and tries to reduce the setup latency of vNF-SC by invoking request prediction and pre-deployment at the beginning of each service cycle. We introduce a DRL-based observer (DRL-Observer) to select the duration of each service cycle adaptively according to the network status. The DRL-Observer is designed based on the advantage actor critic, which can interact with the network environment constantly through its deep neural network and learn how to make wise decisions based on the environment’s feedback. Our simulation results demonstrate that the DRL-Observer converges fast in online training with the help of a few asynchronous training threads, and the Deep-NFVOrch with it achieves better performance than several benchmarks, in terms of balancing the tradeoffs among overall resource utilization, vNF-SC request-blocking probability, and the number of network reconfigurations in a DCI-EON.

On Incentive-Driven VNF Service Chaining in Inter-Datacenter Elastic Optical Networks: A Hierarchical Game-Theoretic Mechanism

In this paper, we propose an incentive-driven virtual network function service chaining (VNF-SC) framework for optimizing the cross-stratum resource provisioning in multi-broker orchestrated inter-datacenter elastic optical networks (IDC-EONs). The proposed framework employs a non-cooperative hierarchical game-theoretic mechanism, where the resource brokers and the VNF-SC users play the leader and the follower games, respectively. In the leader game, the brokers calculate VNF-SC service schemes for users and compete for the provisioning tasks. While in the follower game, the users compete for VNF-SC services for jointly optimizing the resource cost and the received quality-of-service. We first elaborate on the modeling of the follower game, discuss the existence of Nash equilibrium and propose a mixed-strategy gaming approach enabled by an auxiliary graph-based algorithm to facilitate users selecting the most appropriate service schemes. Then, under the assumption that the brokers are aware of the principle of the follower game, we present the model for the leader game and develop a time-efficient heuristic algorithm for brokers to compete for the provisioning tasks. Simulations show that the proposed incentive-driven VNF-SC framework significantly improves the network throughput (i.e., $>4.8 \times$ blocking reduction) while assisting users and brokers in achieving higher utilities compared with existing solutions.

Optimal VNF Chains Management for Proactive Caching

Notwithstanding the significant attention that network function virtualization architectures received over the last few years, little attention has been placed on cases where proactive caching is considered within a service chain. Caching algorithms have been developed independently from virtual network function (VNF) chaining schemes, and as we explain in detail in this paper such operation might lead to sub-optimal overall network and service performance. Since caching of popular content is envisioned to be one of the key adopted technologies in emerging 5G networks to increase network efficiency and overall end user perceived quality of service, we explicitly consider the interplay and subsequent optimization of caching-based VNF service chains. To this end, a mathematical programming framework is proposed tailored to VNF caching chains and, in addition, we detail a scale-free heuristic algorithm to provide competitive solutions for large network instances since the problem itself can be seen as a variant of the classical NP-hard uncapacitated facility location problem. A wide set of numerical investigations is presented for characterizing the attainable system performance of the proposed schemes.

Leveraging Mixed-Strategy Gaming to Realize Incentive-Driven VNF Service Chain Provisioning in Broker-Based Elastic Optical Inter-Datacenter Networks

This paper investigates the problem of how to optimize the provisioning of virtual network function service chains (VNF-SCs) in elastic optical inter-datacenter networks (EO-IDCNs) under elastic optical networking and DC capacity constraints. We take advantage of the broker-based hierarchical control paradigm for the orchestration of cross-stratum resources and propose to realize incentive-driven VNF-SC provisioning with a noncooperative mixed-strategy gaming approach. The proposed gaming model enables tenants to compete for VNF-SC provisioning services due to revenue and quality-of-service incentives and therefore can motivate more reasonable selections of provisioning schemes. We detail the modeling of the game, discuss the existence of the Nash equilibrium states, and design an auxiliary graph-based heuristic algorithm for tenants to compute approximate equilibrium solutions in the games. A dynamic resource pricing strategy, which can set the prices of network resources in real time according to the actual network status, is also introduced for EO-IDCNs as a complementary method to the game-theoretic approach. Results from extensive simulations that consider both static network planning and dynamic service provisioning scenarios indicate that the proposed game-theoretic approach facilitates both higher tenant and network-wide profits and improves the network throughput as well compared with the baseline algorithms, while the dynamic pricing strategy can further reduce the request blocking probability with a factor of ∼2.4×.

VNF-EQ: dynamic placement of virtual network functions for energy efficiency and QoS guarantee in NFV

With the advances of network function virtualization and cloud computing technologies, a number of network services are implemented across data centers by creating a service chain using different virtual network functions (VNFs) running on virtual machines. Due to the complexity of network infrastructure, creating a service chain requires high operational cost especially in carrier-grade network service providers and supporting stringent QoS requirements from users is also a complicated task. There have been various research efforts to address these problems that only focus on one aspect of optimization goal either from users such as latency minimization and QoS based optimization, or from service providers such as resource optimization and cost minimization. However, meeting the requirements both from users and service providers efficiently is still a challenging issue. This paper proposes a VNF placement algorithm called VNF-EQ that allows users to meet their service latency requirements, while minimizing the energy consumption at the same time. The proposed algorithm is dynamic in a sense that the locations or the service chains of VNFs are reconfigured to minimize the energy consumption when the traffic passing through the chain falls below a pre-defined threshold. We use genetic algorithm to formulate this problem because it is a variation of the multi-constrained path selection problem known as NP-complete. The benchmarking results show that the proposed approach outperforms other heuristic algorithms by as much as 49% and reduces the energy consumptions by rearranging VNFs.

Joint Spectrum and IT Resource Allocation for Efficient VNF Service Chaining in Inter-Datacenter Elastic Optical Networks

We study how to allocate spectrum and IT resources jointly for realizing efficient virtual network function (VNF) service chaining in inter-datacenter elastic optical networks. We first formulate an integer linear programming model to solve the problem exactly, and then a longest common subsequence-based heuristic is proposed. The simulation results indicate that the proposed algorithms can reuse the deployed VNFs efficiently and arrange the spectrum utilization in a much more load-balanced manner.