A Machine Learning Approach for Service Function Chain Embedding in Cloud Datacenter Networks

T. Wassing, D. D. Vleeschauwer, C. Papagianni
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引用次数: 2

Abstract

Network Functions Virtualization (NFV) is an industry effort to replace traditional hardware middleboxes with virtualized network functions (VNFs) running on general-build hardware platforms, enabling cost reduction, operational efficiency, and service agility. A Service Function Chain (SFC) constitutes an end-to-end network service, formed by chaining together VNFs in specific order. Infrastructure providers and cloud service providers try to optimally allocate computing and network resources to SFCs, in order to reduce costs and increase profit margins. The corresponding resource allocation problem, known as SFC embedding problem, is proven to be NP-hard.Traditionally the problem has been formulated as Mixed Integer Linear Program (MILP), assuming each SFC’s requirements are known a priori, while the embedding decision is based on a snapshot of the infrastructure’s load at request time. Reinforcement learning (RL) has been recently applied, showing promising results, specifically in dynamic environments, where such assumptions are considered unrealistic. However, standard RL techniques such as Q-learning might not be appropriate for addressing the problem at scale, as they are often ineffective for high-dimensional domains. On the other hand, Deep RL (DRL) algorithms can deal with high dimensional state spaces. In this paper, a Deep Q-Learning (DQL) approach is proposed to address the SFC resource allocation problem. The DQL agent utilizes a neural network for function approximation in Q-learning with experience replay learning. The simulations demonstrate that the new approach outperforms the linear programming approach. In addition, the DQL agent can perform SFC request admission control in real time.
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云数据中心网络中业务功能链嵌入的机器学习方法
网络功能虚拟化(Network Functions Virtualization, NFV)是业界的一项努力,旨在用运行在通用构建硬件平台上的虚拟化网络功能(virtual Network Functions, VNFs)取代传统硬件中间件,从而降低成本、提高运营效率和服务敏捷性。SFC (Service Function Chain)是一个端到端的网络服务,由VNFs按一定的顺序连接在一起形成。基础设施提供商和云服务提供商试图将计算和网络资源优化分配给sfc,以降低成本并提高利润率。相应的资源分配问题,称为SFC嵌入问题,被证明是np困难的。传统上,该问题被表述为混合整数线性规划(MILP),假设每个SFC的需求是先验已知的,而嵌入决策是基于请求时基础设施负载的快照。强化学习(RL)最近得到了应用,显示出有希望的结果,特别是在动态环境中,这种假设被认为是不现实的。然而,标准的强化学习技术(如Q-learning)可能不适合大规模地解决问题,因为它们通常对高维领域无效。另一方面,深度强化学习(DRL)算法可以处理高维状态空间。本文提出了一种深度q -学习(DQL)方法来解决SFC资源分配问题。DQL代理利用神经网络在q学习和经验重放学习中进行函数逼近。仿真结果表明,该方法优于线性规划方法。此外,DQL代理可以实时执行SFC请求准入控制。
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