移动边缘云网络中的可靠性感知业务功能链配置

Shouxu Lin, W. Liang, Jing Li
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引用次数: 11

摘要

移动边缘计算(MEC)一直被设想为一种有前途的技术,以解决移动设备中有限的计算和存储资源。MEC平台提供的虚拟服务是作为虚拟网络功能(virtual Network Functions, vnf)实例实现的。然而,这些VNF实例作为在虚拟机(vm)中运行的软件并不总是可靠的。为了在满足用户业务可靠性要求的同时为用户提供可靠的服务,MEC的服务提供商通常采用副本策略,为每个VNF实例部署一定数量的业务副本。在本文中,我们研究了在MEC网络中通过冗余放置VNFs实例来提供可靠的服务。我们假设每个服务请求由一个服务功能链(SFC)需求和一个服务可靠性需求组成。提出了一种新的基于可靠性感知的业务功能链配置问题,以最大限度地允许请求的数量为目标,同时满足每个允许请求的指定可靠性要求。我们首先证明了问题是np困难的,并在问题规模较小时给出了问题的ILP解。然后,我们开发了一种随机算法,该算法在问题规模较大时具有可证明的近似比和高概率,并且所实现的近似比是以中等计算能力和可靠性约束违反为代价的。我们还设计了一种不违反资源和需求约束的有效启发式方法。最后,我们通过实验模拟来评估所提出算法的性能。实验结果表明,该算法是可行的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reliability-Aware Service Function Chain Provisioning in Mobile Edge-Cloud Networks
Mobile Edge Computing (MEC) has been envisioning as a promising technology to address limited computing and storage resources in mobile devices. The virtual services provided by the MEC platform are implemented as instances of Virtual Network Functions (VNFs). However, these VNF instances as pieces of software that run in virtual machines (VMs) are not always reliable. To provide reliable services for their users while meeting user service reliability requirements, the service providers of MEC usually adopt the replica policy that deploy a certain number of service replicas for each VNF instance. In this paper, we study reliable service provisioning in an MEC network through redundant placement of instances of VNFs. We assume that each service request consists of a Service Function Chain (SFC) requirement and a service reliability requirement. We formulate a novel reliability-aware service function chain provisioning problem with the aim to maximize the number of requests admitted, while meeting the specified reliability requirement of each admitted request. We first show that the problem is NP-hard, and formulate an ILP solution for the problem when the problem size is small. We then develop a randomized algorithm with a provable approximation ratio and high probability for the problem when the problem size is large, and the achieved approximation ratio is at the expense of moderate computing capacity and reliability constraint violations. We also devise an efficient heuristic for the problem without any resource and requirement constraint violations. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising.
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