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Multi-Source Reliable Multicast Routing with QoS Constraints of NFV in Edge Computing

机译:边缘计算中具有NFV QoS约束的多源可靠组播路由

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摘要

Edge Computing (EC) allows processing to take place near the user, hence ensuring scalability and low latency. Network Function Virtualization (NFV) provides the significant convenience of network layout and reduces the service operation cost in EC and data center. Nowadays, the interests of the NFV layout focus on one-to-one communication, which is costly when applied to multicast or group services directly. Furthermore, many artificial intelligence applications and services of cloud and EC are generally communicated through groups and have special Quality of Service (QoS) and reliable requirements. Therefore, we are devoted to the problem of reliable Virtual Network Function (VNF) layout with various deployment costs in multi-source multicast. To guarantee QoS, we take into account the bandwidth, latency, and reliability constraints. Additionally, a heuristic algorithm, named Multi-Source Reliable Multicast Tree Construction (RMTC), is proposed. The algorithm aims to find a common link to place the Service Function Chain (SFC) in the multilevel overlay directed (MOD) network of the original network, so that the deployed SFC can be shared by all users, thereby improving the resource utilization. We then constructed a Steiner tree to find the reliable multicast tree. Two real topologies are used to evaluate the performance of the proposed algorithm. Simulation results indicate that, compared to other heuristic algorithms, our scheme effectively reduces the cost of reliable services and satisfies the QoS requirements.
机译:边缘计算(EC)允许在用户附近进行处理,从而确保可伸缩性和低延迟。网络功能虚拟化(NFV)为网络布局提供了极大的便利,并降低了EC和数据中心的服务运营成本。如今,NFV布局的兴趣集中在一对一通信上,这在直接应用于多播或组服务时非常昂贵。此外,云和EC的许多人工智能应用程序和服务通常通过组进行通信,并且具有特殊的服务质量(QoS)和可靠的要求。因此,我们致力于解决多源多播中具有各种部署成本的可靠虚拟网络功能(VNF)布局问题。为了保证QoS,我们考虑了带宽,延迟和可靠性约束。此外,提出了一种启发式算法,称为多源可靠组播树构造(RMTC)。该算法的目的是找到一个公共链路,将服务功能链(SFC)放置在原始网络的多级覆盖定向(MOD)网络中,以便所有用户都可以共享部署的SFC,从而提高资源利用率。然后,我们构造了Steiner树以找到可靠的多播树。使用两种实际拓扑来评估所提出算法的性能。仿真结果表明,与其他启发式算法相比,我们的方案有效降低了可靠服务的成本并满足了QoS要求。

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