基于灵活资源分配方法的延迟感知VNF放置和链接

Abdelhamid Alleg, T. Ahmed, M. Mosbah, R. Riggio, R. Boutaba
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引用次数: 77

摘要

网络功能虚拟化(NFV)是一项很有前途的技术,受到学术界和工业界的极大关注。NFV模式提出将网络功能(Network Functions, NFs)与专用硬件设备解耦,提供更好的物理资源共享,并为网络运营商提供更大的灵活性。然而,在这种环境下,有效的管理机制对于解决PC-VNF (Virtual Network Functions)的配置和链接问题至关重要。在本文中,我们介绍了一种基于灵活资源分配方法的PC-VNF模型,该模型除了考虑传统的连接性和资源利用率之外,还考虑了延迟方面的服务需求。这对于超可靠、低延迟和大规模机器类型通信等新兴5G服务尤其重要。端到端性能需要满足用户期望和服务需求,以提供所需的QoS/QoE。我们的主要目标是确定最佳的VNF布局,在提供特定延迟(即端到端延迟)的同时最大限度地减少资源消耗,并通过限制分配给给定VNF的资源来达到其所需的性能,从而避免违反服务水平协议(SLA)。结果表明,与传统方法相比,我们的方法实现了所需的延迟和更好的资源利用率,减少了高达40%的资源消耗,并通过恢复15%到60%的被拒绝请求来提高请求接受率。
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Delay-aware VNF placement and chaining based on a flexible resource allocation approach
Network Function Virtualization (NFV) is a promising technology that is receiving significant attention in both academia and the industry. NFV paradigm proposes to decouple Network Functions (NFs) from dedicated hardware equipment, offering a better sharing of physical resources and providing more flexibility to network operators. However, in such environment, efficient management mechanisms are crucial to address the problem of Placement and Chaining of Virtual Network Functions (PC-VNF). In this paper, we introduce a PC-VNF model based on a flexible resource allocation approach that takes into account service requirements in terms of latency, in addition to traditional connectivity and resource utilization. This is particularly important for emerging 5G services such as ultrareliable, low latency and massive machine type communications. The end-to-end performance needs to meet the user expectations as well as service requirements to provide the desired QoS/QoE. Our main goal is to determine the optimal VNF placement minimizing resource consumption while providing specific latency (i.e., end-to-end delay) and avoiding violation of Service Level Agreements (SLA) by constraining allocated resources to a given VNF to reach its required performance. Results show that our approach achieves the required latency with better resources utilization compared to the classical approaches, with a reduction of up to 40% of resource consumption and a higher rate of accepted requests by recovering 15 to 60 % of the rejected requests.
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