业务功能链,支持NFV的超低时延通信

Mohammad Mohammadi Erbati, M. M. Tajiki, Faramarz Keshvari, Gregor Schiele
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引用次数: 2

摘要

通过利用虚拟化和云计算技术的优势,网络功能虚拟化使网络更加灵活、可管理和可扩展。5G、6G和IoT的超低延迟应用需要非常低的延迟和有保证的QoS。由于网络资源有限,网络提供商必须开发一种有效的策略来支持超低延迟应用程序。本文提出了一种新的服务功能链算法,以最小化延迟和优化超低延迟应用的物理资源分配,同时对其他应用产生最小的负面影响。我们优先考虑超低延迟的流量流,并使它们能够通过使用预留的物理资源(带宽、CPU和内存)来优化其供应路径。我们以整数线性规划优化模型的形式为SFC嵌入问题提供了一个数学模型,该模型考虑了QoS约束(与延迟和链路和服务器的消耗有关)。我们提出了一种启发式算法,以最小的最优性差距和执行时间获得近最优解,使其能够应用于现实世界的网络拓扑结构。性能评估表明,与现有算法相比,我们提出的算法在端到端延迟(高达20%),带宽利用率(高达27%)和SFC接受率(高达10%)方面有效地为超低延迟应用程序提供了更好的结果。
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Service function chaining to support ultra-low latency communication in NFV
By exploiting the benefits of virtualization and cloud computing technologies, Network Function Virtualization enables networks to be more flexible, manageable, and scalable. Ultra-low latency applications in 5G, 6G and IoT demand very low latency and assured QoS. With limited network resources, network providers must develop an effective strategy to support ultra-low latency applications. We propose a novel Service Function Chaining algorithm in this paper with the goal of minimizing latency and optimizing physical resource allocation for ultra-low latency applications while having the minimum possible negative effects on other applications. We prioritize ultra-low latency traffic flows and enable them to optimize their provisioning paths by using reserved physical resources (bandwidth, CPU, and memory). We provide a mathematical model for the SFC embedding problem in the form of an Integer Linear Programming optimization model that takes QoS constraints into account (related to latency and consumption of links and servers). We present a heuristic algorithm for obtaining near-optimal solutions with the smallest possible optimality gap and execution time, allowing it to be applied to real-world network topologies. The performance evaluations show that our proposed algorithms effectively provide better results for ultra-low latency applications in terms of end-to-end delay (up to 20 percent), bandwidth utilization (up to 27 percent) and SFC acceptance rate (up to 10 percent) compared to the existing algorithms.
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