Traffic engineering and quality of service in hybrid software defined networks

Samiullah Mehraban, R. K. Yadav
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Abstract

For Future networks, many research projects have proposed different architectures around the globe; Software Defined Network (SDN) architectures, through separating Data and Control Layers, offer a crucial structure for it. With a worldwide view and centralized Control, the SDN network provides flexible and reliable network management that improves network throughput and increases link utilization. In addition, it supports an innovative flow scheduling system to help advance Traffic Engineering (TE). For Medium and large-scale networks migrating directly from a legacy network to an SDN Network seems more complicated & even impossible, as there are High potential challenges, including technical, financial, security, shortage of standards, and quality of service degradation challenges. These challenges cause the birth and pave the ground for Hybrid SDN networks, where SDN devices coexist with traditional network devices. This study explores a Hybrid SDN network's Traffic Engineering and Quality of Services Issues. Quality of service is described by network characteristics such as latency, jitter, loss, bandwidth, and network link utilization, using industry standards and mechanisms in a Hybrid SDN Network. We have organized the related studies in a way that the Quality of Service may gain the most benefit from the concept of Hybrid SDN networks using different algorithms and mechanisms: Deep Reinforcement Learning (DRL), Heuristic algorithm, K path partition algorithm, Genetic algorithm, SOTE algorithm, ROAR method, and Routing Optimization with different optimization mechanisms that help to ensure high-quality performance in a Hybrid SDN Network.
混合软件定义网络中的流量工程和服务质量
对于未来网络,全球许多研究项目都提出了不同的架构;软件定义网络(SDN)架构通过分离数据层和控制层,为其提供了一个重要的结构。通过全球视角和集中控制,SDN 网络可提供灵活可靠的网络管理,从而提高网络吞吐量和链路利用率。此外,它还支持创新的流量调度系统,有助于推进流量工程(TE)。对于中型和大型网络来说,从传统网络直接迁移到 SDN 网络似乎更加复杂,甚至是不可能的,因为存在着很大的潜在挑战,包括技术、资金、安全、标准短缺和服务质量下降等挑战。这些挑战为混合 SDN 网络(SDN 设备与传统网络设备共存)的诞生奠定了基础。本研究探讨了混合 SDN 网络的流量工程和服务质量问题。在混合 SDN 网络中,服务质量由延迟、抖动、损耗、带宽和网络链接利用率等网络特性来描述,并采用行业标准和机制。我们对相关研究进行了整理,以便利用不同的算法和机制,从混合 SDN 网络的概念中获得服务质量的最大收益:深度强化学习(DRL)、启发式算法、K 路径分割算法、遗传算法、SOTE 算法、ROAR 方法和路由优化等不同的优化机制有助于确保混合 SDN 网络的高质量性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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