Service-driven dynamic QoS on-demand routing algorithm

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-05-01 Epub Date: 2025-01-15 DOI:10.1016/j.future.2024.107685

Hao She, Lixing Yan, Chuanfeng Mao, Qihui Bu, Yongan Guo

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Abstract

With the proliferation of Internet of Things (IoT) devices, the scale of networks is growing exponentially. However, dynamically meeting the diverse quality of service (QoS) routing requirements for users and services in large-scale networks remains a critical challenge. To address this issue, this paper proposes a Service-Driven Dynamic QoS On-Demand model and establishes a corresponding QoS optimization objective function. The SHA-256 hash algorithm is utilized to simplify the large-scale network model, effectively reducing the number of Segment Routing (SR) nodes. The proposed Service-Driven Dynamic QoS On-Demand Routing Algorithm (SDDRL) identifies the optimal path, which is then uniformly disseminated by the SDN controller, thereby addressing existing challenges in SDN-IoT networks. Compared to OSPF-based and DDQN-based algorithms, the SDDRL algorithm reduces the average delay by 53.85% and 31.63%, respectively. The proposed algorithm reduces the packet loss rate, improves the average network congestion degree and route calculation time compared to other existing algorithms, and it demonstrates superior performance in handling complex tasks.

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服务驱动的动态QoS按需路由算法

随着物联网（IoT）设备的激增，网络规模呈指数级增长。然而，如何动态地满足大规模网络中用户和业务对服务质量（QoS）路由的不同需求仍然是一个严峻的挑战。针对这一问题，本文提出了一种服务驱动的动态QoS按需模型，并建立了相应的QoS优化目标函数。利用SHA-256哈希算法简化大规模网络模型，有效减少SR （Segment Routing）节点数量。提出的服务驱动动态QoS按需路由算法（SDDRL）确定最优路径，然后由SDN控制器统一传播，从而解决SDN- iot网络中存在的挑战。与基于ospf和ddqn的算法相比，SDDRL算法的平均时延分别降低了53.85%和31.63%。与现有算法相比，该算法降低了丢包率，提高了平均网络拥塞程度和路由计算时间，在处理复杂任务时表现出优越的性能。

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来源期刊

Future Generation Computer Systems-The International Journal of Escience 工程技术-计算机：理论方法

CiteScore

19.90

自引率

2.70%

发文量

376

审稿时长

10.6 months

期刊介绍： Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.