智能分段路由:在控制开销有限的情况下实现负载平衡

IF 7.7 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Shu Yang;Ruiyu Chen;Laizhong Cui;Xiaolei Chang
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引用次数: 0

摘要

分段路由是近年来交通工程中的一种新架构。然而,分段路由带来了控制开销,即应插入额外的数据包标头。当段头过长时,开销会大大降低大型网络的转发效率。为了实现两个目标中最好的一个,我们提出了用于流量工程的智能路由方案(IRTE),该方案可以在有限的控制开销下实现负载平衡。为了实现最佳性能,我们首先将问题公式化为映射问题,将不同的流量映射到关键的分流点。其次,我们将问题归结为一个k-稠密子图问题,从而证明了该问题是不确定多项式(NP)-困难的。为了解决这个问题,我们开发了一种蚁群优化算法,即改进的蚁群优化算法(IACO),该算法被广泛应用于网络优化问题。我们还设计了具有分流路由的负载平衡算法(LBA-DR),并分析了其理论性能。最后,我们在不同的真实世界拓扑中评估了IRTE,结果表明,IRTE优于传统算法,例如,在BellCanada拓扑上评估时,最大带宽比传统算法低24.6%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intelligent Segment Routing: Toward Load Balancing with Limited Control Overheads
Segment routing has been a novel architecture for traffic engineering in recent years. However, segment routing brings control overheads, i.e., additional packets headers should be inserted. The overheads can greatly reduce the forwarding efficiency for a large network, when segment headers become too long. To achieve the best of two targets, we propose the intelligent routing scheme for traffic engineering (IRTE), which can achieve load balancing with limited control overheads. To achieve optimal performance, we first formulate the problem as a mapping problem that maps different flows to key diversion points. Second, we prove the problem is nondeterministic polynomial (NP)-hard by reducing it to a k-dense subgraph problem. To solve this problem, we develop an ant colony optimization algorithm as improved ant colony optimization (IACO), which is widely used in network optimization problems. We also design the load balancing algorithm with diversion routing (LBA-DR), and analyze its theoretical performance. Finally, we evaluate the IRTE in different real-world topologies, and the results show that the IRTE outperforms traditional algorithms, e.g., the maximum bandwidth is 24.6% lower than that of traditional algorithms when evaluating on BellCanada topology.
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来源期刊
Big Data Mining and Analytics
Big Data Mining and Analytics Computer Science-Computer Science Applications
CiteScore
20.90
自引率
2.20%
发文量
84
期刊介绍: Big Data Mining and Analytics, a publication by Tsinghua University Press, presents groundbreaking research in the field of big data research and its applications. This comprehensive book delves into the exploration and analysis of vast amounts of data from diverse sources to uncover hidden patterns, correlations, insights, and knowledge. Featuring the latest developments, research issues, and solutions, this book offers valuable insights into the world of big data. It provides a deep understanding of data mining techniques, data analytics, and their practical applications. Big Data Mining and Analytics has gained significant recognition and is indexed and abstracted in esteemed platforms such as ESCI, EI, Scopus, DBLP Computer Science, Google Scholar, INSPEC, CSCD, DOAJ, CNKI, and more. With its wealth of information and its ability to transform the way we perceive and utilize data, this book is a must-read for researchers, professionals, and anyone interested in the field of big data analytics.
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