Transforms-Based Bayesian Tensor Completion Method for Network Traffic Measurement Data Recovery

IF 6.7 2区 计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY IEEE Transactions on Network Science and Engineering Pub Date : 2023-03-06 DOI:10.1109/TNSE.2023.3253163
Zecan Yang;Laurence T. Yang;Lingzhi Yi;Xianjun Deng;Chenlu Zhu;Yiheng Ruan
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Abstract

Network traffic measurement is regarded as the bedrock of next-generation network systems. Its purpose is to monitor the network traffic and provide data support for traffic engineering. For this reason, monitoring traffic data from a network-wide perspective is particularly important. However, the proliferation of network services has led to the explosive growth of network traffic, which has brought significant challenges to the measure of network-wide traffic. Therefore, how to infer network-wide traffic from partial traffic data is extremely important. In this article, a transforms-based Bayesian tensor completion (TBTC) method is proposed to infer network traffic data. First, the heterogeneous network traffic data with missing entries are organized into observation tensors according to temporal dimensions and other attributes. Second, the sparse hierarchical prior is used to induce lateral slices sparsity of factor tensors, which makes the tubal rank of the observation tensor can be estimated. Further, a variational Bayesian inference method is developed for model learning, and an efficient updating method is presented. Finally, two cases of the linear transforms-based tensor completion model are implemented in the experiments. Experimental results on two real-world network traffic datasets validate that the proposed method can efficiently and accurately recover network traffic data.
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基于变换的贝叶斯张量补全法用于网络流量测量数据恢复
网络流量测量被视为下一代网络系统的基石。其目的是监控网络流量,为流量工程提供数据支持。因此,从整个网络的角度监控流量数据尤为重要。然而,网络服务的激增导致了网络流量的爆炸式增长,这给全网流量的测量带来了巨大挑战。因此,如何从部分流量数据推断全网流量就显得极为重要。本文提出了一种基于变换的贝叶斯张量补全(TBTC)方法来推断网络流量数据。首先,将条目缺失的异构网络流量数据按照时间维度和其他属性组织成观测张量。其次,利用稀疏层次先验来诱导因子张量的横向切片稀疏性,从而可以估计观测张量的管秩。此外,还开发了一种用于模型学习的变分贝叶斯推理方法,并提出了一种高效的更新方法。最后,在实验中实现了两种基于线性变换的张量补全模型。在两个真实世界网络流量数据集上的实验结果验证了所提出的方法可以高效、准确地恢复网络流量数据。
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来源期刊
IEEE Transactions on Network Science and Engineering
IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering
CiteScore
12.60
自引率
9.10%
发文量
393
期刊介绍: The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.
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