کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4954430 | 1443319 | 2017 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Accurate traffic matrix completion based on multi-Gaussian models
ترجمه فارسی عنوان
تکمیل ماتریس ترافیک دقیق بر اساس مدل های چند گاوسی
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کلمات کلیدی
اندازه گیری شبکه، ترافیک ماتریس، سنجش فشاری، تکمیل ماتریس، مدل های چند گاوسی،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
شبکه های کامپیوتری و ارتباطات
چکیده انگلیسی
Traffic matrix (TM) describes the volumes of traffic between a set of sources and destinations in a network. As an important parameter, TMs are used in a variety of network engineering tasks, such as traffic engineering, capacity planning and anomaly detection. However, it is a challenge to reliably measure TMs in practice. For example, due to flaws in the measurement systems and possible failure in data collection systems, missing values are unavoidable. It is important to recover the missing data from the partial direct measurements. Existing matrix completion methods do not fully consider network traffic behavior and traffic hidden characteristic. Their completion accuracy tends to be significantly worse when the data loss rate is high. In this paper, we perform a study on intrinsic characteristics of network traffic by analyzing real-world traffic trace data, which reveals that traffic has the features of temporal stability and spatial affinity. According to traffic spatial feature, we model TM as multi-Gaussian distributions, which describes the actual network traffic more accurately. Furthermore, we propose a novel matrix completion method based on multi-Gaussian models to estimate the missing traffic data. Finally, we utilize traffic temporal characteristic to further optimize traffic matrix completion for the missing data interpolation. Our proposed approach has been evaluated utilizing real-world traffic trace data. The extensive experiments demonstrate that our method achieves significantly better performance compared with the state-of-the-art interpolation methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computer Communications - Volume 102, 1 April 2017, Pages 165-176
Journal: Computer Communications - Volume 102, 1 April 2017, Pages 165-176
نویسندگان
Huibin Zhou, Dafang Zhang, Kun Xie,