A compressive sensing‐based approach to end‐to‐end network traffic reconstruction utilising partial measured origin‐destination flows. Issue 8 (21st April 2014)

• Record Type: Journal Article
• Authors: Nie, Laisen; Jiang, Dingde; Guo, Lei
• Is Part Of: Transactions on emerging telecommunications technologies. Volume 26:Issue 8(2015)
• Page Start: 1108
• Page End: 1117
• Publication Date: 2014-04-21
• Subjects: Telecommunication -- Periodicals; 384.05
• DOI: 10.1002/ett.2826
• Languages: English
• ISSNs: 2161-5748

Abstract

<abstract abstract-type="main" id="ett2826-abs-0001"> <title>Abstract</title> <p id="ett2826-para-0001">Traffic matrices describe the volume of traffic flowing from origin nodes to destination nodes. As one of the most crucial input parameters for network activities, it is employed to perform a great number of network management tasks. However, traffic matrix estimation is unavailable and has a highly ill‐posed nature, which means that it is significantly difficult to ensure the accuracy for estimating traffic matrices. Thus, it is a challenge to obtain the precise end‐to‐end network traffic. In this paper, we investigate the problem of network traffic estimation and propose a novel compressive sensing‐based approach using partial measured origin‐destination (OD) flows. As a purely data‐driven method, we first propose a strategy to select a small subset of the measured OD flows. We then put forward an optimal greedy adaptive dictionary learning algorithm in order to make the traffic matrix sparse. Furthermore, we model the traffic matrix estimation problem by a fundamental inference problem, which is ill‐posed and obeys the constraints of compressive sensing. In other words, the estimation model is a convex optimisation problem. In the process, we validate the performance of our method by real traffic data. Simulation results indicate that our method can trace the shape of each OD flow much more precisely. Copyright © 2014 John Wiley &amp; Sons, Ltd.</p> </abstract>