Paper Info
Title
Predicting Inter-Data-Center Network Traffic Using Elephant Flow and Sublink Information.
Abstract
With the ever increasing number of large scale Internet applications, inter-data-center (inter-DC) data transfers are becoming more and more common. Traditional inter-DC transfers suffer from both low utilization and congestion, and traffic prediction is an important method to optimize these transfers. Inter-DC traffic is harder to predict than many other types of network traffic because it is dominated by a few large applications. We propose a model that significantly reduces the prediction errors. In our model, we combine wavelet transform with artificial neural network to improve prediction accuracy. Specifically, we explicitly add information of sublink traffic and elephant flows, the least predictable yet dominating traffic in inter-DC network, into our prediction model. To reduce the amount of monitoring overhead for the elephant flow information, we add interpolation to fill in the unknown values in the elephant flows. We demonstrate that we can reduce prediction errors over existing methods by 5% $ \\sim 30$ %. Our prediction is in production as part of the traffic scheduling system at Baidu, one of the largest Internet companies in China, helping to reduce the peak network bandwidth.
Year
DOI
Venue
2016
10.1109/TNSM.2016.2588500
IEEE Trans. Network and Service Management
Keywords
Field
DocType
Predictive models,Wavelet transforms,Internet,Bandwidth,Autoregressive processes
Traffic generation model,Computer science,Computer network,Bandwidth (signal processing),Artificial neural network,Network traffic control,Elephant flow,Data center,The Internet,Wavelet transform
Journal
Volume
Issue
ISSN
13
4
1932-4537
Citations 
PageRank 
References 
3
0.44
0
Authors
6
Name
Order
Citations
PageRank
Yi Li1235.83
Hong Liu230.44
Wenjun Yang3111.63
Dianming Hu4413.04
Xiao-Jing Wang5237159.32
Wei Xu665641.71