A Study of Deep Learning for Network Traffic Data Forecasting

We present a study of deep learning applied to the domain of network traffic data forecasting. This is a very important ingredient for network traffic engineering, e.g., intelligent routing, which can optimize network performance, especially in large networks. In a nutshell, we wish to predict, in advance, the bit rate for a transmission, based on low-dimensional connection metadata ("flows") that is available whenever a communication is initiated. Our study has several genuinely new points: First, it is performed on a large dataset (~50 million flows), which requires a new training scheme that operates on successive blocks of data since the whole dataset is too large for in-memory processing. Additionally, we are the first to propose and perform a more fine-grained prediction that distinguishes between low, medium and high bit rates instead of just "mice" and "elephant" flows. Lastly, we apply state-of-the-art visualization and clustering techniques to flow data and show that visualizations are insightful despite the heterogeneous and non-metric nature of the data. We developed a processing pipeline to handle the highly non-trivial acquisition process and allow for proper data preprocessing to be able to apply DNNs to network traffic data. We conduct DNN hyper-parameter optimization as well as feature selection experiments, which clearly show that fine-grained network traffic forecasting is feasible, and that domain-dependent data enrichment and augmentation strategies can improve results. An outlook about the fundamental challenges presented by network traffic analysis (high data throughput, unbalanced and dynamic classes, changing statistics, outlier detection) concludes the article.Comment: 16 pages, 12 figures, 28th International Conference on Artificial Neural Networks (ICANN 2019

The WCET Tool Challenge 2011

Following the successful WCET Tool Challenges in 2006 and 2008, the third event in this series was organized in 2011, again with support from the ARTIST DESIGN Network of Excellence. Following the practice established in the previous Challenges, the WCET Tool Challenge 2011 (WCC'11) defined two kinds of problems to be solved by the Challenge participants with their tools, WCET problems, which ask for bounds on the execution time, and flow-analysis problems, which ask for bounds on the number of times certain parts of the code can be executed. The benchmarks to be used in WCC'11 were debie1, PapaBench, and an industrial-strength application from the automotive domain provided by Daimler AG. Two default execution platforms were suggested to the participants, the ARM7 as "simple target'' and the MPC5553/5554 as a "complex target,'' but participants were free to use other platforms as well. Ten tools participated in WCC'11: aiT, Astr\'ee, Bound-T, FORTAS, METAMOC, OTAWA, SWEET, TimeWeaver, TuBound and WCA

WCET Tool Challenge 2011 : Report

Following the successful WCET Tool Challenges in 2006 and 2008, the third event in this series was organized in 2011, again with support from the ARTIST DESIGN Network of Excellence. Following the practice established in the previous Challenges, the WCET Tool Challenge 2011 (WCC’11) defined two kinds of problems to be solved by the Challenge participants with their tools, WCET problems, which ask for bounds on the execution time, and flow-analysis problems, which ask for bounds on the number of times certain parts of the code can be executed. The benchmarks to be used in WCC’11 were debie1, PapaBench, and an industrial-strength application from the automotive domain provided by Daimler. Two default execution platforms were suggested to the participants, the ARM7 as “simple target” and the MPC5553/5554 as a “complex target,” but participants were free to use other platforms as well. Ten tools participated in WCC’11: aiT, Astr´ee, Bound-T, FORTAS, METAMOC, OTAWA, SWEET, TimeWeaver, TuBound and WC