Adaptive contact probing mechanisms for delay tolerant applications
Proceedings of the 13th annual ACM international conference on Mobile computing and networking
Anomaly Characterization in Flow-Based Traffic Time Series
IPOM '08 Proceedings of the 8th IEEE international workshop on IP Operations and Management
Opportunistic energy-efficient contact probing in delay-tolerant applications
IEEE/ACM Transactions on Networking (TON)
An empirical evaluation of short-period prediction performance
SPECTS'09 Proceedings of the 12th international conference on Symposium on Performance Evaluation of Computer & Telecommunication Systems
Analysis of prediction performance of training-based models using real network traffic
International Journal of Computer Applications in Technology
Time-Driven vs packet-driven: a deep study on traffic sampling
ICOIN'06 Proceedings of the 2006 international conference on Information Networking: advances in Data Communications and Wireless Networks
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Techniques for sampling Internet traffic are very important to understand the traffic characteristics of the Internet [14, 8]. In spite of all the research efforts on packet sampling, none has taken into account of self-similarity of Internet traffic in devising sampling strategies. In this paper, we perform an in-depth, analytical study of three sampling techniques for self-similar Internet traffic, namely static systematic sampling, stratified random sampling and simple random sampling. We show that while all three sampling techniques can accurately capture the Hurst parameter (second order statistics) of Internet traffic, they fail to capture the mean (first order statistics) faithfully. We also show that static systematic sampling renders the smallest variation of sampling results in different instances of sampling (i.e., it gives sampling results of high fidelity). Based on an important observation, we then devise a new variation of static systematic sampling, called biased systematic sampling (BSS), that gives much more accurate estimates of the mean, while keeping the sampling overhead low. Both the analysis on the three sampling techniques and the evaluation of BSS are performed on synthetic and real Internet traffic traces. Our performance study shows that BSS gives a performance improvement of 40% and 20% (in terms of efficiency) as compared to static systematic and simple random sampling.