Adaptive Sampling for Network Management
Journal of Network and Systems Management
Improving accuracy in end-to-end packet loss measurement
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
Observations on Cisco sampled NetFlow
ACM SIGMETRICS Performance Evaluation Review - Special issue on the First ACM SIGMETRICS Workshop on Large Scale Network Inference (LSNI 2005)
Cybernetics and Systems Analysis
A Modified FLC Adaptive Sampling Method
CMC '09 Proceedings of the 2009 WRI International Conference on Communications and Mobile Computing - Volume 02
Empirical Evaluation of Hash Functions for PacketID Generation in Sampled Multipoint Measurements
PAM '09 Proceedings of the 10th International Conference on Passive and Active Network Measurement
Packet sampling for flow accounting: challenges and limitations
PAM'08 Proceedings of the 9th international conference on Passive and active network measurement
Journal of Network and Computer Applications
A Traffic Prediction Based Bandwidth Management Algorithm of a Future Internet Architecture
ICINIS '10 Proceedings of the 2010 Third International Conference on Intelligent Networks and Intelligent Systems
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The deployment of efficient measurement solutions to assist network management tasks without interfering with normal network operation assumes a prominent role in today's high-speed networks attending to the huge amounts of traffic involved. From a myriad of proposals for traffic measurement, sampling techniques are particularly relevant contributing effectively for this purpose as only a subset of the overall traffic volume is handled for processing, preserving ideally the correct estimation of network statistical behavior. In this context, this paper proposes MuST - a multiadaptive sampling technique based on linear prediction, aiming at reducing significantly the measurement overhead and still assuring that traffic samples reflect the statistical characteristics of the global network traffic under analysis. Conversely to current sampling techniques, MuST is a multi and self-adaptive technique as both the sample size and interval between samples are self-adjustable parameters according to the ongoing network activity and the accuracy of prediction achieved. The tests carried out demonstrate that the proposed sampling technique is able to achieve accurate network estimations with reduced overhead, using throughput as reference parameter. The evaluation results, obtained resorting to real traffic traces representing wired and wireless aggregated traffic scenarios and actual network services, prove that the simplicity, flexibility and self-adaptability of the proposed technique can be successfully explored to improve network measurements efficiency over distinct traffic conditions. For optimization purposes, this paper also includes a study of the impact of varying the order of prediction, i.e., of considering different degrees of past memory in the self-adaptive estimation mechanism. The significance of the obtained results is demonstrated through statistical benchmarking.