Abstract
Traffic monitoring and estimation of flow characteristics, such as the size and duration distributions, can be problematic when the length of an observation window is constrained (e.g., due to hard network resources). Indeed, as shown in this work, sampled flows are usually affected by censoring in an observation window, which leads to biased estimators. To account for censoring, a mathematical framework that describes sampling of flows in a time window is developed. Using censoring analysis, we provide nonparametric maximum likelihood estimators for the flow duration and size distributions. The estimators are computed using the EM algorithm. Finally, the estimators are applied to an actual traffic trace, and are found to perform very well.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
