Abstract
Elephant flows contribute a large portion of the total traffic volume despite being relatively few in the number of flows. Thus, it is very important to identify these elephant flows in traffic engineering, network operation and management. One can easily keep counters for a few elephant flows using a small amount of fast memory (SRAM). Thus a reasonable goal is to devise an algorithm that identifies elephant flows using a limited SRAM memory. In this paper, we propose a novel approach to tackle this issue by using an identifying Elephant flows based on a Scalable Non-uniform Sampling algorithm (ESNS) to make the monitoring system self-adjustable to the varying monitored traffic. The paper use CERNET traces to compare the performance the ESNS algorithm with the sample and hold algorithm, and sampled algorithm. The experiment shows that the ESNS has better precision than PSH and sampled algorithm under the same flow memory size and configures.
Sign-in/Register to access full text options 
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.
