TY - GEN
T1 - Independent counter estimation buckets
AU - Einziger, Gil
AU - Fellman, Benny
AU - Kassner, Yaron
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/8/21
Y1 - 2015/8/21
N2 - Measurement capabilities are essential for a variety of network applications, such as load balancing, routing, fairness and intrusion detection. These capabilities require large counter arrays in order to monitor the traffic of all network flows. While commodity SRAM memories are capable of operating at line speed, they are too small to accommodate large counter arrays. Previous works suggested estimators, which trade precision for reduced space. However, in order to accurately estimate the largest counter, these methods compromise the accuracy of the rest of the counters. In this work we present a closed form representation of the optimal estimation function. We then introduce Independent Counter Estimation Buckets (ICE-Buckets), a novel algorithm that improves estimation accuracy for all counters. This is achieved by separating the flows to buckets and configuring the optimal estimation function according to each bucket's counter scale. We prove an improved upper bound on the relative error and demonstrate an accuracy improvement of up to 57 times on real Internet packet traces.
AB - Measurement capabilities are essential for a variety of network applications, such as load balancing, routing, fairness and intrusion detection. These capabilities require large counter arrays in order to monitor the traffic of all network flows. While commodity SRAM memories are capable of operating at line speed, they are too small to accommodate large counter arrays. Previous works suggested estimators, which trade precision for reduced space. However, in order to accurately estimate the largest counter, these methods compromise the accuracy of the rest of the counters. In this work we present a closed form representation of the optimal estimation function. We then introduce Independent Counter Estimation Buckets (ICE-Buckets), a novel algorithm that improves estimation accuracy for all counters. This is achieved by separating the flows to buckets and configuring the optimal estimation function according to each bucket's counter scale. We prove an improved upper bound on the relative error and demonstrate an accuracy improvement of up to 57 times on real Internet packet traces.
UR - http://www.scopus.com/inward/record.url?scp=84954290366&partnerID=8YFLogxK
U2 - 10.1109/INFOCOM.2015.7218646
DO - 10.1109/INFOCOM.2015.7218646
M3 - Conference contribution
AN - SCOPUS:84954290366
T3 - Proceedings - IEEE INFOCOM
SP - 2560
EP - 2568
BT - 2015 IEEE Conference on Computer Communications, IEEE INFOCOM 2015
PB - Institute of Electrical and Electronics Engineers
T2 - 34th IEEE Annual Conference on Computer Communications and Networks, IEEE INFOCOM 2015
Y2 - 26 April 2015 through 1 May 2015
ER -