Abstract
We consider the mean-variance relationship of the number of flows in traffic aggregation, where flows are divided into several groups randomly, based on a predefined flow aggregation index, such as source IP address. We first derive a quadratic relationship between the mean and the variance of the number of flows belonging to a randomly chosen traffic aggregation group. Note here that the result is applicable to sampled flows obtained through packet sampling. We then show that our analytically derived mean-variance relationship fits well those in actual packet trace data sets. Next, we present two applications of the mean-variance relationship to traffic management. One is an application to detecting network anomalies through monitoring a time series of traffic. Using the mean-variance relationship, we determine the traffic aggregation level in traffic monitoring so that it meets two predefined requirements on false positive and false negative ratios simultaneously. The other is an application to load balancing among network equipments that require per-flow management. We utilize the mean-variance relationship for estimating the processing capability required in each network equipment.
| Original language | English |
|---|---|
| Pages (from-to) | 1560-1576 |
| Number of pages | 17 |
| Journal | Computer Networks |
| Volume | 57 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 2013 Apr 22 |
| Externally published | Yes |
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Keywords
- Mean-variance relationship
- Number of sampled flows
- Traffic aggregation
- Traffic measurement
ASJC Scopus subject areas
- Computer Networks and Communications
Cite this
Mean-variance relationship of the number of flows in traffic aggregation and its application to traffic management. / Kawahara, Ryoichi; Takine, Tetsuya; Mori, Tatsuya; Kamiyama, Noriaki; Ishibashi, Keisuke.
In: Computer Networks, Vol. 57, No. 6, 22.04.2013, p. 1560-1576.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Mean-variance relationship of the number of flows in traffic aggregation and its application to traffic management
AU - Kawahara, Ryoichi
AU - Takine, Tetsuya
AU - Mori, Tatsuya
AU - Kamiyama, Noriaki
AU - Ishibashi, Keisuke
PY - 2013/4/22
Y1 - 2013/4/22
N2 - We consider the mean-variance relationship of the number of flows in traffic aggregation, where flows are divided into several groups randomly, based on a predefined flow aggregation index, such as source IP address. We first derive a quadratic relationship between the mean and the variance of the number of flows belonging to a randomly chosen traffic aggregation group. Note here that the result is applicable to sampled flows obtained through packet sampling. We then show that our analytically derived mean-variance relationship fits well those in actual packet trace data sets. Next, we present two applications of the mean-variance relationship to traffic management. One is an application to detecting network anomalies through monitoring a time series of traffic. Using the mean-variance relationship, we determine the traffic aggregation level in traffic monitoring so that it meets two predefined requirements on false positive and false negative ratios simultaneously. The other is an application to load balancing among network equipments that require per-flow management. We utilize the mean-variance relationship for estimating the processing capability required in each network equipment.
AB - We consider the mean-variance relationship of the number of flows in traffic aggregation, where flows are divided into several groups randomly, based on a predefined flow aggregation index, such as source IP address. We first derive a quadratic relationship between the mean and the variance of the number of flows belonging to a randomly chosen traffic aggregation group. Note here that the result is applicable to sampled flows obtained through packet sampling. We then show that our analytically derived mean-variance relationship fits well those in actual packet trace data sets. Next, we present two applications of the mean-variance relationship to traffic management. One is an application to detecting network anomalies through monitoring a time series of traffic. Using the mean-variance relationship, we determine the traffic aggregation level in traffic monitoring so that it meets two predefined requirements on false positive and false negative ratios simultaneously. The other is an application to load balancing among network equipments that require per-flow management. We utilize the mean-variance relationship for estimating the processing capability required in each network equipment.
KW - Mean-variance relationship
KW - Number of sampled flows
KW - Traffic aggregation
KW - Traffic measurement
UR - http://www.scopus.com/inward/record.url?scp=84876132260&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84876132260&partnerID=8YFLogxK
U2 - 10.1016/j.comnet.2013.02.010
DO - 10.1016/j.comnet.2013.02.010
M3 - Article
AN - SCOPUS:84876132260
VL - 57
SP - 1560
EP - 1576
JO - Computer Networks
JF - Computer Networks
SN - 1389-1286
IS - 6
ER -