IEICE TRANSACTIONS on Communications
Effective Flow Aggregation for Traffic Engineering
Summary :
Although the use of software-defined networking (SDN) enables routes of packets to be controlled with finer granularity (down to the individual flow level) by using traffic engineering (TE) and thereby enables better balancing of the link loads, the corresponding increase in the number of states that need to be managed at routers and controller is problematic in large-scale networks. Aggregating flows into macro flows and assigning routes by macro flow should be an effective approach to solving this problem. However, when macro flows are constructed as TE targets, variations of traffic rates in each macro flow should be minimized to improve route stability. We propose two methods for generating macro flows: one is based on a greedy algorithm that minimizes the variation in rates, and the other clusters micro flows with similar traffic variation patterns into groups and optimizes the traffic ratio of extracted from each cluster to aggregate into each macro flow. Evaluation using traffic demand matrixes for 48 hours of Internet2 traffic demonstrated that the proposed methods can reduce the number of TE targets to about 1/50 ∼ 1/400 without degrading the link-load balancing effect of TE.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E98-B No.10 pp.2049-2059
- Publication Date
- 2015/10/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.E98.B.2049
- Type of Manuscript
- PAPER
- Category
- Network
Authors
Noriaki KAMIYAMA
Osaka University,NTT Corporation
Yousuke TAKAHASHI
NTT Corporation
Keisuke ISHIBASHI
NTT Corporation
Kohei SHIOMOTO
NTT Corporation
Tatsuya OTOSHI
Osaka University
Yuichi OHSITA
Osaka University
Masayuki MURATA
Osaka University
Keyword
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Noriaki KAMIYAMA, Yousuke TAKAHASHI, Keisuke ISHIBASHI, Kohei SHIOMOTO, Tatsuya OTOSHI, Yuichi OHSITA, Masayuki MURATA, "Effective Flow Aggregation for Traffic Engineering" in IEICE TRANSACTIONS on Communications,
vol. E98-B, no. 10, pp. 2049-2059, October 2015, doi: 10.1587/transcom.E98.B.2049.
Abstract: Although the use of software-defined networking (SDN) enables routes of packets to be controlled with finer granularity (down to the individual flow level) by using traffic engineering (TE) and thereby enables better balancing of the link loads, the corresponding increase in the number of states that need to be managed at routers and controller is problematic in large-scale networks. Aggregating flows into macro flows and assigning routes by macro flow should be an effective approach to solving this problem. However, when macro flows are constructed as TE targets, variations of traffic rates in each macro flow should be minimized to improve route stability. We propose two methods for generating macro flows: one is based on a greedy algorithm that minimizes the variation in rates, and the other clusters micro flows with similar traffic variation patterns into groups and optimizes the traffic ratio of extracted from each cluster to aggregate into each macro flow. Evaluation using traffic demand matrixes for 48 hours of Internet2 traffic demonstrated that the proposed methods can reduce the number of TE targets to about 1/50 ∼ 1/400 without degrading the link-load balancing effect of TE.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E98.B.2049/_p
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@ARTICLE{e98-b_10_2049,
author={Noriaki KAMIYAMA, Yousuke TAKAHASHI, Keisuke ISHIBASHI, Kohei SHIOMOTO, Tatsuya OTOSHI, Yuichi OHSITA, Masayuki MURATA, },
journal={IEICE TRANSACTIONS on Communications},
title={Effective Flow Aggregation for Traffic Engineering},
year={2015},
volume={E98-B},
number={10},
pages={2049-2059},
abstract={Although the use of software-defined networking (SDN) enables routes of packets to be controlled with finer granularity (down to the individual flow level) by using traffic engineering (TE) and thereby enables better balancing of the link loads, the corresponding increase in the number of states that need to be managed at routers and controller is problematic in large-scale networks. Aggregating flows into macro flows and assigning routes by macro flow should be an effective approach to solving this problem. However, when macro flows are constructed as TE targets, variations of traffic rates in each macro flow should be minimized to improve route stability. We propose two methods for generating macro flows: one is based on a greedy algorithm that minimizes the variation in rates, and the other clusters micro flows with similar traffic variation patterns into groups and optimizes the traffic ratio of extracted from each cluster to aggregate into each macro flow. Evaluation using traffic demand matrixes for 48 hours of Internet2 traffic demonstrated that the proposed methods can reduce the number of TE targets to about 1/50 ∼ 1/400 without degrading the link-load balancing effect of TE.},
keywords={},
doi={10.1587/transcom.E98.B.2049},
ISSN={1745-1345},
month={October},}
Copy
TY - JOUR
TI - Effective Flow Aggregation for Traffic Engineering
T2 - IEICE TRANSACTIONS on Communications
SP - 2049
EP - 2059
AU - Noriaki KAMIYAMA
AU - Yousuke TAKAHASHI
AU - Keisuke ISHIBASHI
AU - Kohei SHIOMOTO
AU - Tatsuya OTOSHI
AU - Yuichi OHSITA
AU - Masayuki MURATA
PY - 2015
DO - 10.1587/transcom.E98.B.2049
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E98-B
IS - 10
JA - IEICE TRANSACTIONS on Communications
Y1 - October 2015
AB - Although the use of software-defined networking (SDN) enables routes of packets to be controlled with finer granularity (down to the individual flow level) by using traffic engineering (TE) and thereby enables better balancing of the link loads, the corresponding increase in the number of states that need to be managed at routers and controller is problematic in large-scale networks. Aggregating flows into macro flows and assigning routes by macro flow should be an effective approach to solving this problem. However, when macro flows are constructed as TE targets, variations of traffic rates in each macro flow should be minimized to improve route stability. We propose two methods for generating macro flows: one is based on a greedy algorithm that minimizes the variation in rates, and the other clusters micro flows with similar traffic variation patterns into groups and optimizes the traffic ratio of extracted from each cluster to aggregate into each macro flow. Evaluation using traffic demand matrixes for 48 hours of Internet2 traffic demonstrated that the proposed methods can reduce the number of TE targets to about 1/50 ∼ 1/400 without degrading the link-load balancing effect of TE.
ER -
English
