TY - GEN
T1 - Optimal deployment of distributed passive measurement monitors
AU - Chengchen, Hu
AU - Bin, Liu
AU - Zhen, Liu
AU - Shifang, Gao
AU - Dapeng, Wu
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2006
Y1 - 2006
N2 - Flow-level traffic measurement is important for network management. The widely used centralized per-flow measurement faces a great challenge due to the demanding requirement on both memory bandwidth and memory size within a single traffic monitor. This paper addresses the issue of deploying a Distributed Passive Measurement System (DPMS) in a large scale network; specifically, we study how to optimally place traffic monitors and sample stochastic traffic flows, so that the probability of a packet being sampled (a.k.a. measurement coverage) is maximized. We formulate this problem as a Stochastic Chance Constrained Optimization (SCCO) problem; and we propose a Hybrid Intelligent (HI) algorithm to solve this problem. The HI algorithm consists of two major components, namely, uncertain function approximation and genetic algorithm. Equipped with the HI algorithm, we are able to address the optimal tradeoff between measurement coverage and deployment cost for networks with random traffic, which has not been studied before. Our simulations and experiments demonstrate the effectiveness of our algorithm, i.e., a small deployment cost or a small number of monitors are sufficient to maintain a high level of measurement coverage. © 2006 IEEE.
AB - Flow-level traffic measurement is important for network management. The widely used centralized per-flow measurement faces a great challenge due to the demanding requirement on both memory bandwidth and memory size within a single traffic monitor. This paper addresses the issue of deploying a Distributed Passive Measurement System (DPMS) in a large scale network; specifically, we study how to optimally place traffic monitors and sample stochastic traffic flows, so that the probability of a packet being sampled (a.k.a. measurement coverage) is maximized. We formulate this problem as a Stochastic Chance Constrained Optimization (SCCO) problem; and we propose a Hybrid Intelligent (HI) algorithm to solve this problem. The HI algorithm consists of two major components, namely, uncertain function approximation and genetic algorithm. Equipped with the HI algorithm, we are able to address the optimal tradeoff between measurement coverage and deployment cost for networks with random traffic, which has not been studied before. Our simulations and experiments demonstrate the effectiveness of our algorithm, i.e., a small deployment cost or a small number of monitors are sufficient to maintain a high level of measurement coverage. © 2006 IEEE.
KW - Distributed monitoring
KW - Genetic algorithm
KW - Passive measurement
KW - Stochastic chance constrained optimization
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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-42549135641&origin=recordpage
U2 - 10.1109/ICC.2006.254776
DO - 10.1109/ICC.2006.254776
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 1424403553
SN - 9781424403554
VL - 2
T3 - IEEE International Conference on Communications
SP - 621
EP - 626
BT - 2006 IEEE International Conference on Communications, ICC 2006
T2 - 2006 IEEE International Conference on Communications, ICC 2006
Y2 - 11 July 2006 through 15 July 2006
ER -