IEICE TRANSACTIONS on Communications
Inferring Link Loss Rates from Unicast-Based End-to-End Measurement
Summary :
In the Internet, because of huge scale and distributed administration, it is of practical importance to infer network-internal characteristics that cannot be measured directly. In this paper, based on a general framework we proposed previously, we present a feasible method of inferring packet loss rates of individual links from end-to-end measurement of unicast probe packets. Compared with methods using multicast probes, unicast-based inference methods are more flexible and widely applicable, whereas they have a problem with imperfect correlation in concurrent events on paths. Our method can infer link loss rates under this problem, and is applicable to various path-topologies including trees, inverse trees and their combinations. We also show simulation results which indicate potential of our unicast-based method.
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- IEICE TRANSACTIONS on Communications Vol.E85-B No.1 pp.70-78
- Publication Date
- 2002/01/01
- Publicized
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- Type of Manuscript
- Special Section PAPER (Special Issue on Internet Technology II -- Traffic Control and Performance Evaluation in the Internet)
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Masato TSURU, Tetsuya TAKINE, Yuji OIE, "Inferring Link Loss Rates from Unicast-Based End-to-End Measurement" in IEICE TRANSACTIONS on Communications,
vol. E85-B, no. 1, pp. 70-78, January 2002, doi: .
Abstract: In the Internet, because of huge scale and distributed administration, it is of practical importance to infer network-internal characteristics that cannot be measured directly. In this paper, based on a general framework we proposed previously, we present a feasible method of inferring packet loss rates of individual links from end-to-end measurement of unicast probe packets. Compared with methods using multicast probes, unicast-based inference methods are more flexible and widely applicable, whereas they have a problem with imperfect correlation in concurrent events on paths. Our method can infer link loss rates under this problem, and is applicable to various path-topologies including trees, inverse trees and their combinations. We also show simulation results which indicate potential of our unicast-based method.
URL: https://global.ieice.org/en_transactions/communications/10.1587/e85-b_1_70/_p
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@ARTICLE{e85-b_1_70,
author={Masato TSURU, Tetsuya TAKINE, Yuji OIE, },
journal={IEICE TRANSACTIONS on Communications},
title={Inferring Link Loss Rates from Unicast-Based End-to-End Measurement},
year={2002},
volume={E85-B},
number={1},
pages={70-78},
abstract={In the Internet, because of huge scale and distributed administration, it is of practical importance to infer network-internal characteristics that cannot be measured directly. In this paper, based on a general framework we proposed previously, we present a feasible method of inferring packet loss rates of individual links from end-to-end measurement of unicast probe packets. Compared with methods using multicast probes, unicast-based inference methods are more flexible and widely applicable, whereas they have a problem with imperfect correlation in concurrent events on paths. Our method can infer link loss rates under this problem, and is applicable to various path-topologies including trees, inverse trees and their combinations. We also show simulation results which indicate potential of our unicast-based method.},
keywords={},
doi={},
ISSN={},
month={January},}
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TY - JOUR
TI - Inferring Link Loss Rates from Unicast-Based End-to-End Measurement
T2 - IEICE TRANSACTIONS on Communications
SP - 70
EP - 78
AU - Masato TSURU
AU - Tetsuya TAKINE
AU - Yuji OIE
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Communications
SN -
VL - E85-B
IS - 1
JA - IEICE TRANSACTIONS on Communications
Y1 - January 2002
AB - In the Internet, because of huge scale and distributed administration, it is of practical importance to infer network-internal characteristics that cannot be measured directly. In this paper, based on a general framework we proposed previously, we present a feasible method of inferring packet loss rates of individual links from end-to-end measurement of unicast probe packets. Compared with methods using multicast probes, unicast-based inference methods are more flexible and widely applicable, whereas they have a problem with imperfect correlation in concurrent events on paths. Our method can infer link loss rates under this problem, and is applicable to various path-topologies including trees, inverse trees and their combinations. We also show simulation results which indicate potential of our unicast-based method.
ER -
English
