Accurate Measurement Technique of Packet Loss Rate
in Parallel Flow Monitoring
Kohei Watabe
Norinosuke Murai
Shintaro Hirakawa
Kenji Nakagawa
Graduate School of Engineering, Nagaoka University of Technology
Background and Objectives

The Proposed Loss Monitoring Method
Extension for Loss Rate Measurements

End-to-end Measurements

n We extend the method [5] to a loss rate measurement.

n It is important to achieve accurate
measurement without increasing thenumber of probe packets.

1. Samples
are recorded
for all congestion periods.
2. Delay samples are converted
each other with method [5].
3. Samplesare united toxxxxxxxxxxxxxxxx
whendelay samples are converted.

Probe packet

n An active measurement is a common
method to measure end-to-end metrics.

NetworkThe recorded data in sender and receiverare matched to calculate delay/loss.

Parallel Monitoring of Probe Flowsn Most of prior works utilize only one
probe flow for a measurement of one
path in a parallel path monitoring.
n The information concerning a flow canbe utilized supplementary for improving
a measurement of another flow.

n The loss rate on the path of Flow A is estimated by,

Node4Packet injection time
Loss samples
in th congestion period of Flow B,including united loss samples from Flow A

n To provide an unbiased estimator of loss rate on each path, samples
should be weighted.

Link1

Unite loss
samplesn The samples by our method is not uniformly distributed.

n For most measurement applications, multiple paths are monitored inparallel to measure end-to-end metrics.
Flow A
Flow B

Loss samples
in thcongestion period of Flow A

Delay

n End-to-end metrics are fundamental for a network evaluation.

32where5

Flow C
Flow Dfor: The set of all delay samples of flow A.
: The set of all loss samples of flow A.

Objectives: The set of original delay samples in th congestion period of Flow A.
: The set of delay samples in th congestion period of Flow A, including convert samples.

n We have proposed a parallel flow monitoring method for delay [5].

: The set of original loss samples in th congestion period of Flow A.

n The method achieves accurate measurement by utilizing theobservation results of flows sharing the source/destination.

Flow Bn The proposed method recursivelyconverts/unites samples from theFlow D
leaves to the root of the tree.

9.13"$13.03: A virtual delay which is the queueing delay experienced by a virtual packet injected into thepath of Flow A at time .

Conversion Processn We consider that virtual delay processes overlap if the two flows
satisfy the following conditions:
1. The two flows have the same source/destination;
2. The interval between the packet injection/receive times of thefirst/last samples in a congestion period is smaller than ;n To remove inappropriate samples, we utilize a clustering technique inmachine learning.

"%

13.29"&6.89

8.18

"'

10.00

2.77

10.45
9.05

13.42"+"*

17.05

13.85

Internal link

6Flow C54

Recursivelyconvert/unitesamples")The threshold

The tuning parameter"(

6.99
External link

: 0.2 sto define congestion: 0.01 s
in clustering process: 0.1

Simulation time : 1005 s (Data are used from 5 s to 1005 s)
Queue size : 1024 packets

Link capacity: 15.552 Mbps

Flow ID

8-0

Conventional
Proposed(No recursive conversion)
Proposed(Recursive conversion)

4-0

The number ofcaptured loss events

140120
100
80
60
40
200

3-0

8-0

3-02-0Conventional
Proposed(No recursive conversion)
Proposed(Recursive conversion)

0-80.0014
0.00120.001
0.00080.00060.00040.000200-3RMSEn We also evaluate Root Mean Squared Errors (RMSE) when the loss
rate on end-to-end path are measured.

0-4Delayn Queueing delay processes within a congestion period that havecommon links frequently overlap.
n Ifandin a congestion period tightly overlap, information of
the period can be utilized each other.
n To utilize this information, we should discriminate whether processes
overlap.
n The determination should be based on samples.

5Probe packet intervals"#Parallel Flow Monitoring Method for Delay
Overlap of Virtual Delay Processes

62-0Packet injection time

A congestion periodcaused by Link 2 (Overlap) caused by Link 314

n We perform NS-3 simulations to confirm that loss samples of parallel
flows are appropriately united between each other.
n Poisson, Exponential ON/OFF, and Probe traffic stream between allpairs of 9 nodes in a network (i.e., 72 flows stream for each type).

Loss samples in a congestion period
Delay samples

n We assume that links with large
queueing delay, i.e. links with many
packet loss events, are sparse
A congestion periodamong all links in a network.

1Experiments

n Propagation delay can be regarded as a constant.

4-0

n To measure packet delay and loss rate on paths, probe packets are
periodically injected for all or a part of paths.
n A delay/loss sample can be obtained by a probe packet.
n Most of loss events are caused bybuffer overflows in interfaces
placed on links with congestions.

Flow A

0-8

Assumptions and Models

n Trees that represent dependencyof conversions are generated foreach congestion period.

0-4

1. We extend the delay measurement method to a loss measurement.
2. We improve its accuracy by utilizing information of all flows
including flows with different source and destination.

Recursive Conversionn By recursively converting samples obtained from each probe flow, theproposed method utilizes information of all probe flows.

0-3

n In this paper, we propose a parallel flow monitoring for packet loss rate.

: The set of loss samples in th congestion period of Flow A, including united samples.

Flow IDn The proposed method provides 31.3% (No recursive) and 57.5%(Recursive) reduction of RMSE on average.
n The number of captured loss events extremely increases.

Conclusions and Future Worksn We proposed a loss measurement method that fully utilizes flows,including flows with different source and destination in this paper.
n We plan to develop highly accurate delay/loss tomography using theparallel monitoring technique.

References[5] K. Watabe, S. Hirakawa, and K. Nakagawa, “Accurate Delay Measurement for Parallel
Monitoring of Probe Flows,” in Proceedings of 2017 13th International Conference on Network andService Management (CNSM 2017), Tokyo, Japan, 2017.