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In this paper, we compare real-time and interactive network applications in terms of their ability to handle network errors, i.e., network delay and loss. Our approach is based on session times, which indicate users’ perceptions of the network performance. Using a regression model, we separate the effect of network error on users’ departure decisions from an application’s baseline departure rate, and define an index to quantify an application’s network robustness. We demonstrate the effectiveness of our framework on real-life data traces.
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H H Ch 1 Ch CHung-Hsuan Chen1, Cheng-CM
Hung Hsuan Chen , Cheng CM 1P l i St t U i it 2StM 1Pennsylvania State University, 2StoPennsylvania State University, Sto
LabLab
M ti tiM ti tiM ti tiM ti ti M th d lM th d lM th d lM th d lMotivationMotivationMotivationMotivation MethodologyMethodologyMethodologyMethodologyMotivationMotivationMotivationMotivation MethodologyMethodologyMethodologyMethodologyvvvv gygygygy
A f li i ’ k b E bli h h l i hi bAssessment of applications’ network robustness Establish the relationship betweeAssessment of applications network robustness Establish the relationship betweef h k hNetwork of the network path
Application Delay Loss Rate Network of the network pathApplication Delay Loss Rate Rob stness The departure rate can be dividedpp y Robustness The departure rate can be divided
?? Application specific: “ideal” sce1 Good Poor ?? Application‐specific: “ideal” sce1 Good Poor ?? pp p
2 Poor Good ??2 Poor Good ????
hi h li i h b k b ?Which application has better network robustness?Which application has better network robustness?pp
h ih ih ih iHypothesisHypothesisHypothesisHypothesisHypothesisHypothesisHypothesisHypothesisHypothesisHypothesisHypothesisHypothesisU th b t j d f t k li ti ’ fUsers are the best judge of a network application’s performancej g pp p
Risk score: a magnifier summarRisk score: a magnifier summarimpairment comparable amon
Poor Network Qualityimpairment, comparable amon
Poor Network Quality (Risk score↑ network robus(Risk score ↑ network robus
ffaffectsUnstable Game Play affectsUnstable Game Playf d b
yVerified by Verified by real-life traces real life traces in our previous in our previous Less Fun worksLess Fun works
Sh G Pl TiShorter Game Play TimeShorter Game Play Time
Adopting Gaussian mixture modeAdopting Gaussian‐mixture modep gscenarios (number of mixture comscenarios (number of mixture com(
ContributionContributionContributionContributionContributionContributionContributionContributionContributionContributionContributionContribution
A framework for quantifying an application’s network robustnessA framework for quantifying an application s network robustnessq y g pp
D t ti th ff ti l lif d t tDemonstrating the effectiveness on real‐life data tracesg
Ch T 2 d K T Ch 3Chun Tu2, and Kuan-Ta Chen3Chun Tu , and Kuan Ta Chen B k U i it 3A d i Si iony Brook University 3Academia Sinicaony Brook University Academia Sinica
N t k R b t I dN t k R b t I dN t k R b t I dN t k R b t I dNetwork Robustness IndexNetwork Robustness IndexNetwork Robustness IndexNetwork Robustness IndexNetwork Robustness IndexNetwork Robustness IndexNetwork Robustness IndexNetwork Robustness IndexNNNN1d d h li 1en user departure rates and the quality =1
(NRI)IndexRobustnessNetworken user departure rates and the quality ∫
=)( scorerisk
(NRI) Index Robustness Network∫ )( score risk∫ )(
d into two components:d into two components:1
enario no network impairment1
enario, no network impairment =, pp
∫ ∑ ))exp(( dxdxx∫ ∑β ),...,)exp(( 1 pii dxdxx ∫ ∑β ), ,)p(( 1,...,11
pxx iip
∫ ∑β, ,
11 ip =
Ri k S DiffRi k S DiffRi k S DiffRi k S DiffRisk Score DifferencesRisk Score DifferencesRisk Score DifferencesRisk Score DifferencesRisk Score DifferencesRisk Score DifferencesRisk Score DifferencesRisk Score DifferencesRisk Score DifferencesRisk Score DifferencesRisk Score DifferencesRisk Score Differences
rizing the impact of networkrizing the impact of network g different applicationsg different applications stness↓)stness ↓)
Most users in our traces experienced network scenarios located to theMost users in our traces experienced network scenarios located to the left of the equivalence line where the risk scores of SZ is lower than theleft of the equivalence line where the risk scores of SZ is lower than the hypothetical gamehypothetical game
In most scenarios risk scores of SZ is lower than the hypotheticalIn most scenarios, risk scores of SZ is lower than the hypothetical , ypgame
el to capture the target networkg
el to capture the target network p gmponents 2) K lK lK lK lmponents: 2) Key ResultKey ResultKey ResultKey Resultp ) Key ResultKey ResultKey ResultKey ResultKey ResultKey ResultKey ResultKey Result
fNRI of SZ: 6 58NRI of SZ: 6.58NRI f h h h i l 4 07NRI of the hypothetical game: 4 07NRI of the hypothetical game: 4.07
O SZ’ t k f i b ttOn average SZ’s network performance is betterOn average, SZ s network performance is better
Multimedia Networking and Systems LabMultimedia Networking and Systems Labg yInstit te of Information Science Academia Sinica Institute of Information Science, Academia Sinica Institute of Information Science, Academia Sinica
http://mmnet iis sinica edu twhttp://mmnet.iis.sinica.edu.tw
