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A User-Centric Framework for Comparing Applications’ Network Robustness
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A User-Centric Framework for Comparing Applications’ Network Robustness

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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 …

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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  • 1. Hung H Hung-Hsuan Chen1, Cheng-C H Hsuan Ch Cheng C Ch Chun Tu2, and Kuan-Ta Chen3 Ch T d Kuan T Ch K Ta M 1P Pennsylvania State University, 2Stony B l i St t U i it Sto Brook U i k University 3A d it Academia Sinica i Si i Lab Motivation M tiv ti Methodology M th d l gy Network Robustness Index N t kR b t I d 1 Assessment of applications’ network robustness Assessment of applications’ network robustness A f li i k b Establish the relationship betwee user d Establish the relationship b E bli h h l i hi betwee en user departure rates and the quality  en departure rates and the quality d h li Network  Robustness  Index  (NRI)  = Network of the network path of the network path f h k h ∫ (risk  score ) Application pp Delay y Loss Rate The departure rate can be divided into two components: d into two components: d The departure rate can be divided Rob stness Robustness 1 1 Good Poor ? Application‐specific: “ideal” sce p enario, no network impairment enario, no network impairment Application specific: “ideal” sce pp p = p (∫ exp(∑ β i xi ) dx1 ,..., dx p ) p( , , 2 Poor Good ? x1 ,..., x p , , i =1 Which application has better network robustness? Which application h b hi h ppli i has better network robustness? k b ? Risk Score Differences Ri k S Diff Hypothesis h i Users are the best judge of a network application’s performance U th b t j dg f t k ppli ti ’ p f Risk score: a magnifier summar Risk score: a magnifier rizing the impact of network rizing the impact of network  impairment, comparable g different applications  impairment comparable among different applications Poor Network Quality (Risk score ↑  network robus (Risk score ↑ stness ↓) stness ↓) network robus Unstable Game Play y affects ff Verified by f db real life real-life traces in our previous Less Fun works Most users in our traces experienced network scenarios located to the  Most users in our traces experienced network scenarios located to the left of the equivalence line where the risk scores of SZ is lower than the  left of the equivalence line where the risk scores of SZ is lower than the hypothetical game hypothetical game Shorter Game Play Time Sh G Pl Ti In most scenarios, risk scores of SZ is lower than the hypothetical yp In most scenarios, risk scores of SZ is lower than the hypothetical  g game Adopting Gaussian mixture mode to capture the target network Adopting Gaussian‐mixture mode p g el to capture the target network  el p g scenarios (number of mixture comp scenarios ( mponents: 2) mponents 2) (number of mixture com ) Key Result K l Contribution NRI of SZ: 6 58 f NRI of SZ: 6.58 NRI of the hypothetical game: 4.07 NRI of the hypothetical game: 4 07 f h h h i l A framework for quantifying an application s network robustness q y g pp A framework for quantifying an application’s network robustness On average, SZ network performance i b tt On O average SZ’s network performance is better SZ’s t k f is better D t ti g th ff ti l lif d t t Demonstrating the effectiveness on real‐life data traces Multimedia Networking and Systems Lab g y Institute Instit te of Information Science Academia Sinica Science, http://mmnet.iis.sinica.edu.tw http://mmnet iis sinica edu tw

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