Browsing by Author "Markopoulou, A."

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Dynamic FEC algorithms for TFRC flows
(IEEE, 2010-12) Seferoğlu, H.; Markopoulou, A.; Kozat, Ulaş; Civanlar, Mehmet Reha; Kempf, J.; Computer Science; CİVANLAR, Mehmet Reha
Media flows coexist with TCP-based data traffic on the Internet and are required to be TCP-friendly. The TCP protocol slowly increases its sending rate until episodes of congestion occur, and then it quickly reduces its rate to remove congestion. However, media flows can be sensitive to even brief episodes of congestion. In this paper, we are interested in protecting media flows from TCP-induced congestion while maintaining their TCP friendliness. In particular, we consider media flows carried over the TCP-Friendly Rate Control (TFRC) protocol and we design algorithms that dynamically adapt the level of forward error correction (FEC) based on the congestion state of the network. To this end, first, we investigate the loss and delay characteristics of TFRC flows in several TCP-induced congestion scenarios, and we develop novel predictors of loss events based on packet delay information. Second, we use these predictors to dynamically adapt the level of FEC protection based on the predicted level of congestion. We showthat this technique can significantly improve the overhead versus reliability trade-off compared to fixed FEC. Third, we select the FEC and original media packets within each FEC block, in a rate-distortion optimized way, and we show that this technique significantly improves media quality.
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Minimizing peak load from information cascades: Social networks meet cellular networks
(IEEE, 2016-04) Malandrino, F.; Kurant, M.; Markopoulou, A.; Westphal, C.; Kozat, Ulaş
Online social networks (OSNs) serve today as a platform for information dissemination. At the same time, mobile devices provide ubiquitous network access through the cellular infrastructure. In this paper, we develop mechanisms for minimizing the peak load of the cellular network due to information cascades spreading on social media. First, we exploit the social ties for predicting information dissemination and we propose Proactive Seeding-a technique for minimizing the peak load of cellular networks. Much of such a load is due to information cascades spreading in social media, and we address it by proactively pushing (“seeding”) content to selected users before they actually request it. We develop a family of algorithms that take as input information primarily about: (i) cascades on the OSN, (ii) the background traffic load in the cellular network, and (iii) the local connectivity among mobiles; the algorithms then select which nodes to seed and when. We prove that Proactive Seeding is optimal when the prediction of information cascades is perfect. We perform simulations driven by traces from Twitter and cellular networks and we find that Proactive Seeding reduces the peak cellular load by 20-50 percent. Then, we exploit the fact that there is correlation between social ties and physical proximity and we combine Proactive Seeding with device-to-device communication to further reduce the peak load.