Browsing by Author "Demirci, Sercan"

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Citation - WoS: 6
Citation - Scopus: 5
Adaptive, Incentive and Scalable Dynamic Tree Overlay for P2p Live Video Streaming
(Springer, 2016) Sayit, Muge; Demirci, Sercan; Kaymak, Yagiz; Tunali, E. Turhan
In this paper, we propose a new multicast tree framework to be used in peer-to-peer (P2P) live video streaming systems. The proposed system, adapts the tree links under high peer churn and runs in a totally distributed manner. In order to provide this dynamism and seamless streaming at the same time, we propose a cross layer design involving scalable video codec, backup parents and hierarchical clusters. The performance of the system is measured in real world environment PlanetLab that has nodes distributed all over the world. The experiments show that the proposed system provides high quality of experience (QoE) in terms of Peak Signal to Noise Ratio (PSNR), playback delay and duration of pauses. The proposed system also provides incentive mechanism to its users.
Citation - WoS: 1
Citation - Scopus: 1
An Efficient Jsd-Based Search on Interest-Based Hierarchical Clustering of Overlay Networks
(Iaria Xps Press, 2010) Bulut, Hasan; Yardimci, Asil; Demirci, Sercan; Kaymak, Yagiz; Fesci-Sayit, Muge; Tunali, E. Turhan
In P2P networks, peers share contents, especially video files, which represent their interests. However, the underlying P2P topology may not represent this interest distribution. Thus, one important aspect of constructing an efficient P2P network is to exploit the interest similarity among peers. In this paper, we propose a hierarchical clustering mechanism for constructing an overlay network that takes interest similarity among peers into account. By measuring the similarity among interests of peers and clusters, interest-based hierarchical clusters are formed by using Jensen-Shannon Divergence metric. The clustering performance metrics, accuracy and correctness, are reported on PlanetLab. For limited keyword collections, a novel Jensen-Shannon Divergence-based search mechanism is implemented. It has been observed that the integrated mechanism provides an efficient method and better performance as compared to classical keyword-based search.
Citation - WoS: 12
Citation - Scopus: 13
A Hierarchical P2p Clustering Framework for Video Streaming Systems
(Elsevier, 2017) Demirci, Sercan; Yardimci, Asil; Sayit, Muge; Tunali, E. Turhan; Bulut, Hasan
In this study, a novel overlay architecture for constructing hierarchical and scalable clustering of Peer-to-Peer (P2P) networks is proposed. The proposed architecture attempts to enhance the clustering of peers by incorporating join, split, merge and cluster leader election mechanisms in a fully distributed manner. It takes delay proximity of peers into account as distance measure. By constructing hierarchical clustering of peers, the control message overhead and maintenance such as host departure/host join overhead are decreased. Theoretical comparisons on overheads of the proposed system with that of other systems from literature are studied. The control mechanism for dynamic peer behavior of the architecture is tested over PlanetLab. The performance metrics used are end-to-end delay, diameter, cluster head distance, occupancy rate, peer join latency, accuracy and correctness. The test results are compared with Hierarchical Ring Tree (HRT) and mOverlay architecture. In addition, a P2P video streaming application is run over the proposed network overlay. Streaming tests show that video streaming applications perform well in terms of received video quality if hierarchical clusters considering delay proximity are used as underlying network architecture. (C) 2016 Elsevier B.V. All rights reserved.