Efficient and Reliable Application Layer Multicast for Flash Dissemination

Kim, Kyungbaek; Mehrotra, Sharad; Venkatasubramanian, Nalini

doi:10.1109/tpds.2013.280

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…However, BitTorrent is designed to minimize the dissemination time of each peer egoistically. On the other hand, previous studies, including Slurpie [8], Bullet [9], M2M-ALM [10] and ReCREW [11], O-Torrent [12], [13] and [14], have proposed to construct and maintain an overlay network of multiple trees, rings, or a random mesh to deliver content from a single server. In the design of these P2P protocols, content are usually divided into m parts of equal size, each being called a block.…”

Section: Introductionmentioning

confidence: 99%

Time-Critical Data Dissemination Under Flash Crowd Traffic

2022

IEEE Open J. Comput. Soc.

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How to rapidly disseminate a large-sized file to many recipients in flash crowd arrival patterns is a fundamental challenge in many applications, such as distributing multimedia content. To tackle this challenge, we present the Bee , which is a time-critical peer-to-peer data dissemination system aiming at minimizing the maximum dissemination time for all peers to obtain the complete file in flash crowd arrival patterns. Bee is a decentralized system that organizes peers into a randomized mesh-based overlay, and each peer only works with local knowledge. We introduce the slowest peer first strategy to boost the speed of dissemination and present a topology adaptation algorithm that adapts the number of connections based on upload bandwidth capacity of a peer. Bee is designed to support network heterogeneity and deals with the flash crowd arrival pattern without sacrificing the dissemination speed. We also show the lower bound analysis of the data dissemination problem, and present the experimental results to demonstrate that the performance of Bee can roughly approximate the lower bound of the data dissemination problem under flash crowd traffic.

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Section: Introductionmentioning

confidence: 99%

Time-Critical Data Dissemination Under Flash Crowd Traffic

2022

IEEE Open J. Comput. Soc.

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“…Local update strategy means when links with the following characteristics like heavy congestion [8][9], higher bandwidth [10], more stable parent nodes [11][12] and a closer network distance [13][14] are detected, we will start the local updating of the multicast tree, but the local update cannot guarantee a global optimal multicast. Global update means that the overall performance of the multicast tree is unable to meet the needs of business applications, for example the maximum delay exceeds the threshold [15] and the data retransmission rate is high [16] and so on. At this time, the entire multicast tree needs to be reconstructed.…”

Section: Introductionmentioning

confidence: 99%

Overlay Multicast Update Strategy Based on Perturbation Theory

2016

KSII TIIS

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The change of any element in the network is possible to cause performance degradation of the multicast network. So it is necessary to optimize the topology path through the multicast update strategy, which directly affects the performance and user experience of the overlay multicast. In view of the above, a new multicast tree update strategy based on perturbation theory Musp (Multicast Update Strategy based on Perturbation theory) is proposed, which reduces the data transmission interruption caused by the multicast tree update and improves user experiences. According to the multicast tree's elements performance and the topology structure, the Musp strategy defines the multicast metric matrix and based on the matrix perturbation theory it also defines the multicast fluctuation factor. Besides it also demonstrates the calculability of the multicast fluctuation factor presents the steps of the Musp algorithm and calculates the complexity. The experimental results show that compared with other update strategies, as for the sensitivity of the multicast fluctuation factor's energized multicast tree to the network disturbance, the maximum delay of the Musp update strategy is minimal in the case of the local degradation of network performance.

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