503,427 research outputs found

    Desain Video on Demand (Vod) Menggunakan High Speed Downlink Packet Access (Hsdpa) Di Wilayah Urban Kota Malang

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    Layanan Video On Demand (VOD) adalah salah satu dari kegiatan streaming. Layanan VOD membutuhkan alokasi bandwidth yang lebih besar daripada aplikasi streaming suara. High Speed Downlink Packet Access (HSDPA) adalah jaringan generasi 3,5G dengan bandwidth 5 MHz dan data rate mencapai 13,6 Mbps. HSDPA secara teoretis mampu memenuhi kebutuhan untuk layanan VOD. Pada penelitian dilakukan desain konfigurasi layanan VOD pada HSDPA dan kajian penerapannya. Parameter yang digunakan untuk menentukan Quality of Service (QoS) layanan VOD pada HSDPA adalah delay end to end, packet loss dan throughput yang dihitung dengan pendekatan teoretis dan pengamatan langsung menggunakan perangkat analisis jaringan (Wireshark). Wireshark adalah perangkat lunak untuk analisis paket jaringan (packet sniffer). Pada penelitian digunakan 3 jenis resolusi video,yakni 144p, 240p, 360p. Kualitas performansi layanan VOD di daerah Rumah Sakit Syaiful Anwar Malang menggunakan High Speed Downlink Packet Access (HSDPA) adalah sesuai dengan standar ITU-T G.1010 untuk delay dan packet loss ratio (PLR), jika menggunakan video dengan resolusi 144p, 240p.Kata Kunci—VOD, HSDPA, QoS, ITU

    Minimizing buffer requirements in video-on-demand servers

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    23rd Euromicro Conference EUROMICRO 97: 'New Frontiers of Information Technology', Budapest, Hungary, 1-4 Sept 1997Memory management is a key issue when designing cost effective video on demand servers. State of the art techniques, like double buffering, allocate buffers in a per stream basis and require huge amounts of memory. We propose a buffering policy, namely Single Pair of Buffers, that dramatically reduces server memory requirements by reserving a pair of buffers per storage device. By considering in detail disk and network interaction, we have also identified the particular conditions under which this policy can be successfully applied to engineer video on demand servers. Reduction factors of two orders of magnitude compared to the double buffering approach can be obtained. Current disk and network parameters make this technique feasible.Publicad

    Efficient memory management in video on demand servers

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    In this article we present, analyse and evaluate a new memory management technique for video-on-demand servers. Our proposal, Memory Reservation Per Storage Device (MRPSD), relies on the allocation of a fixed, small number of memory buffers per storage device. Selecting adequate scheduling algorithms, information storage strategies and admission control mechanisms, we demonstrate that MRPSD is suited for the deterministic service of variable bit rate streams to intolerant clients. MRPSD allows large memory savings compared to traditional memory management techniques, based on the allocation of a certain amount of memory per client served, without a significant performance penaltyPublicad

    Cooperative announcement-based caching for video-on-demand streaming

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    Recently, video-on-demand (VoD) streaming services like Netflix and Hulu have gained a lot of popularity. This has led to a strong increase in bandwidth capacity requirements in the network. To reduce this network load, the design of appropriate caching strategies is of utmost importance. Based on the fact that, typically, a video stream is temporally segmented into smaller chunks that can be accessed and decoded independently, cache replacement strategies have been developed that take advantage of this temporal structure in the video. In this paper, two caching strategies are proposed that additionally take advantage of the phenomenon of binge watching, where users stream multiple consecutive episodes of the same series, reported by recent user behavior studies to become the everyday behavior. Taking into account this information allows us to predict future segment requests, even before the video playout has started. Two strategies are proposed, both with a different level of coordination between the caches in the network. Using a VoD request trace based on binge watching user characteristics, the presented algorithms have been thoroughly evaluated in multiple network topologies with different characteristics, showing their general applicability. It was shown that in a realistic scenario, the proposed election-based caching strategy can outperform the state-of-the-art by 20% in terms of cache hit ratio while using 4% less network bandwidth

    CloudMedia: When cloud on demand meets video on demand

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    Internet-based cloud computing is a new computing paradigm aiming to provide agile and scalable resource access in a utility-like fashion. Other than being an ideal platform for computation-intensive tasks, clouds are believed to be also suitable to support large-scale applications with periods of flash crowds by providing elastic amounts of bandwidth and other resources on the fly. The fundamental question is how to configure the cloud utility to meet the highly dynamic demands of such applications at a modest cost. In this paper, we address this practical issue with solid theoretical analysis and efficient algorithm design using Video on Demand (VoD) as the example application. Having intensive bandwidth and storage demands in real time, VoD applications are purportedly ideal candidates to be supported on a cloud platform, where the on-demand resource supply of the cloud meets the dynamic demands of the VoD applications. We introduce a queueing network based model to characterize the viewing behaviors of users in a multichannel VoD application, and derive the server capacities needed to support smooth playback in the channels for two popular streaming models: client-server and P2P. We then propose a dynamic cloud resource provisioning algorithm which, using the derived capacities and instantaneous network statistics as inputs, can effectively support VoD streaming with low cloud utilization cost. Our analysis and algorithm design are verified and extensively evaluated using large-scale experiments under dynamic realistic settings on a home-built cloud platform. © 2011 IEEE.published_or_final_versionThe 31st International Conference on Distributed Computing Systems (ICDCS 2011), Minneapolis, MN., 20-24 June 2011. In Proceedings of 31st ICDCS, 2011, p. 268-27
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