Srinilta, Chutimet

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Srinilta, Chutimet
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Srinilta, C.
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chutimet.sr@kmitl.ac.th

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    Item type:Publication,
    PP caching: Proxy caching mechanism for YouTube videos in campus network
    (2011-07-26)
    Suranuntakul, Varangpa
    ;
    Srinilta, Chutimet
    Nowadays, YouTube has become a very popular website offering video sharing service. YouTube accounts for a significant portion of Internet traffic in global and local networks. Many techniques have been implemented to deliver videos smoothly to every user. Streaming proxy servers are put into action to reduce user-perceived latency as well as network resource utilization. "What is viewed next" can be guessed from relationships of YouTube videos. This paper proposes PP caching mechanism that benefits from prefix caching and prefetching technique. Certain parts of selected videos are prefetched and kept at the proxy cache. The experimental results show that startup delay decreases and cache hit rate increases when the proposed mechanism is tested against actual YouTube traces from two campuses.
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    Data placement in heterogeneous storage of short videos
    (2009-12-01)
    Jaipahkdee, W.
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    Srinilta, C.
    The overall service performance of I/O intensive system depends mainly on workload on its storage system. In heterogeneous storage environment where storage elements from different vendors with different capacity and performance are put together, workload should be distributed according to storage capability. This paper addresses data placement issue in short video sharing website. Workload contributed by a video is estimated by the number of views and life time span of existing videos in same category. Experiment was conducted on 42,000 video titles in six weeks. Result showed that the proposed algorithm distributed workload and maintained balance better than round robin and random algorithms.
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    Item type:Publication,
    Dynamic load balancing of short videos in heterogeneous storage environment
    (2010-12-01)
    Jaiphakdee, W.
    ;
    Srinilta, C.
    Performance of storage system has great impact on overall performance of I/O intensive systems. Data distribution across storage devices affects storage performance directly. Data migration can degrade system performance. This paper focuses on data placement issue in heterogeneous tiers of storage devices storing YouTube videos. In addition to storage capability and current workload, our data placement algorithm also takes into account future workload. Future workload is estimated from video's characteristics. Workload in all storage tiers is dynamically adjusted to achieve balance at all times. Four data placement algorithms were used to distribute 16,314 videos across 3-tier storage system in the experiment. The proposed algorithm resulted in a more balance workload distribution compared to round robin and random algorithms.