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  <leader>02068nam a22001337a 4500</leader>
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    <subfield code="c">64818</subfield>
    <subfield code="d">64815</subfield>
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    <subfield code="a">R/IMS-19</subfield>
    <subfield code="a">16-MS(CS)-10</subfield>
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  <datafield tag="100" ind1=" " ind2=" ">
    <subfield code="a">Ansari, Abdul Ghani </subfield>
    <subfield code="a">16MSCS10</subfield>
    <subfield code="a">Supervisor - Dr. Fareed Ahmed Jokhio</subfield>
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  <datafield tag="245" ind1=" " ind2=" ">
    <subfield code="a">Storage v/s transcoding cost stategy in clouding computing</subfield>
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    <subfield code="a">Nawab shah:</subfield>
    <subfield code="b">QUEST,</subfield>
    <subfield code="c">2019.</subfield>
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    <subfield code="a">40p</subfield>
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    <subfield code="a">ABSTRACT

In this era the use of video is becoming more common now a day on internet. The videos may be related to	studies, general  knowledge,  news, sports, drama, kids'  activities etc. User wants to watch good quality videos. There are several video formats available; a user device may support only a subset of these formats. User may be connected with low bandwidth network. Hence, providing a good quality video in real time is not possible for each user. Therefore, video transcoding is used to convert the video according to user requirements.  The transcoding  process  is both  compute  intensive  and  data  intensive. Therefore,  it  should  be  performed   at  server  side  in  such  an  environment  where computing resources  can be provided  as per need.  Such environment  can be a cloud computing environment where transcoding can be performed . The transcoded videos can also be stored for subsequent request. In this thesis different transcoding cost models and storage cost models are used. The transcoded video is stored in video repository as long as it is cost efficient. This cost efficiency is based on popularity score of each transcoded video. A video  is popular  if it is being accessed buy  users. If a particular  transcoded video is no more accessed then it is removed from repository to save storage cost. This thesis takes different cost models of amazon cloud for storage as well as for transcoding servers. The results have shown that current storage cost model (US NORTH REGION)  is less expensive and better to use it for a transcoding service were both computing and storage resources as required on large scale.
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    <subfield code="a">Department 0f information technology</subfield>
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    <subfield code="c">THESIS</subfield>
  </datafield>
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    <subfield code="0">0</subfield>
    <subfield code="1">0</subfield>
    <subfield code="4">0</subfield>
    <subfield code="7">0</subfield>
    <subfield code="a">QUESTCL</subfield>
    <subfield code="b">REF</subfield>
    <subfield code="d">2019-07-01</subfield>
    <subfield code="l">0</subfield>
    <subfield code="o">R/IMS-19</subfield>
    <subfield code="p">MP/43-482</subfield>
    <subfield code="r">2019-07-01 00:00:00</subfield>
    <subfield code="w">2019-07-01</subfield>
    <subfield code="y">THESIS</subfield>
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