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CVS: A CostEfficient and QoSAware Cloud Video Streaming Xiangbo Li, Mohsen AminiS Magdy Bayoumi, Rajkumar Buyya University of Louisiana at LafayeHe, Louisiana The University of Melbourne, Australia 1

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Page 1: CVS:%A%CostEfficientand% QoS*Aware% …streamingsystems.org/Presentations/Xiangbo Li.pdf · Magdy%Bayoumi, Rajkumar%Buyya%% % ... C.%V.%N.%Index,%“Forecastand%methodology,%2014*2019,”%2015.%

CVS:  A  Cost-­‐Efficient  and  QoS-­‐Aware  Cloud  Video  Streaming    

 Xiangbo  Li,  Mohsen  Amini-­‐S    

Magdy  Bayoumi,  Rajkumar  Buyya      

 University  of  Louisiana  at  LafayeHe,  Louisiana  The  University  of  Melbourne,  Australia  

 

 

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Video  Streams   Streaming  Providers   Client  Devices  

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•  Video  streaming  consLtutes    approximately  64%  of  all  the  U.S.  Internet  traffic  in  2014  [1].    •  Cisco  esLmates  that  the    streaming  traffic  will  increase    to  80%  by  2019  [2].  

 

[1]  G.  I.  P.  Report,  “h2ps://www.sandvine.com/trends/global-­‐internet-­‐  phenomena/,”  accessed  Oct.  1,  2015.      [2]  C.  V.  N.  Index,  “Forecast  and  methodology,  2014-­‐2019,”  2015.      

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•  Storage  soluLons  •  Hardware  failover    •  Networking  infrastructure  

•  Video  contents  •  Customer  experience  

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Challenges  v   How  to  improve  clients’  QoS  saLsfacLon?      

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§  Minimize  startup  delay      §  Reduce  presentaLon    deadline  miss  rate  

 

v   How  to  minimize  the  cost  for              streaming  service  providers?  

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Cloud-­‐  based  Video  Streaming  (CVS)  Architecture      

QoS-­‐Aware  Scheduling  Method  

Dynamic  cost-­‐efficient  provisioning  

policy    

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QoS-­‐Aware  Scheduling  Method  

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Step1:  Search  for  the  shortest  compleLon  Lme  VM.  Step2:  Insert  GOP  from  startup  queue  in  front  of  the  GOP  in  the                              batch  queue.    Step3:  Check  if  the  GOP  in  the  batch  queue  will  miss  deadline  or  not.    

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Dynamic  Cost-­‐Efficient  Provisioning  Policy    

I.  Back  to  the  future              

II.  Look  to  the  past  

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v Our  dynamic  system  keeps  the  QoS            violaLon  constantly  low  and  Stable              in  compare  with  staLc  method.    

v Our  method  save  the  cost  when            the  system  is  not  oversubscribed.    

Performance  EvaluaLon  

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Future  Works  

q Machine  Learning-­‐based  Scheduling  on  Heterogeneous  Cluster      

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q Live  Video  Streaming  (e.g  video  conference,  live  broadcasLng)  

q MulLple  Clouds,  uLlize  nearest  cloud  to  gain  beHer  response  Lme      

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QuesLons?  

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Thank  You!