DocumentCode :
2784597
Title :
Towards Quality Aware Collaborative Video Analytic Cloud
Author :
Lee, JongHyuk ; Feng, Tao ; Shi, Weidong ; Bedagkar-Gala, Apurva ; Shah, Shishir K. ; Yoshida, Hanako
Author_Institution :
Dept. of Comput. Sci., Univ. of Houston, Houston, TX, USA
fYear :
2012
fDate :
24-29 June 2012
Firstpage :
147
Lastpage :
154
Abstract :
As cloud diversifies into different application fields, understanding and characterizing the specific work load sand application requirements play important roles in the design of efficient cloud infrastructure and system software support. Video analytic is a rapidly advancing field and it is widely used in many application domains (i.e., health, medical care, surveillance, and defense). To support video analytic applications efficiently in cloud, one has to overcome many challenges such as lack of understanding of the relationship and trade off between analytic performance metrics and resource requirements. Furthermore, cloud computing has grown from the early model of resource sharing to data sharing and workflow sharing. To address the challenges and to lever age emerging trends, we propose and experiment with a domain specific cloud environment for video analytic applications. We design a cloud infrastructure framework for sharing video data, analytic software, and workflow. In addition, we create a video analytic quality aware resource plan model to guarantee users QoS and optimize usage of resources based on predictive knowledge of video analytic softwares performance metrics and a resource planning model that optimizes the overall analytic service quality under users constraints (i.e., time and cost).The predictive knowledge is represented as input and analytic software specific predictors. The experimental results show that the video analytic quality aware resource planning model can balance the tradeoff between analytic quality and resource requirements, and achieve optimal or near-optimal planning for video analytic workloads with constraints in a resource shared environment. Simulation studies show that resource planning results using ground truth and video analytic performance predictions are very similar, which indicates that our analytic quality/resource predictors are very accurate.
Keywords :
cloud computing; groupware; knowledge representation; quality of service; resource allocation; software metrics; software performance evaluation; video signal processing; analytic performance metrics; application requirement; cloud computing; cloud environment; cloud infrastructure design; defense; health; medical care; overall analytic service quality optimization; predictive knowledge representation; quality aware collaborative video analytic cloud; resource plan model; resource planning model; resource predictors; resource requirements; resource shared environment; resource sharing; resource usage optimization; surveillance; system software support; user constraint; users QoS guarantee; video analytic application; video analytic software performance metrics; video analytic workload; video data sharing; workflow sharing; Algorithm design and analysis; Analytical models; Measurement; Planning; Prediction algorithms; Software; Streaming media; Cloud Computing; Planning; Quality Prediction; Video Analytic;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cloud Computing (CLOUD), 2012 IEEE 5th International Conference on
Conference_Location :
Honolulu, HI
ISSN :
2159-6182
Print_ISBN :
978-1-4673-2892-0
Type :
conf
DOI :
10.1109/CLOUD.2012.141
Filename :
6253500
Link To Document :
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