Towards Quality Aware Collaborative Video Analytic Cloud
Jonghyuk Lee, Tao Feng, Weidong Larry Shi, Apurva Bedagkar-Gala, Shishir K. Shah, Hanako Yoshida · 2012
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.