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Edge-Assisted Adaptive Configuration for Serverless-Based Video Analytics
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AI
DOI：10.1109/TON.2024.3523956.png)
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Abstract
En 中文
The growth of video volumes and increased DNN capabilities have led to a growing desire for video analytics, which demands intensive computation resources. Traditional resource provisioning strategies, such as configuring a cluster per peak utilization, lead to low resource efficiency. Serverless computing is a promising way to avoid wasteful resource provisioning since video analytics regularly encounters bursty input workloads and fine-grained video content dynamics. For serverless-based video analytics, the application configuration (frame rate, detection model, and computation resources) will impact several metrics, such as computation cost and analytics accuracy. In this paper, we investigate the joint configuration adjustment problem for video knobs and computation resources provided by the serverless platform. We propose an algorithm that can efficiently adapt configurations for video streams to address two key challenges in serverless-based video analytics systems, including the complex relationships between the configurations and the key performance metrics, and the dynamically best configuration. Our adaptive configuration adjustment algorithm is developed based on Markov approximation to minimize the computation cost. To guarantee the accuracy, we then design the keyframe selection algorithm based on the secretary algorithm to identify significant changes in video content. We have developed a prototype over AWS Lambda and conducted extensive experiments with real-world video streams. The results show that our algorithm can greatly reduce the computation cost under the constraint of target accuracy.
Keywords:
Video analytics
edge computing
serverless computing
deep neural network
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