A Load-Aware Pluggable Cloud Framework for Real-Time Video Processing

A large number of video applications require real-time response. The high-speed video processing then requires a distributed and parallelized framework utilizing all possible computing resources, i.e., both Central Processing Unit (CPU) and Graphics Processing Unit (GPU) at their best. The CPU-GPU collaboration may cause resource imbalance where GPU-based jobs consume less computing resources while occupying more memory compared with CPU-based jobs. In this paper, we propose a load-aware pluggable cloud framework for real-time video processing where CPU-GPU switching based on workload status can be performed at runtime. Furthermore, we design aspect-oriented monitors to collect framework metrics and propose a distance coverage algorithm to detect performance degradation in order to make sure that the framework runs optimally to achieve good performance when a load-aware task switching is made. We have comprehensively evaluated the framework and the evaluation results show that the proposed framework has good performance, reusability, pluggability, and scalability.