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

• Published in: IEEE transactions on industrial informatics Vol. 12; no. 6; pp. 2166 - 2176
• Main Authors: Zhang, Weishan, Duan, Pengcheng, Gong, Wenjuan, Lu, Qinghua, Yang, Su
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Central processing units; Cloud computing; Computation; CPUs; CPU–GPU collaboration; Graphics processing units; Image processing; Measurement; Monitoring; Performance degradation; Real time; Run time (computers); Storms; Streaming media; Switches; Switching; Time response; Topology; Video; video processing
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2016.2560802

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.