Performance bottleneck analysis and resource optimized distribution method for IoT cloud rendering computing system in cyber-enabled applications

• Main Authors: Ronghe Wang, Bo Zhang, Manqing Wu, Jun Zhang, Xiaolei Guo, Xinhai Zhang, Huibo Li, Dong Jiao, Shilong Ma
• Format: Article
• Language: English
• Published: SpringerOpen 2019-03-01
• Series: EURASIP Journal on Wireless Communications and Networking
• Subjects: Mobile network; Big data; Performance bottleneck; Visualization rendering; Performance optimization
• Online Access: http://link.springer.com/article/10.1186/s13638-019-1401-9

Abstract This paper analyzes current cloud computing, cloud rendering industry, and related businesses. In this field, cloud system performance lacks unified evaluation criterion. A novel analysis method and a related measure of cloud rendering system performance are presented in this paper. The main paper investigates the number of system concurrent users and average response delay about user access, average frame speed, system operation speed, and average response about user browsing system. This paper makes a theoretical analysis of a core business process about cloud rendering system, multi-task rendering processes especially. This paper analyzes the efficiency, average frame rate, rendering performance bottleneck of the cloud rendering system, and put forward a unique parameter adjustment strategy to improve system performance, by optimizing related server and rendering machine configuration. This paper puts forward a method to reduce the bottleneck of the system and prevent system performance deterioration in the new scheme. This paper puts forward a set of system optimization strategies to improve system performance. This is a new cloud rendering system performance optimization configuration scheme and optimization strategy.