Hao Xiang
Papers
1
Total Citations
2
H-Index
1
About
Hao Xiang is a researcher whose work centers on the optimization of cloud computing infrastructure, with a particular focus on load balancing and resource allocation in big data environments. His most-cited paper, "Big data cloud platform server load balancing algorithm based on improved chaotic partition algorithm" (2021), addresses a critical challenge in distributed systems: the uneven distribution of tasks across heterogeneous cloud servers. By introducing an improved chaotic partition algorithm, Xiang proposes a method to more evenly distribute computational loads, thereby enhancing system efficiency and preventing server bottlenecks. This work, which has garnered 2 citations, contributes to the broader field of cloud resource management, where efficient load balancing is essential for handling massive, concurrent data processing tasks. Xiang’s research is particularly relevant for students and engineers working on scalable cloud architectures, as it offers a novel approach to a persistent problem in distributed computing. His contributions highlight the ongoing need for intelligent, adaptive algorithms in the era of big data and cloud-native applications.
Research Focus
Key Achievements
Top Papers
- 1