A research team led by Xu Cheng-Zhong, chair professor in the Faculty of Science and Technology (FST) of the University of Macau (UM), and Xu Huanle, assistant professor in FST, have made significant breakthroughs in the field of cloud computing. The team has designed an innovative resource management system that can improve the efficiency of computing resource use and reduce CPU resource use by nearly 1.6 times. The research results have been published in ACM Transactions on Computer Systems (ToCS), a leading computer science journal.
The artificial intelligence sector is undergoing rapid technological change. With a soaring demand for computing resources, optimising the efficiency of computing resources and accommodating larger computational workloads have become challenges for the cloud computing industry. To improve efficiency in the use of computing resources, the team has designed a new resource management system called Erms. The system is capable of dynamically deploying resources and optimising resource scaling based on actual workloads, and, for the first time, achieving optimal resource management in large-scale, complex microservice scenarios. In addition, the team has designed a new set of scheduling strategy to optimise resource allocation for shared microservices, which significantly improves the efficiency of resource utilisation. Compared with existing microservice systems, Erms can reduce the likelihood of SLA (service-level agreement) violations to one-fifth of the original risk, and achieve nearly 1.6 times savings in CPU resources.
Titled ‘Optimizing Resource Management for Shared Microservices: A Scalable System Design’, the paper is the first study to fully address microservice multiplexing scenarios. It has been published in ACM Transactions on Computer Systems (ToCS). The journal enjoys a high reputation in the field of computer system and has published many significant research results on computer operating systems, networks, databases, and distributed systems. Since its inception 40 years ago, the journal has included only a limited number of papers from China, and the abovementioned paper is the first contribution from the Guangdong-Hong Kong-Macao Greater Bay Area.
Prof Xu Cheng-Zhong and Prof Xu Huanle are the corresponding authors of the study. Luo Shutian, a doctoral graduate co-trained by UM and the Chinese Academy of Sciences (now a postdoctoral fellow at Yale University), is the first author. The research project was supported by the Science and Technology Development Fund of the Macao SAR (File no: 0024/2022/A1), the Key Research and Development Program of the Ministry of Science and Technology (File no: 2019YFB2102100), and the Guangdong Province Key Research and Development Program (File no: 2020B010164003). The full version of the research article can be viewed at https://doi.org/10.1145/3631607.
Furthermore, Prof Xu Cheng-Zhong’s team was awarded the sole Best Paper award at the ACM Symposium on Cloud Computing 2021 for their paper titled ‘Characterizing Microservice Dependency and Performance: Alibaba Trace Analysis’. This marks the first instance of a scholar from China (including Hong Kong, Macao, and Taiwan) receiving the award since the conference’s establishment in 2009.
Both of the abovementioned papers are the results of the team’s collaboration with the Shenzhen Institute of Advanced Technology under the Chinese Academy of Sciences and Alibaba, an international and leading cloud computing company. The team has been funded by the Alibaba Innovative Research Program for five consecutive years and received the Alibaba Outstanding Collaborative Project Award in 2022. Mei Hong, chairman of the China Computer Federation and director of the Academic Committee of the State Key Laboratory of Internet of Things for Smart City, said that cloud computing plays a key role in the wave of smart cities and artificial intelligence models, adding that the publication of the two important papers indicates that UM’s research in the field of cloud computing has achieved international standing.|
Source: Faculty of Science and Technology |
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