Research Grants:
1. PI, Low-latency Scheduling Approach for Large-scale Microservices Cluster, National Natural Science Foundation of China (NSFC)
￥300k, 2022.1-2024.12
2. PI, Autoscaling Approach for Large-scale and Co-locatated Cloud-native Applications, NSF of Guangdong Province,China
￥150k, 2024.1-2026.12
3. PI, Management of Large-scale and Co-located Microservice-based Cluster, Shenzhen Science and Technology Program
￥300k, 2024.11-2027.11
4. PI, Delay-aware and Interactive Microservice Application Management, Shenzhen Science and Technology Program
￥300k, 2022.4-2024.4
5. PI for one of the sub research topics about microservice platform management, Key R & D plan of the Ministry of science and technology
￥13 million，I am responsible for ~￥378.7k，2021.1-2023.12
6. PI, Shenzhen Science and Technology Program of understaking the National Key R & D plan of the Program of China
￥380k, 2023.7-2025.7
7. PI, University Lab Open Funding
￥120k, 2025.2-2025.8
8. PI, CAS Young Scientist Salon
￥250k, 2025.2-2025.12
9. PI, SIAT Innovation Program for Excellent Young Researchers, Supported by SIAT-CAS
￥80k, 2020.1-2021.12 (Completion Evaluation as Excellence)
10. Co-PI of CAS side, CAS President's International Fellowship Initiative (PIFI) Group, Supported by CAS
￥3 million, I am responsible for 900k, 2025.1-2027.12
11. PI of CAS side, CAS President's International Fellowship Initiative (PIFI), Supported by CAS
￥100k, 2024-2025
12. PI of CAS side, CAS President's International Fellowship Initiative (PIFI), Supported by CAS
￥100k, 2023-2024
13. PI of CAS side, CAS President's International Fellowship Initiative (PIFI), Supported by CAS
￥270k, 2023-2024
14. PI of CAS side, CAS President's International Fellowship Initiative (PIFI), Supported by CAS
￥180k, 2023-2024
15. PI of CAS side, CAS Special Communication Plan, Supported by CAS
￥100k, 2024-2025
16. Co-PI (with Prof. Huanle Xu at Univeristy of Macau), Collaboration Project of Business Chatarterization and Resilience Scheduling Techniques for Container Instances, Huawei Cloud Computing Technologies Co. Ltd
￥633k, 2022.4-2023.4
17. Co-PI (with Prof. Kejiang Ye at SIAT), Load Balancing in dLORA, Alibaba Innovative Research (AIR) Project
￥500k, 2024.12-2025.12
18. Co-PI (with Prof. Kejiang Ye at SIAT), Fine-grained Resource Characterization of Cloud Native Applications, Alibaba Innovative Research (AIR) Project
￥500k, 2022.1-2022.12
19. Co-PI (with Prof. Wenhong Tian at UESTC), CloudSim Research Project for Siemens MindSphere, Supported by Siemens (Chengdu) Company
￥150k, 2019.9-2019.12
20. Core member of a team for Software Defined Approach and Key Technology of Smart Management of Cloud Data Centers, Key-Area Research and Development Program of Guangdong Province
￥20 million, 2020.1-2022.12
Projects with codes:
Some representatie projects are as below, more can be found at: https://gitee.com/siat-minxian-group/
CoScal: Deep Learning based Approach for Cloud Workloads Prediction
Predicted Alibaba and Google cloud workloads based on supervised learning and gradient recurrent units.
Reinforcement learning based approach for scaling decision, including vertical scaling, horizontal scaling and brownout mechanisms.
Energy Efficient Algorithms based on VM Consolidation Evaluated in CloudSim (CCGRID 2020)
Implemented algorithms published in TPDS, JPDC, TSC, TCC and etc.
BrownoutCon (JSS)
A software prototype system implementing brownout based on containers. The testbed is based is based on Grid'5000. Containers are managed by Docker Swarm. The containers are controlled to save energy consumption.
CloudSimBrownout (TSUSC)
heduling process, we propose FlexCloud, a new flexible and scalable simulator that eBrownout is an approach enables to dynamically deactivate/acticate optional application components to handle overloads. We have implemented brownout techniques in CloudSim to reduce energy consumption in data centers.
FlexCloud
February 2013 to June 2013
Cloud Data centers aim to provide reliable, sustainable and scalable services for different requests. Resource scheduling is a key part for Cloud services. To simplify the scheduling process, we propose FlexCloud, a new flexible and scalable simulator that enables simulating the process of initializing cloud data centers, allocating virtual machine requests, and providing performance evaluation for various scheduling algorithms.