285 citations · 652 across the 11 of their papers we have counts for
11 papers
EsDNN: Deep Neural Network based Multivariate Workload Prediction Approach in Cloud Environment
Minxian Xu, Chenghao Song, Huaming Wu +3
Cloud computing has been regarded as a successful paradigm for IT industry by providing benefits for both service providers and customers. In spite of the advantages, cloud computi…
Prepartition: Load Balancing Approach for Virtual Machine Reservations in a Cloud Data Center
Wenhong Tian, Minxian Xu, Guangyao Zhou +3
Load balancing is vital for the efficient and long-term operation of cloud data centers. With virtualization, post (reactive) migration of virtual machines after allocation is the…
Incorporating Reachability Knowledge into a Multi-Spatial Graph Convolution Based Seq2Seq Model for Traffic Forecasting
Jiexia Ye, Furong Zheng, Juanjuan Zhao +2
Accurate traffic state prediction is the foundation of transportation control and guidance. It is very challenging due to the complex spatiotemporal dependencies in traffic data. E…
A Deep Reinforcement Learning Approach for Traffic Signal Control Optimization
Zhenning Li, Chengzhong Xu, Guohui Zhang
Inefficient traffic signal control methods may cause numerous problems, such as traffic congestion and waste of energy. Reinforcement learning (RL) is a trending data-driven approa…
Machine Learning-based Orchestration of Containers: A Taxonomy and Future Directions
Zhiheng Zhong, Minxian Xu, Maria Alejandra Rodriguez +2
Containerization is a lightweight application virtualization technology, providing high environmental consistency, operating system distribution portability, and resource isolation…
PDMA: Probabilistic Service Migration Approach for Delay-aware and Mobility-aware Mobile Edge Computing
Minxian Xu, Qiheng Zhou, Huaming Wu +3
As a key technology in the 5G era, Mobile Edge Computing (MEC) has developed rapidly in recent years. MEC aims to reduce the service delay of mobile users, while alleviating the pr…
Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning
Zhenning Li, Hao Yu, Guohui Zhang +2
Inefficient traffic control may cause numerous problems such as traffic congestion and energy waste. This paper proposes a novel multi-agent reinforcement learning method, named KS…
Green-aware Mobile Edge Computing for IoT: Challenges, Solutions and Future Directions
Minxian Xu, Chengxi Gao, Shashikant Ilager +3
The development of Internet of Things (IoT) technology enables the rapid growth of connected smart devices and mobile applications. However, due to the constrained resources and li…
How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey
Jiexia Ye, Juanjuan Zhao, Kejiang Ye +1
In recent years, various deep learning architectures have been proposed to solve complex challenges (e.g. spatial dependency, temporal dependency) in traffic domain, which have ach…
Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction
Jiexia Ye, Juanjuan Zhao, Kejiang Ye +1
Stock price movement prediction is commonly accepted as a very challenging task due to the volatile nature of financial markets. Previous works typically predict the stock price ma…
Energy Efficient Algorithms based on VM Consolidation for Cloud Computing: Comparisons and Evaluations
Qiheng Zhou, Minxian Xu, Sukhpal Singh Gill +4
Cloud Computing paradigm has revolutionized IT industry and be able to offer computing as the fifth utility. With the pay-as-you-go model, cloud computing enables to offer the reso…