Wentai Wu

Orcid: 0000-0001-5851-327X

According to our database1, Wentai Wu authored at least 31 papers between 2016 and 2024.

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Bibliography

2024
HybridAD: A Hybrid Model-Driven Anomaly Detection Approach for Multivariate Time Series.
IEEE Trans. Emerg. Top. Comput. Intell., February, 2024

2023
Performance Interference of Virtual Machines: A Survey.
ACM Comput. Surv., December, 2023

Publisher Correction to: Thermal‑aware virtual machine placement based on multi‑objective optimization.
J. Supercomput., October, 2023

FedProf: Selective Federated Learning Based on Distributional Representation Profiling.
IEEE Trans. Parallel Distributed Syst., June, 2023

An adaptive workload-aware power consumption measuring method for servers in cloud data centers.
Computing, March, 2023

Thermal-aware virtual machine placement based on multi-objective optimization.
J. Supercomput., 2023

Evolving Deep Multiple Kernel Learning Networks Through Genetic Algorithms.
IEEE Trans. Ind. Informatics, 2023

SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting.
CoRR, 2023

PETformer: Long-term Time Series Forecasting via Placeholder-enhanced Transformer.
CoRR, 2023

2022
An On-Line Virtual Machine Consolidation Strategy for Dual Improvement in Performance and Energy Conservation of Server Clusters in Cloud Data Centers.
IEEE Trans. Serv. Comput., 2022

Developing an Unsupervised Real-Time Anomaly Detection Scheme for Time Series With Multi-Seasonality.
IEEE Trans. Knowl. Data Eng., 2022

Multi-scale residual denoising GAN model for producing super-resolution CTA images.
J. Ambient Intell. Humaniz. Comput., 2022

VFedCS: Optimizing Client Selection for Volatile Federated Learning.
IEEE Internet Things J., 2022

2021
Towards efficient horizontal federated learning.
PhD thesis, 2021

Accelerating Federated Learning Over Reliability-Agnostic Clients in Mobile Edge Computing Systems.
IEEE Trans. Parallel Distributed Syst., 2021

An Efficiency-Boosting Client Selection Scheme for Federated Learning With Fairness Guarantee.
IEEE Trans. Parallel Distributed Syst., 2021

A Power Consumption Model for Cloud Servers Based on Elman Neural Network.
IEEE Trans. Cloud Comput., 2021

SAFA: A Semi-Asynchronous Protocol for Fast Federated Learning With Low Overhead.
IEEE Trans. Computers, 2021

A Taxonomy and Survey of Power Models and Power Modeling for Cloud Servers.
ACM Comput. Surv., 2021

Variation-Incentive Loss Re-weighting for Regression Analysis on Biased Data.
CoRR, 2021

FedProf: Optimizing Federated Learning with Dynamic Data Profiling.
CoRR, 2021

MGGAN: Improving sample generations of Generative Adversarial Networks.
Proceedings of the 2021 IEEE 23rd Int Conf on High Performance Computing & Communications; 7th Int Conf on Data Science & Systems; 19th Int Conf on Smart City; 7th Int Conf on Dependability in Sensor, 2021

2020
A novel syntax-aware automatic graphics code generation with attention-based deep neural network.
J. Netw. Comput. Appl., 2020

2019
SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead.
CoRR, 2019

Local Trend Inconsistency: A Prediction-driven Approach to Unsupervised Anomaly Detection in Multi-seasonal Time Series.
CoRR, 2019

2018
A heuristic task scheduling algorithm based on server power efficiency model in cloud environments.
Sustain. Comput. Informatics Syst., 2018

A Power Monitoring System Based on a Multi-Component Power Model.
Int. J. Grid High Perform. Comput., 2018

Energy-efficient hadoop for big data analytics and computing: A systematic review and research insights.
Future Gener. Comput. Syst., 2018

Experimental and quantitative analysis of server power model for cloud data centers.
Future Gener. Comput. Syst., 2018

2017
An intelligent power consumption model for virtual machines under CPU-intensive workload in cloud environment.
Soft Comput., 2017

2016
A Heuristic Task Scheduling Algorithm for Heterogeneous Virtual Clusters.
Sci. Program., 2016


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