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Your search keyword '"Tian, Wenhong"' showing total 40 results
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40 results on '"Tian, Wenhong"'

14. Nonfragile Sampled-Data Filtering of Uncertain Fuzzy Systems With Time-Varying Delays.

Author

Luo, Jinnan, Liu, Xinzhi, Tian, Wenhong, Zhong, Shouming, and Shi, Kaibo

Subjects
UNCERTAIN systems, TIME-varying systems, SYMMETRIC matrices
Abstract

This article studies the nonfragile sampled-data filtering problems for Takagi–Sugeno fuzzy systems with uncertainties and delays. By introducing two delay-product-type terms and other augmented terms, a novel Lyapunov–Krasovskii functional containing more detailed information of time delays is constructed. With the help of some new bounding inequalities, improved stability results for the addressed systems are established. Besides, a resilient sampled-data controller is devised with less cost. Furthermore, the efficiency of the obtained criteria is illustrated by a numerical example. [ABSTRACT FROM AUTHOR]

Published
2021
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25. A Service Framework for Scientific Workflow Management in the Cloud.

Author

Zhao, Yong, Li, Youfu, Raicu, Ioan, Lu, Shiyong, Lin, Cui, Zhang, Yanzhe, Tian, Wenhong, and Xue, Ruini

Abstract

Cloud computing is an emerging computing paradigm that can offer unprecedented scalability and resources on demand, and is getting more and more adoption in the science community, while scientific workflow management systems provide essential support such as management of data and task dependencies, job scheduling and execution, provenance tracking, etc., to scientific computing. As we are entering into a “big data” era, it is imperative to migrate scientific workflow management systems into the cloud to manage the ever increasing data scale and analysis complexity. We propose a reference service framework for integrating scientific workflow management systems into various cloud platforms, which consists of eight major components, including Cloud Workflow Management Service, Cloud Resource Manager, etc., and six interfaces between them. We also present a reference framework for the implementation of Cloud Resource Manager, which is responsible for the provisioning and management of virtual resources in the cloud. We discuss our implementation of the framework by integrating the Swift scientific workflow management system with theOpenNebula and Eucalyptus cloud platforms, and demonstrate the capability of the solution using a NASA MODIS image processing workflow and a production deployment on the Science@Guoshi network with support for the Montage image mosaic workflow. [ABSTRACT FROM PUBLISHER]

Published
2015
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37. A Toolkit for Modeling and Simulation of Real-Time Virtual Machine Allocation in a Cloud Data Center.

Author

Tian, Wenhong, Zhao, Yong, Xu, Minxian, Zhong, Yuanliang, and Sun, Xiashuang

Subjects
*COMPUTER simulation, *CLOUD computing, *REAL-time computing, *VIRTUAL machine systems, *COMPUTATION laboratories, *SERVER farms (Computer network management)
Abstract

Resource scheduling in infrastructure as a service (IaaS) is one of the keys for large-scale Cloud applications. Extensive research on all issues in real environment is extremely difficult because it requires developers to consider network infrastructure and the environment, which may be beyond the control. In addition, the network conditions cannot be predicted or controlled. Therefore, performance evaluation of workload models and Cloud provisioning algorithms in a repeatable manner under different configurations and requirements is difficult. There is still lack of tools that enable developers to compare different resource scheduling algorithms in IaaS regarding both computing servers and user workloads. To fill this gap in tools for evaluation and modeling of Cloud environments and applications, we propose CloudSched. CloudSched can help developers identify and explore appropriate solutions considering different resource scheduling algorithms. Unlike traditional scheduling algorithms considering only one factor such as CPU, which can cause hotspots or bottlenecks in many cases, CloudSched treats multidimensional resource such as CPU, memory and network bandwidth integrated for both physical machines and virtual machines (VMs) for different scheduling objectives (algorithms). In this paper, two existing simulation systems at application level for Cloud computing are studied, a novel lightweight simulation system is proposed for real-time VM scheduling in Cloud data centers, and results by applying the proposed simulation system are analyzed and discussed. [ABSTRACT FROM PUBLISHER]

Published
2015
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