Search

Your search keyword '"Weng, Chuliang"' showing total 162 results
Start Over Author "Weng, Chuliang"
162 results on '"Weng, Chuliang"'

5. A Parallel Random Forest Algorithm for Big Data in a Spark Cloud Computing Environment

Author

Chen, Jianguo, Li, Kenli, Tang, Zhuo, Bilal, Kashif, Yu, Shui, Weng, Chuliang, and Li, Keqin

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

With the emergence of the big data age, the issue of how to obtain valuable knowledge from a dataset efficiently and accurately has attracted increasingly attention from both academia and industry. This paper presents a Parallel Random Forest (PRF) algorithm for big data on the Apache Spark platform. The PRF algorithm is optimized based on a hybrid approach combining data-parallel and task-parallel optimization. From the perspective of data-parallel optimization, a vertical data-partitioning method is performed to reduce the data communication cost effectively, and a data-multiplexing method is performed is performed to allow the training dataset to be reused and diminish the volume of data. From the perspective of task-parallel optimization, a dual parallel approach is carried out in the training process of RF, and a task Directed Acyclic Graph (DAG) is created according to the parallel training process of PRF and the dependence of the Resilient Distributed Datasets (RDD) objects. Then, different task schedulers are invoked for the tasks in the DAG. Moreover, to improve the algorithm's accuracy for large, high-dimensional, and noisy data, we perform a dimension-reduction approach in the training process and a weighted voting approach in the prediction process prior to parallelization. Extensive experimental results indicate the superiority and notable advantages of the PRF algorithm over the relevant algorithms implemented by Spark MLlib and other studies in terms of the classification accuracy, performance, and scalability.

Published
2018
Full Text
View/download PDF

7. D2-GCN: a graph convolutional network with dynamic disentanglement for node classification.

Author

Wu, Shangwei, Xiong, Yingtong, Liang, Hui, and Weng, Chuliang

Abstract

Classic Graph Convolutional Networks (GCNs) often learn node representation holistically, which ignores the distinct impacts from different neighbors when aggregating their features to update a node's representation. Disentangled GCNs have been proposed to divide each node's representation into several feature units. However, current disentangling methods do not try to figure out how many inherent factors the model should assign to help extract the best representation of each node. This paper then proposes D2-GCN to provide dynamic disentanglement in GCNs and present the most appropriate factorization of each node's mixed features. The convergence of the proposed method is proved both theoretically and experimentally. Experiments on real-world datasets show that D2-GCN outperforms the baseline models concerning node classification results in both single- and multi-label tasks. [ABSTRACT FROM AUTHOR]

Published
2025
Full Text
View/download PDF

8. CBA-Detector: An Accurate Detector Against Cache-Based Attacks Using HPCs and Pintools

Author

Zheng, Beilei, Gu, Jianan, Weng, Chuliang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yew, Pen-Chung, editor, Stenström, Per, editor, Wu, Junjie, editor, Gong, Xiaoli, editor, and Li, Tao, editor

Published
2019
Full Text
View/download PDF

9. SIMD Accelerates the Probe Phase of Star Joins in Main Memory Databases

Author

Fang, Zhuhe, He, Zeyu, Chu, Jiajia, Weng, Chuliang, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Li, Guoliang, editor, Yang, Jun, editor, Gama, Joao, editor, Natwichai, Juggapong, editor, and Tong, Yongxin, editor

Published
2019
Full Text
View/download PDF

10. BigOP: Generating Comprehensive Big Data Workloads as a Benchmarking Framework

Author

Zhu, Yuqing, Zhan, Jianfeng, Weng, Chuliang, Nambiar, Raghunath, Zhang, Jinchao, Chen, Xingzhen, and Wang, Lei

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Databases, Computer Science - Performance
Abstract

Big Data is considered proprietary asset of companies, organizations, and even nations. Turning big data into real treasure requires the support of big data systems. A variety of commercial and open source products have been unleashed for big data storage and processing. While big data users are facing the choice of which system best suits their needs, big data system developers are facing the question of how to evaluate their systems with regard to general big data processing needs. System benchmarking is the classic way of meeting the above demands. However, existent big data benchmarks either fail to represent the variety of big data processing requirements, or target only one specific platform, e.g. Hadoop. In this paper, with our industrial partners, we present BigOP, an end-to-end system benchmarking framework, featuring the abstraction of representative Operation sets, workload Patterns, and prescribed tests. BigOP is part of an open-source big data benchmarking project, BigDataBench. BigOP's abstraction model not only guides the development of BigDataBench, but also enables automatic generation of tests with comprehensive workloads. We illustrate the feasibility of BigOP by implementing an automatic test generation tool and benchmarking against three widely used big data processing systems, i.e. Hadoop, Spark and MySQL Cluster. Three tests targeting three different application scenarios are prescribed. The tests involve relational data, text data and graph data, as well as all operations and workload patterns. We report results following test specifications., Comment: 10 pages, 3 figures

Published
2014

12. Survey on Protection Mechanisms for Untrusted Hypervisor in Cloud

Author

GU Jianan, ZHENG Beilei, WENG Chuliang

Subjects
cloud platform, virtualization security, untrusted hypervisor, construction of protection mechanism, Electronic computers. Computer science, QA75.5-76.95
Abstract

In this big data era, the multi-tenant cloud platform plays an important role. However, as one of the major technologies adopted in the cloud platforms, the virtualization technology is not secure enough. The Hypervisor is a key layer in the virtualization software stack that manages vital tasks between guest virtual machines and the bare metal, such as resource allocation, sharing and isolation. Meanwhile, the Hypervisor suffers from vulnerabilities along with its large attack surface, which makes attacks on the Hypervisor threaten the cloud and applications above. Therefore, constructing protection mechanisms for the untrusted Hypervisor in the cloud is necessary. Survey from the perspective of the protection mechanism's construction: analyze the feasibility and challenges, and do cla-ssifications; present the related work in this field in terms of integrity detection mechanisms, defense mechanisms, and isolation mechanisms; give research trends and provide a valuable reference for future researches on the vir-tualization security and on building a reliable multi-tenant cloud platform.

Published
2020
Full Text
View/download PDF

21. Dynamic Malicious Code Detection Based on Binary Translator

Author

Fang, Zhe, Li, Minglu, Weng, Chuliang, Luo, Yuan, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Jaatun, Martin Gilje, editor, Zhao, Gansen, editor, and Rong, Chunming, editor

Published
2009
Full Text
View/download PDF

23. An Incentive Approach for Computational Resource Sharing in the Autonomous Environment

Author

Weng, Chuliang, Li, Minglu, Lu, Xinda, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Wu, Song, editor, Yang, Laurence T., editor, and Xu, Tony Li, editor

Published
2008
Full Text
View/download PDF

24. Combining Wireless Sensor Network with Grid for Intelligent City Traffic

Author

Tang, Feilong, Li, Minglu, Weng, Chuliang, Zhang, Chongqing, Zhang, Wenzhe, Huang, Hongyu, Wang, Yi, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Jesshope, Chris, editor, and Egan, Colin, editor

Published
2006
Full Text
View/download PDF

25. A Resource Scheduling Strategy for the CFD Application on the Grid

Author

Li, Minglu, Weng, Chuliang, Lu, Xinda, Yin, Yong, Deng, Qianni, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Chen, Guihai, editor, Pan, Yi, editor, Guo, Minyi, editor, and Lu, Jian, editor

Published
2005
Full Text
View/download PDF

26. A User-Guided Semi-automatic Parallelization Method and Its Implementation

Author

Weng, Chuliang, Chen, Zhongguo, Lu, Xinda, Li, Minglu, Yin, Yong, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Cao, Jiannong, editor, Nejdl, Wolfgang, editor, and Xu, Ming, editor

Published
2005
Full Text
View/download PDF

27. ShanghaiGrid: A Grid Prototype for Metropolis Information Services

Author

Li, Minglu, Wu, Min-You, Li, Ying, Huang, Linpeng, Deng, Qianni, Cao, Jian, Xue, Guangtao, Weng, Chuliang, Lin, Xinhua, Lu, Xinda, Jiang, Changjun, Tong, Weiqin, Gui, Yadong, Zhou, Aoying, Wu, Xinhong, Jiang, Shui, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Zhang, Yanchun, editor, Tanaka, Katsumi, editor, Yu, Jeffrey Xu, editor, Wang, Shan, editor, and Li, Minglu, editor

Published
2005
Full Text
View/download PDF

28. A New Method for Online Scheduling in Computational Grid Environments

Author

Weng, Chuliang, Li, Minglu, Lu, Xinda, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Zhang, Yanchun, editor, Tanaka, Katsumi, editor, Yu, Jeffrey Xu, editor, Wang, Shan, editor, and Li, Minglu, editor

Published
2005
Full Text
View/download PDF

29. Resource Management and Scheduling for High Performance Computing Application Based on WSRF

Author

Weng, Chuliang, Li, Minglu, Lu, Xinda, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Zhang, Yanchun, editor, Tanaka, Katsumi, editor, Yu, Jeffrey Xu, editor, Wang, Shan, editor, and Li, Minglu, editor

Published
2005
Full Text
View/download PDF

30. A Double Auction Mechanism for Resource Allocation on Grid Computing Systems

Author

Weng, Chuliang, Lu, Xinda, Xue, Guangtao, Deng, Qianni, Li, Minglu, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Jin, Hai, editor, Pan, Yi, editor, Xiao, Nong, editor, and Sun, Jianhua, editor

Published
2004
Full Text
View/download PDF

31. A Distributed Approach for Resource Pricing in Grid Environments

Author

Weng, Chuliang, Lu, Xinda, Deng, Qianni, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Li, Minglu, editor, Sun, Xian-He, editor, Deng, Qianni, editor, and Ni, Jun, editor

Published
2004
Full Text
View/download PDF

32. Formalizing Service Publication and Discovery in Grid Computing Systems

Author

Weng, Chuliang, Lu, Xinda, Deng, Qianni, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Li, Minglu, editor, Sun, Xian-He, editor, Deng, Qian-ni, editor, and Ni, Jun, editor

Published
2004
Full Text
View/download PDF

35. Handling Data Skew for Aggregation in Spark SQL Using Task Stealing

Author

Huang Qiuli, Li Zhifang, Weng Chuliang, and He Zeyu

Subjects
010302 applied physics, SQL, Computer science, Process (engineering), Distributed computing, Skew, Partition problem, 02 engineering and technology, 01 natural sciences, 020202 computer hardware & architecture, Theoretical Computer Science, Task (computing), Operator (computer programming), 0103 physical sciences, Spark (mathematics), 0202 electrical engineering, electronic engineering, information engineering, Overhead (computing), computer, Software, Information Systems, computer.programming_language
Abstract

In distributed in-memory computing systems, data distribution has a large impact on performance. Designing a good partition algorithm is difficult and requires users to have adequate prior knowledge of data, which makes data skew common in reality. Traditional approaches to handling data skew by sampling and repartitioning often incur additional overhead. In this paper, we proposed a dynamic execution optimization for the aggregation operator, which is one of the most general and expensive operators in Spark SQL. Our optimization aims to avoid the additional overhead and improve the performance when data skew occurs. The core idea is task stealing. Based on the relative size of data partitions, we add two types of tasks, namely segment tasks for larger partitions and stealing tasks for smaller partitions. In a stage, stealing tasks could actively steal and process data from segment tasks after processing their own. The optimization achieves significant performance improvements from 16% up to 67% on different sizes and distributions of data. Experiments show that involved overhead is minimal and could be negligible.

Published
2020

39. ShadowVM

Author

Li, Zhifang, primary, Han, Mingcong, additional, Wu, Shangwei, additional, and Weng, Chuliang, additional

Published
2021
Full Text
View/download PDF

47. ShanghaiGrid: A Grid Prototype for Metropolis Information Services

Author

Li, Minglu, primary, Wu, Min-You, additional, Li, Ying, additional, Huang, Linpeng, additional, Deng, Qianni, additional, Cao, Jian, additional, Xue, Guangtao, additional, Weng, Chuliang, additional, Lin, Xinhua, additional, Lu, Xinda, additional, Jiang, Changjun, additional, Tong, Weiqin, additional, Gui, Yadong, additional, Zhou, Aoying, additional, Wu, Xinhong, additional, and Jiang, Shui, additional

Published
2005
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results