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RTS-PGATS based approach for data-intensive scheduling in data grids

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Frontiers of Computer Science 2011年第4期5卷 513-525页

作者： kenli li Zhao TONG Dan liU Teklay TESFAZGHI Xiangke liAO College of Information Science and Engineering Hunan University Changsha 410082 China Computer School National University of Defense Technology Changsha 410072 China

Grid computing is the combination of com- puter resources in a loosely coupled, heterogeneous, and geographically dispersed environment. Grid data are the data used in grid computing, which consists of large-scale data-intensive applications, producing and consuming huge amounts of data, distributed across a large number of machines. Data grid computing composes sets of independent tasks each of which require massive distributed data sets that may each be replicated on different resources. To reduce the completion time of the application and improve the performance of the grid, appropriate computing resources should be selected to execute the tasks and appropriate storage resources selected to serve the files required by the tasks. So the problem can be broken into two sub-problems： selection of storage resources and assignment of tasks to computing resources. This paper proposes a scheduler, which is broken into three parts that can run in parallel and uses both parallel tabu search and a parallel genetic algorithm. Finally, the proposed algorithm is evaluated by comparing it with other related algorithms, which target minimizing makespan. Simulation results show that the proposed approach can be a good choice for scheduling large data grid applications.

关键词： data grid task scheduling tabu search,genetic algorithms parallelism

Communication contention in APN list scheduling algorithm

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Science in China(Series F) 2009年第1期52卷 59-69页

作者： TANG XiaoYong li kenli PADUA Divid School of Computer and Communication Hunan University Changsha 410082 China Oriental Science & Technology College Hunan Agricultural University Changsha 410128 China Department of Computer Science University of Illinois at Urbana-Champaign Champaign 61801 USA

Task scheduling is an essential aspect of parallel process system. This NP-hard problem assumes fully connected homogeneous processors and ignores contention on the communication links. However, as arbitrary processor network （APN）, communication contention has a strong influence on the execution time of a parallel application. This paper investigates the incorporation of contention awareness into task scheduling. The innovation is the idea of dynamically scheduling edges to links, for which we use the earliest finish communication time search algorithm based on shortest-path search method. The other novel idea proposed in this paper is scheduling priority based on recursive rank computation on heterogeneous arbitrary processor network. In the end, to reduce time complexity of algorithm, a parallel algorithm is proposed and speedup O（PPE） is achieved. The comparison study, based on both randomly generated graphs and the graphs of some real applications, shows that our scheduling algorithm significantly surpasses classic and static communication contention awareness algorithm, especially for high data transmission rate parallel application.

关键词： list scheduling arbitrary processor network DAG communication contention parallel algorithm

CLS-Miner: efficient and effective closed high-utility itemset mining

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Frontiers of Computer Science 2019年第2期13卷 357-381页

作者： Thu-Lan DAM kenli li Philippe FOURNIER-VIGER Quang-Huy DUONG College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China Faculty of Information Technology Hanoi University of Industry Hanoi Vietnam CIC of HPC National University of Defense Technology Changsha 410073 China National Supercomputing Center in Changsha Changsha 410082 China School of Natural Sciences and Humanities Harbin Institute of Technology Shenzhen Graduate School Shenzhen 518055 China Department of Computer Science Norwegian University of Science and Technology Trondheim Norway

High-utility itemset mining (HUIM) is a popular data mining task with applications in numerous domains. However, traditional HUIM algorithms often produce a very large set of high-utility itemsets (HUIs). As a result, analyzing HUIs can be very time consuming for users. Moreover, a large set of HUIs also makes HUIM algorithms less efficient in terms of execution time and memory consumption. To address this problem, closed high-utility itemsets (CHUIs), concise and lossless representations of all HUIs, were proposed recently. Although mining CHUIs is useful and desirable, it remains a computationally expensive task. This is because current algorithms often generate a huge number of candidate itemsets and are unable to prune the search space effectively. In this paper, we address these issues by proposing a novel algorithm called CLS-Miner. The proposed algorithm utilizes the utility-list structure to directly compute the utilities of itemsets without producing candidates. It also introduces three novel strategies to reduce the search space, namely chain-estimated utility co-occurrence pruning, lower branch pruning, and pruning by coverage. Moreover, an effective method for checking whether an itemset is a subset of another itemset is introduced to further reduce the time required for discovering CHUIs. To evaluate the performance of the proposed algorithm and its novel strategies, extensive experiments have been conducted on six benchmark datasets having various characteristics. Results show that the proposed strategies are highly efficient and effective, that the proposed CLS-Miner algorithm outperforms the current state-ofthe- art CHUD and CHUI-Miner algorithms, and that CLSMiner scales linearly.

关键词： utility mining high-utility itemset mining closed itemset mining closed high-utility itemset mining

Energy-Aware Scheduling with Uncertain Execution Time for Real-Time Systems

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Chinese Journal of Electronics 2017年第1期26卷 42-49页

作者： ZHANG Longxin TONG Zhao ZHU Ningbo XIAO Zheng li kenli College of Computer and Communication Hunan University of Technology College of Computer Science and Electronic Engineering Hunan University College of Mathematic and Computer Science Hunan Normal University

Energy-saving is extraordinary important for real-time systems. Dynamic voltage scaling(DVS) is an important technique to reduce the energy consumption of processors that support voltage scaling. It has been exploited extensively in task scheduling. However, many approaches take simple treatments and some of them even neglect the large voltage transition overheads. Although some strategies consider the penalty, frequency switching produces extra large time overhead and deadline misses occur frequently. In our paper, we propose an energy efficient soft real-time dynamic program scheme by using quantitative switching overhead and communication penalty for multitask scheduling with uncertain execution time. The experiments show that our approaches significantly outperform existing solutions both on simple-core and multi-core systems in terms of energy-saving.

关键词： Dynamic voltage scaling(DVS) Energy minimization Real-time Task scheduling

Monodirectional evolutional symport tissue P systems with channel states and cell division

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Science China(Information Sciences) 2023年第3期66卷 293-294页

作者： Bosheng SONG kenli li Xiangxiang ZENG Mario J.PéREZ-JIMéNEZ Claudio ZANDRON College of Computer Science and Electronic Engineering Hunan University Department of Computer Science and Artificial Intelligence Universidad de Sevilla Dipartimento di Informatica Sistemistica e Comunicazione Università degli Studi di Milano-Bicocca

Bioinspired computing is a research framework that solves computer science problems based on the principles of biology and the natural world, including DNA and membrane computing. Membrane computing(MC), an active subfield of bioinspired computing based on the biological cell structure,emerged as an unconventional computational paradigm and has generated an extensive fundamental research scope in theoretical computer science [1].

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科学研究的第五范式——以智能驱动的材料设计为例

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Engineering 2023年第5期24卷 126-137,I0003,I0004页

作者： Can Leng Zhuo Tang Yi-Ge Zhou Zean Tian Wei-Qing Huang Jie liu Keqin li kenli li Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense TechnologyChangsha 410073China Laboratory of Software Engineering for Complex Systems National University of Defense TechnologyChangsha 410073China National Supercomputing Center in Changsha Changsha 410082China College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China Institute of Chemical Biology and Nanomedicine State Key Laboratory of Chemo/Biosensing and ChemometricsCollege of Chemistry and Chemical EngineeringHunan UniversityChangsha 410082China Department of Applied Physics School of Physics and ElectronicsHunan UniversityChangsha 410082China Department of Computer Science State University of New YorkNew PaltzNY 12561USA

科学正在进入一个新时代——第五范式——它被认为是知识整合到不同领域的主要特征,是基于无所不在的机器学习系统的计算社区中智能驱动的工作。在此,我们通过在天河一号超级计算机系统上构建的催化材料专门设计的典型平台案例,生动地... 详细信息

科学正在进入一个新时代——第五范式——它被认为是知识整合到不同领域的主要特征,是基于无所不在的机器学习系统的计算社区中智能驱动的工作。在此,我们通过在天河一号超级计算机系统上构建的催化材料专门设计的典型平台案例,生动地阐明了第五范式的本质,旨在促进第五范式在其他领域的培养。第五范式平台主要包括模型自动构建(原始数据提取)、指纹自动构建(神经网络特征选择)以及跨学科知识串联的重复迭代(“火山图”)。与分解一起进行的是对迭代中实现的体系结构的性能评估。通过讨论,第五范式的智能驱动平台可以极大地简化和改进研究中极其繁琐和具有挑战性的工作,并通过补偿机器学习中缺少样本和替代一些由于计算资源不足而导致的数值计算来实现数值计算与机器学习之间的相互反馈,从而加速探索过程。在数据驱动的学科中,跨学科专家的协同作用和对动态数据需求的急剧增长仍然是一个挑战。我们相信,对第五范式平台的一瞥可以为其在其他领域的应用铺平道路。

关键词：机器学习自动构建天河一号数据驱动神经网络动态数据知识整合科学研究

Predicting the Antigenic Variant of Human Influenza A(H3N2) Virus with a Stacked Auto-Encoder Model

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国际计算机前沿大会会议论文集 2017年第2期 71-73页

作者： Zhiying Tan kenli li Taijiao Jiang Yousong Peng College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China Center of System Medicine Institute of Basic Medical SciencesChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100005 China Suzhou Institute of Systems Medicine Suzhou 215123 Jiangsu China College of Biology Hunan University Changsha 410082 China

The influenza virus changes its antigenicity frequently due to rapid mutations, leading to immune escape and failure of vaccination. Rapid determination of the influenza antigenicity could help identify the antigenic variants in time. Here, we built a stacked auto-encoder (SAE) model for predicting the antigenic variant of human influenza A(H3N2) viruses based on the hemagglutinin (HA) protein sequences. The model achieved an accuracy of 0.95 in five-fold cross-validations, better than the logistic regression model did. Further analysis of the model shows that most of the active nodes in the hidden layer reflected the combined contribution of multiple residues to antigenic variation. Besides, some features (residues on HA protein) in the input layer were observed to take part in multiple active nodes, such as residue 189, 145 and 156, which were also reported to mostly determine the antigenic variation of influenza A(H3N2) viruses. Overall,this work is not only useful for rapidly identifying antigenic variants in influenza prevention, but also an interesting attempt in inferring the mechanisms of biological process through analysis of SAE model, which may give some insights into interpretation of the deep learning

关键词： Stacked auto-encoder Antigenic variation nfluenza Machine learning

Prediction of Cell Specific O-GalNAc Glycosylation in Human

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国际计算机前沿大会会议论文集 2017年第2期 65-67页

作者： Yuanqiang Zou kenli li Taijiao Jiang Yousong Peng College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082 China Center of System Medicine Institute of Basic Medical Sciences Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100005 China Suzhou Institute of Systems Medicine Suzhou 215123 Jiangsu China College of Biology Hunan University Changsha 410082 China

Glycosylation is one of the most extensive post-translation modifications of proteins. Although lots of computational models have been developed to predict the glycosylation sites, none of them considered the tissue and cell specificity of glycosylation. Here, we built a two-step computational method GlycoCell to predict the cell-specific O-GalNAc glycosylation, the most complex type of O-glycosylation reported so far, in 12 human cell types. The first step predicted whether a site had the potential to be O-glycosylated. The model achieved an accuracy of 0.83. The second step predicted whether a potential glycosite would be O-glycosylated in the given cell type. For 12 cell types, a model was built for each cell type. The accuracies for these models ranged from 0.78 to 0.87. To facilitate the usage of GlycoCell for the public, a web server was built which is available at http://***/GlyoCell/***. It could be useful for investigating the cell-specific O-glycosylation in human.

关键词： O-glycosylation Cell-specific SVM Computational Prediction Word vector

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一种基于风险的多域互操作动态访问控制模型

计算机研究与发展 2009年第6期46卷 948-955页

作者：唐卓赵林李肯立李瑞轩湖南大学计算机与通信学院长沙410082 华中科技大学计算机科学与技术学院武汉430074

随着Internet及其相关技术的快速发展,在开放的、异构的多自治域环境下,出现了大量的分布式应用之间的互操作.多自治域环境的复杂性与信息安全共享不断演变进化的特点,使得传统访问控制模型难以保证数据资源在交互过程中的安全.通过将... 详细信息

随着Internet及其相关技术的快速发展,在开放的、异构的多自治域环境下,出现了大量的分布式应用之间的互操作.多自治域环境的复杂性与信息安全共享不断演变进化的特点,使得传统访问控制模型难以保证数据资源在交互过程中的安全.通过将风险概念引入访问控制中,提出一种基于风险的多域动态访问控制模型.在本模型中,主体所具有的某项安全策略的风险等级由自治域间的互操作历史记录、客体的安全等级以及访问事件的安全系数得出,通过对高风险等级的安全策略进行调整以达到对系统风险的实时控制.理论分析表明这种方法可有效保证访问控制的灵活性和多自治域环境的安全性.

关键词：风险多自治域互操作访问控制信任