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检索条件"机构=Science and Technology on Parallel and Distributed Laboratory"

共 544 条记录，以下是181-190 订阅

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Using Configuration Semantic Features and Machine Learning Algorithms to Predict Build Result in Cloud-Based Container Environment

Using Configuration Semantic Features and Machine Learning A...

引用

International Conference on parallel and distributed Systems (ICPADS)

作者： Yiwen Wu Yang Zhang Junsheng Chang Bo Ding Tao Wang Huaimin Wang Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology China

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

Container technologies are being widely used in large scale production cloud environments, of which Docker has become the de-facto industry standard. In practice, Docker builds often break, and a large amount of efforts are put into troubleshooting broken builds. Prior studies have evaluated the rate at which builds in large organizations fail. However, there is still a lack of early warning methods for predicting the Docker build result before the build starts. This paper provides a first attempt to propose an automatic method named PDBR. It aims to use the configuration semantic features extracted by AST and the machine learning algorithms to predict build result in the cloud-based container environment. The evaluation experiments based on more than 36,000 collected Docker builds show that PDBR achieves 73.45%-91.92% in F1 and 29.72%-72.16% in AUC. We also demonstrate that different ML classifiers have significant and large effects on the PDBR AUC performance.

关键词： Machine learning algorithms Semantics Standards organizations Production Organizations Containers Feature extraction

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DIPP——An LLC Replacement Policy for On-chip Dynamic Heterogeneous Multi-core Architecture

DIPP——An LLC Replacement Policy for On-chip Dynamic Hetero...

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International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2015

作者： Zhang Yang Xing Zuocheng Ma Xiao Science and technology on Parallel and distributed processing laboratory National University of Defense Technology

As the big data era is coming,it brings new challenges to the massive data processing.A combination of GPU and CPU on chip is the trend to release the pressure of large scale *** found that there are different memory access characteristics between GPU and *** most important one is that the programs of GPU include a large number of threads,which lead to higher access frequency in cache than the CPU *** the LRU policy favors the programs with high memory access frequency,the programs of GPU can't get the corresponding performance boost even more cache resources are *** LRU policy is not suitable for heterogeneous multi-core *** on the different characteristics of GPU and CPU programs on memory access,this paper proposes an LLC dynamic replacement policy--DIPP(Dynamic Insertion/Promotion Policy) for heterogeneous multi-core *** core idea of the replacement policy is to reduce the miss rate of the program and enhance the overall system performance by limiting the cache resources that GPU can acquire and reducing the thread interferences between *** compare the DIPP replacement policy with LRU and we conduct a classified discussion according to the program results of *** programs enhance 23.29% on the average performance(using arithmetic mean).Large working sets programs can improve 13.95%,compute-intensive programs enhance 9.66% and stream class programs improve 3.8%.

关键词： Big data Heterogeneous Multicore Replacement Policy DIPP

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DISVMs: Fast SVMs Training on Large-scale Data Sets

DISVMs: Fast SVMs Training on Large-scale Data Sets

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International Conference on Tools with Artificial Intelligence

作者： Lijuan Cui Changjian Wang Ziyang Li Yuxing Peng Science and Technology on Parallel and distributed Processing Laboratory National University of Defence Technology

ISBN: (纸本)9781509044603

Support Vector Machines (SVMs) are powerful classification tools. However, the model training is very time-consuming when meeting large scale data sets. Some efforts have been devoted to screening out non-support vectors (non-SVs) to accelerate the training. But their processes rely on prior knowledge of other classifiers with different parameters to screen out non-SVs. In this paper, we propose Directional Indicator Support Vector Machines (DISVMs) to efficiently identify non-SVs. DISVMs employs a directional indicator, which points to the approximately orthogonal direction of the separating hyperplane, to qualitatively define the location of different samples and thus identify non-SVs. Furthermore, DISVMs leverages a two-stage algorithm: the first stage is to compute the directional indicator. The second stage is to identify non-SVs using the indicator. To avoid misjudgement, we propose CnSV method for non-SVs based on the majority rule. DISVMs screens out non-SVs with light computation and little accuracy loss. Experiments show that our approach significantly reduces the total computation cost.

关键词： Support vector machines Non-support vector Directional indicator Computation efficiency Data preprocessing

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Design and Implementation of distributed Stage DB:A High Performance distributed Key-Value Database

Design and Implementation of Distributed Stage DB:A High Per...

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2014 International Conference on Industrial Engineering and Information technology

作者： Hui-jun Wu Kai Lu Gen Li Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology

With the development of high performance computing and Web 2.0 applications,unstructured data storage becomes more and more *** RDBMS isn't efficient for big data ***,RDBMS's scalability is ***' expansion often leads to a large scale of data *** paper designs and implements a high performance distributed key-value database,which is distributed Stage *** servers are organized by a consistent hashing ring and distributed with the support of Zookeeper,a distributed service *** has a high single-node read/write *** route information is calculated by clients,which reduces the expense of expansion.

关键词： distributed system,database,key-value,Zookeeper

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2018 International Joint Conference on Neural Networks, IJCNN 2018

作者： Yu, Hao Zhang, Zhaoning Zhu, Zijie Wang, Xiong Zhang, Gen Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha China College of Meteorology and Oceanology National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781509060146

Similarity of nominal data plays fundamental roles in numerous fields of both machine learning and data mining. Unlike the similarity of numerical data, that of nominal data is much more difficult to describe, and few efforts have been done for it. Although existing nominal similarity measures can reveal a part of data properties, they suffer from low accuracy due to ignoring value relationships or integrating multi-view relationships inappropriately. In this paper, we propose a novel hierarchical measure for nominal data similarity (HNS). The HNS leverages the intrinsic data characteristics by considering low-level information both within and between attributes, and hierarchically seizes the value distributions, attribute interactions and attribute to object contributions. Meanwhile, it aggregates multi-view relationships trough a bottom to top framework, remaining consistency as well as complementary details. We theoretically analyzed this measure, and experiments on six UCI data sets demonstrate that the HNS outperforms the state-of-the-art nominal similarity measures in term of target alignment and clustering accuracy. © 2018 IEEE.

关键词： Data mining

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A secure light-weight public auditing scheme in cloud computing with potentially malicious third party auditor

A secure light-weight public auditing scheme in cloud comput...

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作者： Wu, Yilun Lin, Xinye Lu, Xicheng Su, Jinshu Chen, Peixin College of Computer National University of Defense Technology China School of Computer Science McGill University Montreal Canada National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology China

Public auditing is a new technique to protect the integrity of outsourced data in the remote cloud. Users delegate the ability of auditing to a third party auditor (TPA), and assume that each result from the TPA is correct. However, the TPA is not always trustworthy in reality. In this paper, we consider a scenario in which the TPA may lower the reputation of the cloud server by cheating users, and propose a novel public auditing scheme to address this security issue. The analyses and the evaluation prove that our scheme is both secure and efficient. © 2016 The Institute of Electronics, Information and Communication Engineers.

关键词： Cloud security

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Dockerfile Changes in Practice: A Large-Scale Empirical Study of 4,110 Projects on GitHub

Dockerfile Changes in Practice: A Large-Scale Empirical Stud...

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Asia-Pacific Conference on Software Engineering

作者： Yiwen Wu Yang Zhang Tao Wang Huaimin Wang Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology China

ISBN: (数字)9781728195537

ISBN: (纸本)9781728195544

Docker is one of the most popular containerization tools in current DevOps practice. Particularly, Dockerfile plays an important role in the Docker-based software development process by specifying the commands and build environment of Docker containers. As a project progresses through its development stages, the content of the Dockerfile may be revised many times. Previous studies have examined Dockerfile usage in open-source projects. However, little is known about the details of Dockerfile changes in practice. In this paper, we conduct an empirical study on Dockerfile changes for 4,110 open-source projects hosted on GitHub. Based on the Dockerfile data, we measure the frequency, magnitude, and instructions of Dockerfile changes and report how Dockerfile co-changed with other files. To explore the relationship between Dockerfile changes and project outcomes, i.e., popularity, success, and productivity, we also develop regression models, by controlling for various confounds. Our findings help to characterize and understand Dockerfile changes and motivate the need for collecting more empirical evidence.

关键词： Productivity Tools Maintenance engineering Frequency measurement Open source software Software development management Software engineering

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A Novel Interactive Recurrent Attention Network for Emotion-Cause Pair Extraction

A Novel Interactive Recurrent Attention Network for Emotion-...

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作者： Xiangyu Jia Xinhai Chen Qian Wan Jie Liu Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology

Unlike Emotion Cause Extraction(ECE) task which consists of pre-annotate emotions and passage, emotion-cause pair extraction(ECPE) aims at extracting potential emotions and corresponding causes in the document without the need for pre-annotations. Traditional ECPE solutions divide the extracting emotions and causes operation into two separate parts. However, separating the bidirectional dependence between emotion and cause may lose a lot of potentially useful information. In this paper, we propose a novel interactive recurrent attention network(IRAN). Our approach focuses on the bidirectional impact between emotions and causes, and extracts emotions and causes simultaneously. The information in the document can be fully exploited through multiple modeling and information extraction. Our emotion-specific transformation and distance fusion correlation can adaptively focus on the emotions and the distance, gracefully incorporate them into a distinguishable neural network attention framework. The experimental results show that our proposed model achieves better performance than other widely-used models on the ECPE corpus.

关键词： Natural language processing Deep learning Sentiment analysis Text mining Emotion-cause pair extraction

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A simple model for the energy-efficient optimal real-time multiprocessor scheduling

A simple model for the energy-efficient optimal real-time mu...

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2012 IEEE International Conference on Computer science and Automation Engineering, CSAE 2012

作者： Wu, Fei Jin, Shiyao Wang, Yuming College of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai 201620 China National Laboratory of Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China

ISBN: (纸本)9781467300865

As the energy consumption of embedded multiprocessor systems becomes increasingly prominent, it becomes an urgent problem of real-time energy-efficient scheduling in multiprocessor systems to reduce system energy consumption while meeting real-time constraints. In this paper, we introduce a unifying energy-efficient real-time scheduling theory with the Deadline Partitioning Fair Dynamic Voltage Frequency Scaling (DPF-DVFS) policy for multiprocessor systems. The proposed model can offer a unifying, extendable, on-line and simple method to realize energy-efficient real-time scheduling with low scheduling overhead. We then present an optimal energy-efficient scheduling algorithm DPF-DVFS-WRAP for periodic tasks. © 2012 IEEE.

关键词： Real time systems

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Learning from Wireless: a Prospective Approach to Human-Centered Computing

Learning from Wireless: a Prospective Approach to Human-Cent...

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International Joint Conference on Neural Networks

作者： Yong Lu Shaohe Lv Xue Yang Xiaodong Wang Minne Li Xingming Zhou Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology

ISBN: (纸本)9781509006212

One of the most important topics in Human-Centered Computing (HCC) is to recognise human's activities. In this paper, the technology of wireless-based activity recognition is introduced. By using wireless signals, one can achieve Non-Line-Of-Sight (NLOS) recognition without carrying any devices. Also, it is easy to deploy a wireless-based recognition system due to the ubiquity of wireless communication systems. The basic idea is to detect different characteristics of signal propagation that correspond to the distinct human behaviors. As a result, action recognition is performed by analyzing the distinguishable features of signal propagation. This paper introduces the basic principles and applications of wireless-based activity recognition, and discusses the challenges and related performance metrics. Finally, open problems are discussed to point out the future research trends.

关键词： Human-centered computing wireless Wireless communication systems Activity recognition recognition system Performance metrics signals Learning Paper technology Human behavior

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