检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

402 篇 会议
172 篇 期刊文献
7 册 图书

馆藏范围

581 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

316 篇 工学
- 275 篇 计算机科学与技术...
- 184 篇 软件工程
- 51 篇 信息与通信工程
- 31 篇 生物工程
- 27 篇 电气工程
- 26 篇 控制科学与工程
- 24 篇 电子科学与技术（可...
- 21 篇 网络空间安全
- 16 篇 动力工程及工程热...
- 14 篇 机械工程
- 13 篇 交通运输工程
- 9 篇 安全科学与工程
- 7 篇 光学工程
- 7 篇 生物医学工程（可授...
- 6 篇 仪器科学与技术
- 6 篇 化学工程与技术
110 篇 理学
- 65 篇 数学
- 33 篇 生物学
- 16 篇 统计学（可授理学、...
- 15 篇 系统科学
- 10 篇 化学
- 8 篇 物理学
96 篇 管理学
- 74 篇 管理科学与工程(可...
- 32 篇 图书情报与档案管...
- 24 篇 工商管理
13 篇 法学
- 7 篇 社会学
- 6 篇 法学
8 篇 经济学
- 7 篇 应用经济学
5 篇 医学
- 5 篇 基础医学(可授医学...
4 篇 文学
- 4 篇 新闻传播学
2 篇 农学
1 篇 军事学
1 篇 艺术学

主题

53 篇 grid computing
33 篇 virtual machinin...
25 篇 computer science
25 篇 peer to peer com...
25 篇 computational mo...
24 篇 bandwidth
24 篇 servers
21 篇 scalability
20 篇 computers
20 篇 cloud computing
19 篇 resource managem...
19 篇 hardware
18 篇 kernel
17 篇 operating system...
17 篇 computer archite...
16 篇 application soft...
16 篇 protocols
15 篇 memory managemen...
15 篇 virtual machine ...
14 篇 semantics

机构

102 篇 national enginee...
71 篇 services computi...
59 篇 school of comput...
52 篇 services computi...
45 篇 school of cyber ...
29 篇 national enginee...
27 篇 cluster and grid...
26 篇 huazhong univers...
26 篇 hubei engineerin...
25 篇 services computi...
23 篇 hubei key labora...
19 篇 zhejiang lab
17 篇 school of softwa...
14 篇 school of inform...
13 篇 services computi...
13 篇 huazhong univers...
12 篇 national enginee...
12 篇 national enginee...
11 篇 services computi...
11 篇 service computin...

作者

300 篇 hai jin
199 篇 jin hai
77 篇 xiaofei liao
47 篇 deqing zou
45 篇 liao xiaofei
37 篇 song wu
27 篇 long zheng
26 篇 zou deqing
24 篇 haikun liu
24 篇 shi xuanhua
22 篇 ran zheng
21 篇 xuanhua shi
21 篇 wu song
19 篇 hu shengshan
18 篇 yu zhang
16 篇 xia xie
16 篇 qin zhang
16 篇 yu huang
15 篇 jiang xiao
14 篇 liu haikun

语言

546 篇 英文
25 篇 其他
11 篇 中文

检索条件"机构=China Services Computing Technology and System Lab and Cluster and Grid"

共 581 条记录，以下是191-200 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Efficient Hardware Redo Logging for Secure Persistent Memory 23

Efficient Hardware Redo Logging for Secure Persistent Memory

引用

23rd IEEE International Conference on High Performance computing and Communications, 7th IEEE International Conference on Data Science and systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Zhang, Zhan Yue, Jianhui Liao, Xiaofei Jin, Hai School of Computer Science and Technology Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Wuhan China Michigan Technological University Computer Science Department HoughtonMI United States

ISBN: (纸本)9781665494571

Write-ahead log and data encryption technologies are employed to ensure both crash consistency and data security for persistent memory (PM). The encryption/decryption of both data and log requests increase the memory request latency, degrading the system performance. To tackle this issue, this paper proposes a novel log-aware memory encryption scheme to reduce encryption/decryption operations, without compromising data security. Specifically, redo log blocks are stored as ciphertext for their corresponding data blocks to avoid encryption/decryption of log data blocks when in-place update operations are applied to them. We further design a compact log record layout with fewer encryption metadata in log records to reduce logging traffic. Our simulation results show that the transaction throughput of the proposed design outperforms the baseline design and the state-of-the-art design by 87.3% and 55.3% on average, respectively. © 2021 IEEE.

关键词： Smart cities system performance Simulation Memory management Layout Memory architecture Metadata

来源：评论

学校读者我要写书评

暂无评论

grid and Cooperative computing - GCC 2004 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Hai Jin Yi Pan Nong Xiao Jianhua Sun

ISBN: (数字)9783540302087

ISBN: (纸本)9783540235644

Welcome to the proceedings of GCC2004 and the city of Wuhan. grid computing has become a mainstream research area in computer science and the GCC conference has become one of the premier forums for presentation of new and exciting research in all aspectsofgridandcooperativecomputing. Theprogramcommitteeispleasedtopresent the proceedings of the 3rd International Conference on grid and Cooperative Comp- ing (GCC2004), which comprises a collection of excellent technical papers, posters, workshops, and keynote speeches. The papers accepted cover a wide range of exciting topics, including resource grid and service grid, information grid and knowledge grid, grid monitoring,managementand organizationtools, grid portal, grid service, Web s- vices and their QoS, service orchestration, grid middleware and toolkits, software glue technologies, grid security, innovative grid applications, advanced resource reservation andscheduling,performanceevaluationandmodeling,computer-supportedcooperative work, P2P computing, automatic computing, and meta-information management. The conference continues to grow and this year a record total of 581 manuscripts (including workshop submissions) were submitted for consideration. Expecting this growth, the size of the program committee was increased from 50 members for GCC 2003 for 70 in GCC 2004. Relevant differences from previous editions of the conf- ence: it is worth mentioning a signi?cant increase in the number of papers submitted by authors from outside china; and the acceptance rate was much lower than for p- vious GCC conferences. From the 427 papers submitted to the main conference, the program committee selected only 96 regular papers for oral presentation and 62 short papers for poster presentation in the program.

关键词： Theory of Computation Computer Communication Networks Operating systems Software Engineering Information systems Applications (incl. Internet) Information Storage and Retrieval

来源：评论

学校读者我要写书评

暂无评论

xGCN: An Extreme Graph Convolutional Network for Large-scale Social Link Prediction 23

xGCN: An Extreme Graph Convolutional Network for Large-scale...

引用

2023 World Wide Web Conference, WWW 2023

作者： Song, Xiran Lian, Jianxun Huang, Hong Luo, Zihan Zhou, Wei Lin, Xue Wu, Mingqi Li, Chaozhuo Xie, Xing Jin, Hai Huazhong University of Science and Technology Wuhan China Microsoft Research Asia Beijing China Microsoft Gaming Redmond United States National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9781450394161

Graph neural networks (GNNs) have seen widespread usage across multiple real-world applications, yet in transductive learning, they still face challenges in accuracy, efficiency, and scalability, due to the extensive number of trainable parameters in the embedding table and the paradigm of stacking neighborhood aggregations. This paper presents a novel model called xGCN for large-scale network embedding, which is a practical solution for link predictions. xGCN addresses these issues by encoding graph-structure data in an extreme convolutional manner, and has the potential to push the performance of network embedding-based link predictions to a new record. Specifically, instead of assigning each node with a directly learnable embedding vector, xGCN regards node embeddings as static features. It uses a propagation operation to smooth node embeddings and relies on a Refinement neural Network (RefNet) to transform the coarse embeddings derived from the unsupervised propagation into new ones that optimize a training objective. The output of RefNet, which are well-refined embeddings, will replace the original node embeddings. This process is repeated iteratively until the model converges to a satisfying status. Experiments on three social network datasets with link prediction tasks show that xGCN not only achieves the best accuracy compared with a series of competitive baselines but also is highly efficient and scalable. © 2023 ACM.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

FedEdge: Accelerating Edge-Assisted Federated Learning 23

FedEdge: Accelerating Edge-Assisted Federated Learning

引用

2023 World Wide Web Conference, WWW 2023

作者： Wang, Kaibin He, Qiang Chen, Feifei Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Federated learning (FL) has been widely acknowledged as a promising solution to training machine learning (ML) model training with privacy preservation. To reduce the traffic overheads incurred by FL systems, edge servers have been included between clients and the parameter server to aggregate clients' local models. Recent studies on this edge-assisted hierarchical FL scheme have focused on ensuring or accelerating model convergence by coping with various factors, e.g., uncertain network conditions, unreliable clients, heterogeneous compute resources, etc. This paper presents our three new discoveries of the edge-assisted hierarchical FL scheme: 1) it wastes significant time during its two-phase training rounds;2) it does not recognize or utilize model diversity when producing a global model;and 3) it is vulnerable to model poisoning attacks. To overcome these drawbacks, we propose FedEdge, a novel edge-assisted hierarchical FL scheme that accelerates model training with asynchronous local federated training and adaptive model aggregation. Extensive experiments are conducted on two widely-used public datasets. The results demonstrate that, compared with state-of-the-art FL schemes, FedEdge accelerates model convergence by 1.14 × -3.20 ×, and improves model accuracy by 2.14% - 6.63%. © 2023 ACM.

关键词： Edge computing

来源：评论

学校读者我要写书评

暂无评论

Optimizing data shuffling in data-parallel computation by understanding user-defined functions 9

Optimizing data shuffling in data-parallel computation by un...

引用

9th USENIX Symposium on Networked systems Design and Implementation, NSDI 2012

作者： Zhang, Jiaxing Zhou, Hucheng Chen, Rishan Fan, Xuepeng Guo, Zhenyu Lin, Haoxiang Li, Jack Y. Lin, Wei Zhou, Jingren Zhou, Lidong Microsoft Research Asia China Microsoft Bing United States Peking University China Services Computing Technologyand System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Georgia Institute of Technology United States

Map/Reduce style data-parallel computation is characterized by the extensive use of user-defined functions for data processing and relies on data-shuffling stages to prepare data partitions for parallel computation. Instead of treating user-defined functions as "black boxes", we propose to analyze those functions to turn them into "gray boxes" that expose opportunities to optimize data shuffling. We identify useful functional properties for userdefined functions, and propose SUDO, an optimization framework that reasons about data-partition properties, functional properties, and data shuffling. We have assessed this optimization opportunity on over 10,000 dataparallel programs used in production SCOPE clusters, and designed a framework that is incorporated it into the production system. Experiments with real SCOPE programs on real production data have shown that this optimization can save up to 47% in terms of disk and network I/O for shuffling, and up to 48% in terms of cross-pod network traffic. © 2012 by The USENIX Association. All Rights Reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Autonomous Agent Based Intrusion Detection in Virtual computing Environment

Autonomous Agent Based Intrusion Detection in Virtual Comput...

引用

2010 IEEE International Conference Conferenhce on Wireless Communications,Networking and Information Security(2010 IEEE 无线通信、网络技术与信息安全国际会议 WCNIS)

作者： Feng Zhao Alex K. Ohoussou Deqing Zou Hai Jin Guofu Xiang Ge Cheng Cluster and Grid Computing Lab Services Computing Technology and System Lab Huazhong University of S Cluster and Grid Computing Lab Services Computing Technology and System Lab Huazhong University of S

One of the motivations for virtualization technology is the desire to develop new services to enhance system security without trusting both the applications and the operating systems. An intrusion detection system is an example of such service that can help to isolate users from malicious attacks. In this paper, we propose hybrid-based intrusion detection architecture in virtual computing environment to detect and isolate harmful behaviors by real-time monitoring and alarming. In contrast to monolithic intrusion detection system, we introduce autonomous agents, acting independently of each other, to monitor the system. The agents are deployed in virtual machines to analyze actions occurring on the network and inside the hosts to determine whether they are potential security violations or not. Our architecture is implemented based on Xen, and the detection management center is deployed in a secure virtual machine.

关键词： intrusion detection autonomous agent virtual computing environment

来源：评论

学校读者我要写书评

暂无评论

Optimization of asynchronous graph processing on GPU with hybrid coloring model 2015

Optimization of asynchronous graph processing on GPU with hy...

引用

20th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2015

作者： Shi, Xuanhua Liang, Junling Di, Sheng He, Bingsheng Jin, Hai Lu, Lu Wang, Zhixiang Luo, Xuan Zhong, Jianlong Services Computing Technology and System Lab. Cluster and Grid Computing Lab. School of Computer Science and Technology Huazhong University of Science and Technology China Argonne National Laboratory United States School of Computer Engineering Nanyang Technological University Singapore Singapore

ISBN: (纸本)9781450332057

Modern GPUs have been widely used to accelerate the graph processing for complicated computational problems regarding graph theory. Many parallel graph algorithms adopt the asynchronous computing model to accelerate the iterative convergence. Unfortunately, the consistent asynchronous computing requires locking or the atomic operations, leading to significant penalties/overheads when implemented on GPUs. To this end, coloring algorithm is adopted to separate the vertices with potential updating conflicts, guaranteeing the consistency/correctness of the parallel processing. We propose a light-weight asynchronous processing framework called Frog with a hybrid coloring model. We find that majority of vertices (about 80%) are colored with only a few colors, such that they can be read and updated in a very high degree of parallelism without violating the sequential consistency. Accordingly, our solution will separate the processing of the vertices based on the distribution of colors.

关键词： Graphics processing unit

来源：评论

学校读者我要写书评

暂无评论

EdgeMove: Pipelining Device-Edge Model Training for Mobile Intelligence 23

EdgeMove: Pipelining Device-Edge Model Training for Mobile I...

引用

2023 World Wide Web Conference, WWW 2023

作者： Dong, Zeqian He, Qiang Chen, Feifei Jin, Hai Gu, Tao Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia School of Computing Macquarie University Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Training machine learning (ML) models on mobile and Web-of-Things (WoT) has been widely acknowledged and employed as a promising solution to privacy-preserving ML. However, these end-devices often suffer from constrained resources and fail to accommodate increasingly large ML models that crave great computation power. Offloading ML models partially to the cloud for training strikes a trade-off between privacy preservation and resource requirements. However, device-cloud training creates communication overheads that delay model training tremendously. This paper presents EdgeMove, the first device-edge training scheme that enables fast pipelined model training across edge devices and edge servers. It employs probing-based mechanisms to tackle the new challenges raised by device-edge training. Before training begins, it probes nearby edge servers' training performance and bootstraps model training by constructing a training pipeline with an approximate model partitioning. During the training process, EdgeMove accommodates user mobility and system dynamics by probing nearby edge servers' training performance adaptively and adapting the training pipeline proactively. Extensive experiments are conducted with two popular DNN models trained on four datasets for three ML tasks. The results demonstrate that EdgeMove achieves a 1.3 × -2.1 × speedup over the state-of-the-art scheme. © 2023 ACM.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Minimum Spanning Tree clustering Based on Density Filtering 7th

Minimum Spanning Tree Clustering Based on Density Filtering

引用

7th CCF Academic Conference on BigData, CCF BigData 2019

作者： Wang, Ke Xie, Xia Sun, Jiayu Cao, Wenzhi National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Hunan University of Technology and Business Changsha410205 China

ISBN: (纸本)9789811518980

clustering analysis is an important method in data mining. In order to recognize clusters with arbitrary shapes as well as clusters with different density, we propose a new clustering approach: minimum spanning tree clustering based on density filtering. It masks the low-density points in the density filtering step, which reduces the interference of noise and makes the gap between clusters clearer. It uses relative values of adjacent distances to find mutations of density and changes between clusters to divide data sets. It is tested on multiple synthetic data sets and real-world data sets, the results of which show that the algorithm is able to detect clusters with arbitrary shape and it is insensitive to the imbalance of density between clusters. It has achieved great results on multiple data sets. © Springer Nature Singapore Pte Ltd 2019.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

N-Docker: A NVM-HDD Hybrid Docker Storage Framework to Improve Docker Performance 16th

N-Docker: A NVM-HDD Hybrid Docker Storage Framework to Impro...

引用

16th IFIP WG 10.3 International Conference on Network and Parallel computing, NPC 2019

作者： Gu, Lin Tang, Qizhi Wu, Song Jin, Hai Zhang, Yingxi Shi, Guoqiang Lin, Tingyu Rao, Jia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China State Key Laboratory of Intelligent Manufacturing System Technology Beijing100854 China The University of Texas at Arlington ArlingtonTX76019 United States

ISBN: (纸本)9783030307080

Docker has been widely adopted in production environment, but unfortunately deployment and cold-start of container are limited by the low speed of disk. The emerging non-volatile memory (NVM) technology, which has high speed and can store data permanently, brings a new chance to accelerate the deployment and cold-start of container. However, it is expensive to replace the whole hard disk driver (HDD) with NVM. To achieve the fastest deployment and cold-start with lowest cost, we conduct in-depth analysis on the Top-134 images in Docker Hub and obtain two main insights as: (1) the storing latency of layered image has become the bottleneck of container deployment;(2) only a few image layers are required for container cold-start. Based on these two findings, we propose a NVM-HDD hybrid docker storage framework as N-Docker. It can effectively accelerate container cold-start by detecting the bottleneck layers as well as cold-start required layers and storing them into NVM for faster container startup with limited NVM capacity. Experimental results show that N-Docker can accelerate the container deployment by 1.21X and cold-start by 2.96X. Compared to NVM-Docker, which stores all images into NVM, N-Docker achieves the same performance improvements while reducing the usage of NVM by 88.22%. © 2019, IFIP International Federation for Information Processing.

关键词： Containers

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共59页 << < 16 17 18 19 20 21 22 23 24 25 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：