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

79 篇 会议
12 篇 期刊文献
1 册 图书

馆藏范围

92 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

52 篇 工学
- 48 篇 计算机科学与技术...
- 36 篇 软件工程
- 11 篇 信息与通信工程
- 10 篇 控制科学与工程
- 5 篇 电气工程
- 4 篇 电子科学与技术（可...
- 3 篇 动力工程及工程热...
- 2 篇 力学（可授工学、理...
- 2 篇 机械工程
- 2 篇 环境科学与工程（可...
- 2 篇 生物医学工程（可授...
- 2 篇 生物工程
- 1 篇 仪器科学与技术
19 篇 管理学
- 17 篇 管理科学与工程(可...
- 10 篇 工商管理
- 3 篇 图书情报与档案管...
16 篇 理学
- 8 篇 数学
- 3 篇 生物学
- 2 篇 物理学
- 2 篇 化学
- 2 篇 大气科学
- 1 篇 系统科学
- 1 篇 统计学（可授理学、...
4 篇 医学
- 2 篇 基础医学(可授医学...
- 2 篇 临床医学
- 2 篇 药学(可授医学、理...
3 篇 法学
- 2 篇 社会学
- 1 篇 法学
2 篇 经济学
- 2 篇 应用经济学
1 篇 教育学
- 1 篇 教育学
1 篇 艺术学

主题

13 篇 resource managem...
13 篇 quality of servi...
8 篇 computational mo...
8 篇 distributed comp...
6 篇 concurrent compu...
6 篇 time factors
5 篇 computer science
5 篇 grid computing
5 篇 interference
4 篇 parallel algorit...
4 篇 linux
4 篇 throughput
4 篇 processor schedu...
4 篇 benchmark testin...
3 篇 runtime
3 篇 data processing
3 篇 scheduling algor...
3 篇 application soft...
3 篇 containers
3 篇 process control

机构

5 篇 parallel and dis...
5 篇 department of co...
5 篇 parallel and dis...
5 篇 high performance...
5 篇 karlstad univers...
4 篇 school of comput...
4 篇 high performance...
4 篇 parallel and dis...
3 篇 school of scienc...
3 篇 high-performance...
3 篇 college of compu...
3 篇 school of comput...
3 篇 center for paral...
3 篇 the university o...
3 篇 center of high p...
3 篇 rmit university ...
3 篇 parallel and dis...
2 篇 parallel and dis...
2 篇 high-performance...
2 篇 center for distr...

作者

19 篇 albert y. zomaya
18 篇 li kuan-ching
18 篇 yang chao-tung
14 篇 zomaya albert y.
8 篇 javid taheri
8 篇 zahir tari
5 篇 kuan-ching li
5 篇 hsu ching-hsien
5 篇 m. reza hoseinyf...
5 篇 chao-tung yang
4 篇 p. banerjee
4 篇 tari zahir
4 篇 lee liang-teh
4 篇 hoseinyfarahabad...
4 篇 taheri javid
4 篇 li wei
4 篇 lee young choon
4 篇 young choon lee
4 篇 m.reza hoseinyfa...
3 篇 grunzke richard

语言

92 篇 英文

检索条件"机构=Center for Distributed and High Performance Computing"

共 92 条记录，以下是41-50 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：

A resource allocation controller for key-value data stores

A resource allocation controller for key-value data stores

引用

IEEE International Symposium on Network computing and Applications

作者： Young Ki Kim F. M. Reza Hoseiny Young Choon Lee Albert Y. Zomaya The University of Sydney Center for Distributed and High Performance Computing School of IT Australia The University of Sydney Sydney NSW AU Department of Computing Macquarie University Sydney Australia

Recent distributed key-value data stores, such as Aerospike are getting the momentum with ever-increasing need for large-scale real-time data processing. While these data stores can provide significantly improved performance, they still struggle to meet Quality of Service (QoS) during workload surges. In this paper, we address the problem of QoS-aware resource allocation for burst workloads in key-value data stores. To this end, we design a resource allocation controller, which enables each application to independently regulate the releases of its requests taking into account QoS. In particular, the proposed controller monitors the actual performance metrics of the target system and dynamically releases requests from a buffer owned by each application accordingly. We have implemented the proposed controller in an Aerospike cluster for our performance evaluation. Experiments have been conducted with various workload intensities (with up to 36,180 write operations per second) in comparison with the default Aerospike policy. Experimental results confirm that the proposed controller decreases the overall average latency up to 41% on high-rate workload while maintaining the QoS of high priority applications.

关键词： Quality of service Resource management Servers Time factors Delays Australia

来源：评论

学校读者我要写书评

暂无评论

Network Load Analysis and Provisioning of MapReduce Applications

Network Load Analysis and Provisioning of MapReduce Applicat...

引用

IEEE International Conference on Parallel and distributed computing, Applications and Technologies (PDCAT)

作者： Nikzad Babaii Rizvandi Javid Taheri Reza Moraveji Albert Y. Zomaya The University of Sydney Sydney NSW AU Center for Distributed and High Performance Computing School of Information Technologies University of Sydney Sydney Australia

ISBN: (纸本)9781467357043

In this paper, we study the dependency between MapReduce configuration parameters and network load of fixed-size MapReduce jobs during the shuffle phase, then we propose an analytical method to model this dependency. Our approach consists of three key phases: profiling, modeling, and prediction. In the first stage, an application is run several times with different sets of MapReduce configuration parameters (here number of map tasks and number of reduce tasks) to profile the network load of an application in the shuffle phase on a given cluster. Then, the relation between these parameters and the network load is modeled by multivariate linear regression. For evaluation, three applications (Word Count, Exim Main log parsing, and TeraSort) are utilized to evaluate our technique on a 5-node MapReduce private cluster.

关键词： Load modeling Predictive models Accuracy Computational modeling Mathematical model distributed computing Data models

来源：评论

学校读者我要写书评

暂无评论

An efficient parallel loop self-scheduling on grid environments

引用

IFIP International Conference on Network and Parallel computing, NPC 2004

作者： Yang, Chao-Tung Cheng, Kuan-Wei Li, Kuan-Ching High Performance Computing Laboratory Dept. of Computer Science and Information Engineering Tunghai University Taichung407 Taiwan Parallel and Distributed Processing Center Dept. of Computer Science and Information Management Providence University Shalu Taichung433 Taiwan

ISBN: (纸本)3540233881

The approaches to deal with scheduling and load balancing on PC-based cluster systems are famous and well-known. Self-scheduling schemes, which are suitable for parallel loops with independent iterations on cluster computer system, they have been designed in the past. In this paper, we propose a new scheme that can adjust the scheduling parameter dynamically on an extremely heterogeneous PC-based cluster and grid computing environments in order to improve system performance. A grid computing environment consists of multiple PC-based clusters is constructed using Globus Toolkit and SUN Grid Engine middleware. The experimental results show that our scheduling can result in higher performance than other similar schemes. © IFIP International Federation for Information Processing 2004.

关键词： Scheduling

来源：评论

学校读者我要写书评

暂无评论

Towards Understanding Optimal Load-Balancing of Heterogeneous Short-Range Molecular Dynamics

Towards Understanding Optimal Load-Balancing of Heterogeneou...

引用

IEEE International Conference on high performance computing Workshops (HiPCW)

作者： Steffen Hirschmann Dirk Pflüger Colin W. Glass Institute for Parallel and Distributed Systems University of Stuttgart Stuttgart Germany High Performance Computing Center Stuttgart University of Stuttgart Stuttgart Germany

ISBN: (纸本)9781509057740

For heterogeneous dynamic short-range molecular dynamics simulations it is critical to employ suitable load-balancing methods to minimize the time to solution. However, designing and parametrizing the optimal load-balancing method is a complex task, depending on detailed properties of the simulation *** main challenge in balancing the load of molecular dynamics simulations is the extreme difference in load density for scenarios with heterogeneous particle density, which can easily reach 4-6 orders of magnitude. Therefore, heterogeneity is deemed to be a relevant property and a suitable metric to reliably quantifying heterogeneity is formulated. This metric, which is based on binning particles and evaluating statistical moments, is then applied to example scenarios and correlated to the performance of five load balancing methodologies. Furthermore, how rapidly the load varies over time will determine how long the benefits of a specific partitioning are expected to last. We deem this to be another relevant property, the dynamics, and introduce corresponding metrics. The results indicate that these metric are useful to differentiate between scenarios and facilitate reasoning over the complex relationship between particle simulation scenarios and optimal load balancing methods. This work is a first step towards understanding this relationship, while introducing key concepts we regard as a crucial for this understanding.

关键词： Load modeling Computational modeling Measurement Dynamics Force Partitioning algorithms Load management

来源：评论

学校读者我要写书评

暂无评论

Spark-Tuner: An Elastic Auto-Tuner for Apache Spark Streaming

Spark-Tuner: An Elastic Auto-Tuner for Apache Spark Streamin...

引用

IEEE International Conference on Cloud computing, CLOUD

作者： M.Reza HoseinyFarahabady Javid Taheri Albert Y. Zomaya Zahir Tari The University of Sydney School of Computer Science Center for Distributed and High Performance Computing Karlstad University Sweden RMIT University School of Science Australia

ISBN: (数字)9781728187808

ISBN: (纸本)9781728187815

Spark has emerged as one of the most widely and successfully used data analytical engine for large-scale enterprise, mainly due to its unique characteristics that facilitate computations to be scaled out in a distributed environment. This paper deals with the performance degradation due to resource contention among collocated analytical applications with different priority and dissimilar intrinsic characteristics in a shared Spark platform. We propose an auto-tuning strategy of computing resources in a distributed Spark platform for handling scenarios in which submitted analytical applications have different quality of service (QoS) requirements (e.g., latency constraints), while the interference among computing resources is considered as a key performance-limiting parameter. We compared Spark-Tuner to two widely used resource allocation heuristics in a large scale Spark cluster through extensive experimental settings across several traffic patterns with uncertain rate and application types. Experimental results show that with Spark-Tuner, the Spark engine can decrease the p-99 latency of high priority applications by 43% during the high-rate traffic periods, while maintaining the same level of CPU throughput across a cluster.

关键词： Cloud computing Tuners Quality of service Traffic control Sparks Resource management Engines

来源：评论

学校读者我要写书评

暂无评论

QSpark: distributed Execution of Batch & Streaming Analytics in Spark Platform

QSpark: Distributed Execution of Batch & Streaming Analytics...

引用

IEEE International Symposium on Network computing and Applications

作者： M.Reza HoseinyFarahabady Javid Taheri Albert Y. Zomaya Zahir Tari The University of Sydney School of Computer Science Center for Distributed and High Performance Computing NSW Australia Karlstad University Sweden RMIT University School of Computing Technologies Victoria Australia

ISBN: (纸本)9781665495516

A significant portion of research work in the past decade has been devoted on developing resource allocation and task scheduling solutions for large-scale data processing platforms. Such algorithms are designed to facilitate deployment of data analytic applications across either conventional cluster computing systems or modern virtualized data-centers. The main reason for such a huge research effort stems from the fact that even a slight improvement in the performance of such platforms can bring a considerable monetary savings for vendors, especially for modern data processing engines that are designed solely to perform high throughput or/and low-latency computations over massive-scale batch or streaming data. A challenging question to be yet answered in such a context is to design an effective resource allocation solution that can prevent low resource utilization while meeting the enforced performance level (such as 99-th latency percentile) in circumstances where contention among applications to obtain the capacity of shared resources is a non negligible performance-limiting parameter. This paper proposes a resource controller system, called QSpark, to cope with the problem of (i) low performance (i.e., resource utilization in the batch mode and p-99 response time in the streaming mode), and (ii) the shared resource interference among collocated applications in a multi-tenancy modern Spark platform. The proposed solution leverages a set of controlling mechanisms for dynamic partitioning of the allocation of computing resources, in a way that it can fulfill the QoS re-quirements of latency-critical data processing applications, while enhancing the throughput for all working nodes without reaching their saturation points. Through extensive experiments in our in-house Spark cluster, we compared the achieved performance of proposed solution against the default Spark resource allocation policy for a variety of Machine Learning (ML), Artificial Intelligence (AI), and De

关键词： Processor scheduling Quality of service Interference Throughput Resource management Sparks Time factors

来源：评论

学校读者我要写书评

暂无评论

Data-Intensive Workload Consolidation in Serverless (Lambda/FaaS) Platforms

Data-Intensive Workload Consolidation in Serverless (Lambda/...

引用

IEEE International Symposium on Network computing and Applications

作者： M.Reza HoseinyFarahabady Javid Taheri Albert Y. Zomaya Zahir Tari The University of Sydney School of Computer Science Center for Distributed and High Performance Computing NSW Australia Karlstad University Sweden RMIT University School of Computing Technologies Victoria Australia

ISBN: (纸本)9781665495516

A significant amount of research studies in the past years has been devoted on developing efficient mechanisms to control the level of degradation among consolidate workloads in a shared platform. Workload consolidation is a promising feature that is employed by most service providers to reduce the total operating costs in traditional computing systems [1]–[3]. Serverless paradigm - also known as Function as a Service, FaaS, and Lambda - recently emerged as a new virtualization run-time model that disentangles the traditional state of applications' users from the burden of provisioning physical computing resources, leaving the difficulty of providing the adequate resource capacity on the service provider's side. This paper focuses on a number of challenges associated with workload consolidation when a serverless platform is expected to execute several data-intensive functional units. Each functional unit is considered to be the atomic component that reacts to a stream of input data. A serverless application in the proposed model is composed of a series of functional units. Through a systematic approach, we highlight the main challenges for devising an efficient workload consolidation process in a data-intensive serverless platform. To this end, we first study the performance interference among multiple workloads to obtain the capacity of last level cache (LLC). We show how such contention among workloads can lead to a significant throughput degradation on a single physical server. We expand our investigation into a general case with the aim to prevent the total throughput never falling below a predefined utilization level. Based on the empirical results, we develop a consolidation model and then design a computationally efficient controller to optimize the throughput degradation among a platform consists fs multiple machines. The performance evaluation is conducted using modern workloads inspired by data management services, and data analytic benchmark tools in our

关键词： Degradation performance evaluation Data analysis Computational modeling Quality of service Interference Benchmark testing

来源：评论

学校读者我要写书评

暂无评论

A recursive-adjustment co-allocation scheme in data grid environments 1

引用

6th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP

作者： Yang, Chao-Tung Yang, I.-Hsien Li, Kuan-Ching Hsu, Ching-Hsien High Performance Computing Laboratory Department of Computer Science and Information Engineering Tunghai University Taichung 40704 Taiwan Parallel and Distributed Processing Center Department of Computer Science and Information Management Providence University Taichung 43301 Taiwan Department of Computer Science and Information Engineering Chung Hua University Hsinchu 300 Taiwan

ISBN: (数字)9783540320715

ISBN: (纸本)3540292357

The co-allocation architecture was developed in order to enable parallel downloads of datasets from multiple servers. Several co-allocation strategies have been coupled and used to exploit rate differences among various client-server links and to address dynamic rate fluctuations by dividing files into multiple blocks of equal sizes. However, a major obstacle, the idle time of faster servers having to wait for the slowest server to deliver the final block, makes it important to reduce differences in finish time among replica servers. In this paper, we propose a dynamic co-allocation scheme, namely Recursive-Adjustment Co-Allocation scheme, to improve the performance of data transfer in Data Grids. Our approach reduces the idle time spent waiting for the slowest server and decreases data transfer completion time. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Parallel processing systems

来源：评论

学校读者我要写书评

暂无评论

performance analysis of applying replica selection technology for Data Grid environments

Performance analysis of applying replica selection technolog...

引用

8th International Conference on Parallel computing Technologies, PaCT 2005

作者： Yang, Chao-Tung Chen, Chun-Hsiang Li, Kuan-Ching Hsu, Ching-Hsien High-Performance Computing Laboratory Department of Computer Science and Information Engineering Tunghai University Taichung 40704 Taiwan Parallel and Distributed Processing Center Department of Computer Science and Information Management Providence University Taichung 43301 Taiwan Department of Computer Science and Information Engineering Chung Hua University Hsinchu 300 Taiwan

The Data Grid enables the sharing, selection, and connection of a wide variety of geographically distributed computational and storage resources for solving large-scale data intensive scientific applications. Such technology efficiently manage and transfer terabytes or even petabytes of data for data-intensive, high-performance computing applications in wide-area, distributed computing environments. Replica selection process allows an application to choose a replica from replica catalog, based on its performance and data access features. In this paper, we build a Grid environment based on three existing PC Cluster environments and perform performance analysis of data transfers using GridFTP protocol over these systems. In addition, based on experimental results, it is proposed a cost model to pick the best replica, in real and dynamic network situations. © Springer-Verlag Berlin Heidelberg 2005.

关键词： distributed computer systems

来源：评论

学校读者我要写书评

暂无评论

A chronological history-based execution time estimation model for embarrassingly parallel applications on grids

A chronological history-based execution time estimation mode...

引用

3rd International Symposium on Parallel and distributed Processing and Applications, ISPA 2005

作者： Yang, Chao-Tung Shih, Po-Chi Lin, Cheng-Fang Hsu, Ching-Hsien Li, Kuan-Ching High Performance Computing Laboratory Department of Computer Science and Information Engineering Tunghai University Taichung 40704 Taiwan Department of Computer Science and Information Engineering Chung Hua University Hsinchu 300 Taiwan Parallel and Distributed Processing Center Department of Computer Science and Information Management Providence University Taichung 43301 Taiwan

ISBN: (纸本)3540297693

In order to identify and schedule jobs that are suitable for determined resources, an execution time estimation model is required. In this paper, it is described a Chronological history-based execution time estimation model to predict current execution time, according to the previous execution results. We built a heterogeneous computational Grid environment using Globus Toolkit, and our research is focused in Grid computing environments and to execute parallel jobs on multiple resources by measuring its accuracy. The experimental results shown that our model can accurately predict the execution time of embarrassingly parallel applications. © Springer-Verlag Berlin Heidelberg 2005.

关键词： distributed computer systems

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10页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：