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IEEE International Conference on Cluster computing (CLUSTER)

作者： Bicer, Tekin Chiu, David Agrawal, Gagan Ohio State Univ Columbus OH 43210 USA Washington State Univ Engn & Comp Sci Pullman WA 99164 USA

ISBN: (纸本)9780769545165

For many organizations, one attractive use of cloud resources can be through what is referred to as cloud bursting or the hybrid cloud. These refer to scenarios where an organization acquires and manages in-house resources to meet its base need, but can use additional resources from a cloud provider to maintain an acceptable response time during workload peaks. Cloud bursting has so far been discussed in the context of using additional computing resources from a cloud provider. However, as next generation applications are expected to see orders of magnitude increase in data set sizes, cloud resources can be used to store additional data after local resources are exhausted. In this paper, we consider the challenge of data analysis in a scenario where data is stored across a local cluster and cloud resources. We describe a software framework to enable data-intensive computing with cloud bursting, i.e., using a combination of compute resources from a local cluster and a cloud environment to perform Map-Reduce type processing on a data set that is geographically distributed. Our evaluation with three different applications shows that data-intensive computing with cloud bursting is feasible and scalable. Particularly, as compared to a situation where the data set is stored at one location and processed using resources at that end, the average slowdown of our system ( using distributed but the same aggregate number of compute resources), is only 15.55%. Thus, the overheads due to global reduction, remote data retrieval, and potential load imbalance are quite manageable. Our system scales with an average speedup of 81% when the number of compute resources is doubled.

关键词： Cloud bursting Cloud computing data processing data-intensive computing Distributed databases Map-reduce Organizations Synchronization Throughput

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Applying idealized lower-bound runtime models to understand inefficiencies in data-intensive computing 11

Applying idealized lower-bound runtime models to understand ...

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2011 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS'11

作者： Krevat, Elie Shiran, Tomer Anderson, Eric Tucek, Joseph Wylie, Jay J. Ganger, Gregory R. Carnegie Mellon University United States HP Labs United States

No abstract available.

ISBN: (纸本)9781450302623

No abstract available.

关键词： cloud computing efficiency data-intensive computing performance

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Memristor-based ternary content addressable memory (mTCAM) for data-intensive computing

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SEMICONDUCTOR SCIENCE AND TECHNOLOGY 2014年第10期29卷 104010-104010页

作者： Zheng, Le Shin, Sangho Kang, Sung-Mo Steve Univ Calif Santa Cruz Jack Baskin Sch Engn Santa Cruz CA 95064 USA Korea Adv Inst Sci & Technol Taejon 305701 South Korea

A memristor-based ternary content addressable memory (mTCAM) is presented. Each mTCAM cell, consisting of five transistors and two memristors to store and search for ternary data, is capable of remarkable nonvolatility and higher storage density than conventional CMOS-based TCAMs. Each memristor in the cell can be programmed individually such that high impedance is always present between searchlines to reduce static energy consumption. A unique two-step write scheme offers reliable and energy-efficient write operations. The search voltage is designed to ensure optimum sensing margins with the presence of variations in memristor devices. Simulations of the proposed mTCAM demonstrate functionalities in write and search modes, as well as a search delay of 2 ns and a search of 0.99 fJ/bit/search for a word width of 128 bits.

关键词： memristor ternary content addressable memory data-intensive computing associative memory

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Improvement of job completion time in data-intensive cloud computing applications

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JOURNAL OF CLOUD computing-ADVANCES SYSTEMS AND APPLICATIONS 2020年第1期9卷 1-20页

作者： Ibrahim, Ibrahim Adel Bassiouni, Mostafa Univ Cent Florida Dept Comp Engn Orlando FL 32816 USA Univ Technol Baghdad Dept Comp Engn Baghdad Iraq Univ Cent Florida Dept Comp Sci Orlando FL 32816 USA

Task stragglers in MapReduce jobs dramatically impede job execution of data-intensive computing in cloud data centers. This impedance is due to the uneven distribution of input data, heterogeneous data nodes, resource contention situations, and network configurations. data skew of intermediate data in MapReduce job causes delay failures due to the violation of job completion time. data-intensive computing frameworks, such as MapReduce or Hadoop YARN, employ HashPartitioner. This partitioner may cause intermediate data skew, which results in straggler reducers. In this paper, we strive to make Hadoop YARN more efficient in cloud environments. We present, a new partitioning scheme, called balanced data clusters partitioner (BDCP), to handle straggler Reduce tasks based on sampling of input data and feedback information about the current processing task. Our extensive experimental results show that BDCP can outperform the default Hadoop HashPartitioner and Range partitioner. BDCP can assist in straggler mitigation during reduce phase and minimize the job completion time in MapReduce jobs within data-intensive cloud computing.

关键词： Cloud computing MapReduce data-intensive computing Parallel and distributed processing Straggler reduce task Sampling

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dispel4py: A Python framework for data-intensive scientific computing

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INTERNATIONAL JOURNAL OF HIGH PERFORMANCE computing APPLICATIONS 2017年第4期31卷 316-334页

作者： Filguiera, Rosa Krause, Amrey Atkinson, Malcolm Klampanos, Iraklis Moreno, Alexander Univ Edinburgh Sch Informat eSci Edinburgh Midlothian Scotland Univ Edinburgh EPCC Edinburgh Midlothian Scotland Natl Ctr Sci Res Demokritos Athens Greece Georgia Inst Technol Sch Comp Sci Atlanta GA 30332 USA

This paper presents dispel4py, a new Python framework for describing abstract stream-based workflows for distributed data-intensive applications. These combine the familiarity of Python programming with the scalability of workflows. data streaming is used to gain performance, rapid prototyping and applicability to live observations. dispel4py enables scientists to focus on their scientific goals, avoiding distracting details and retaining flexibility over the computing infrastructure they use. The implementation, therefore, has to map dispel4py abstract workflows optimally onto target platforms chosen dynamically. We present four dispel4py mappings: Apache Storm, message-passing interface (MPI), multi-threading and sequential, showing two major benefits: a) smooth transitions from local development on a laptop to scalable execution for production work, and b) scalable enactment on significantly different distributed computing infrastructures. Three application domains are reported and measurements on multiple infrastructures show the optimisations achieved;they have provided demanding real applications and helped us develop effective training. The *** is an open-source project to which we invite participation. The effective mapping of dispel4py onto multiple target infrastructures demonstrates exploitation of data-intensive and high-performance computing (HPC) architectures and consistent scalability.

关键词： data-intensive computing e-infrastructures data streaming scientific workflows programming frameworks

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data-intensive Scalable computing for Scientific Applications

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computing IN SCIENCE & ENGINEERING 2011年第6期13卷 25-33页

作者： Bryant, Randal E. Carnegie Mellon Univ Sch Comp Sci Pittsburgh PA 15213 USA

Increasingly, scientific computing applications must accumulate and manage massive datasets, as well as perform sophisticated computations over these data. Such applications call for data-intensive scalable computer (... 详细信息

关键词： Computational modeling DISC data models data storage data-intensive computing database systems MapReduce Predictive models Programming Scientific computing data handling data intensive scalable computing e-Science parallel processing scientific applications scientific computing applications

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Towards building a data-intensive index for big data computing - A case study of Remote Sensing data processing

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INFORMATION SCIENCES 2015年 319卷 171-188页

作者： Ma, Yan Wang, Lizhe Liu, Peng Ranjan, Rajiv Chinese Acad Sci Inst Remote Sensing & Digital Earth Beijing 100864 Peoples R China

With the recent advances in Remote Sensing (RS) techniques, continuous Earth Observation is generating tremendous volume of RS data. The proliferation of RS data is revolutionizing the way in which RS data are processed and understood. data with higher dimensionality, as well as the increasing requirement for real-time processing capabilities, have also given rise to the challenging issue of "data-intensive (DI) computing". However, how to properly identify and qualify the DI issue remains a significant problem that is worth exploring. DI computing is a complex issue. While the huge data volume may be one of the reasons for this, some other factors could also be important. In this paper, we propose an empirical model (DIRS) of DI index to estimate RS applications. DIRS here is a novel empirical model (DIRS) that could quantify the DI issues in RS data processing with a normalized DI index. Through experimental analysis of the typical algorithms across the whole RS data processing flow, we identify the key factors that affect the DI issues mostly. Finally, combined with the empirical knowledge of domain experts, we formulate DIRS model to describe the correlations between the key factors and DI index. By virtue of experimental validation on more selected RS applications, we have found that DIRS model is an easy but promising approach. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Big data Parallel computing data-intensive computing Remote Sensing data processing

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A Decoupled Execution Paradigm for data-intensive High-End computing

A Decoupled Execution Paradigm for Data-Intensive High-End C...

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IEEE International Conference on Cluster computing

作者： Chen, Yong Chen, Chao Sun, Xian-He Gropp, William D. Thakur, Rajeev Texas Tech Univ Dept Comp Sci Lubbock TX 79409 USA Illinois Inst Technol Dept Comp Sci Chicago MA USA Univ Illinois Dept Comp Sci Urbana IL USA Argonne Natl Lab Math & Comp Sci Div Argonne IL USA

ISBN: (纸本)9781467324229

High-end computing (HEC) applications in critical areas of science and technology tend to be more and more data intensive. I/O has become a vital performance bottleneck of modern HEC practice. Conventional HEC execution paradigms, however, are computing-centric for computation intensive applications. They are designed to utilize memory and CPU performance and have inherent limitations in addressing the critical I/O bottleneck issues of HEC. In this study, we propose a decoupled execution paradigm (DEP) to address the challenging I/O bottleneck issues. DEP is the first paradigm enabling users to identify and handle data-intensive operations separately. It can significantly reduce costly data movement and is better than the existing execution paradigms for data-intensive applications. The initial experimental tests have confirmed its promising potential. Its data-centric architecture could have an impact in future HEC systems, programming models, and algorithms design and development.

关键词： decoupled execution paradigm high-end computing data-intensive computing storage

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A balanced scheduler with data reuse and replication for scientific workflows in cloud computing systems

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2017年 74卷 168-178页

作者： Casas, Israel Taheri, Javid Ranjan, Rajiv Wang, Lizhe Zomaya, Albert Y. Univ Sydney Sch Informat Technol Sydney NSW Australia Newcastle Univ Sch Comp Sci Newcastle Upon Tyne Tyne & Wear England CSIRO Data61 Canberra ACT Australia China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Karlstad Univ Dept Math & Comp Sci Karlstad Sweden

Cloud computing provides substantial opportunities to researchers who demand pay-as-you-go computing systems. Although cloud provider (e.g., Amazon Web Services) and application provider (e.g., biologists, physicists, and online gaming companies) both have specific performance requirements (e.g. application response time), it is the cloud scheduler's responsibility to map the application to underlying cloud resources. This article presents a Balanced and file Reuse Replication Scheduling (BaRRS) algorithm for cloud computing environments to optimally schedule scientific application workflows. BaRRS splits scientific workflows into multiple sub-workflows to balance system utilization via parallelization. It also exploits data reuse and replication techniques to optimize the amount of data that needs to be transferred among tasks at run-time. BaRRS analyzes the key application features (e.g., task execution times, dependency patterns and file sizes) of scientific workflows for adapting existing data reuse and replication techniques to cloud systems. Further, BaRRS performs a trade-off analysis to select the optimal solution based on two optimization constraints: execution time and monetary cost of running scientific workflows. BaRRS is compared with a state-of-the-art scheduling approach;experiments prove its superior performance. Experiments include four well known scientific workflows with different dependency patterns and data file sizes. Results were promising and also highlighted most critical factors affecting execution of scientific applications on clouds. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Cloud computing Scientific workflow Scheduling Virtual machine data-intensive computing Big data

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Remote sensing big data computing: Challenges and opportunities

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2015年 51卷 47-60页

作者： Ma, Yan Wu, Haiping Wang, Lizhe Huang, Bormin Ranjan, Rajiv Zomaya, Albert Jie, Wei Chinese Acad Sci Inst Remote Sensing & Digital Earth Beijing Peoples R China Tsinghua Univ Minist Educ Key Lab Earth Syst Modeling Beijing 100084 Peoples R China Tsinghua Univ Ctr Earth Syst Sci Beijing 100084 Peoples R China Univ Wisconsin Madison Ctr Space Sci & Engn Madison WI USA CSIRO Computat Informat Canberra ACT Australia Univ Sydney Sch Informat Technol Sydney NSW 2006 Australia West London Univ Sch Comp & Technol London England

As we have entered an era of high resolution earth observation, the RS data are undergoing an explosive growth. The proliferation of data also give rise to the increasing complexity of RS data, like the diversity and higher dimensionality characteristic of the data. RS data are regarded as RS "Big data". Fortunately, we are witness the coming technological leapfrogging. In this paper, we give a brief overview on the Big data and data-intensive problems, including the analysis of RS Big data, Big data challenges, current techniques and works for processing RS Big data. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Remote sensing data processing Big data data-intensive computing

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