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IEEE international symposium on Parallel and distributed Processing With applications

作者： Naksinehaboon, Nichamon Taerat, Narate Leangsuksun, Chokchai Chandler, Clayton F. Scott, Stephen L. College of Engineering and Science Louisiana Tech. University Ruston LA 71270 United States Computer Science and Mathematics Division Oak Ridge National Laboratory Oak Ridge TN 37831 United States

ISBN: (纸本)9780769541907

Rejuvenation is a technique expected to mitigate failures in HPC systems by replacing, repairing, or resetting system components. Because of the small overhead required by software rejuvenation, we primarily focus on OS/kernel rejuvenation. In this paper, we propose three rejuvenation scheduling techniques. Moreover, we investigate the claim that software rejuvenation prolongs failure times in HPC systems. Also, we compare the lost computing times of the checkpoint/restart mechanism with and without rejuvenation after each checkpoint. © 2010 IEEE.

关键词： Endocrinology

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Computational workload prediction for grid oriented industrial applications: The case of 3D-image rendering

Computational workload prediction for grid oriented industri...

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5th IEEE international symposium on Cluster computing and the Grid (CCGrid 2005)

作者： Litke, A Tserpes, K Varvarigou, T Natl Tech Univ Athens Dept Elect & Comp Engn Telecommun Lab Athens Greece

ISBN: (纸本)0780390741

Grids are typically used for solving large-scale resource and computing intensive problems in science, engineering, and commerce as they seem to be cost-effective for industrial users. In order to be able to meet this requirement the software modules developed should be designed to meet the requisites for commercial business processes on the Grid. In this paper we present a module for predicting computational workload of jobs assigned for execution on commercially exploited Grid infrastructures. The module aims to identify the complexity of a given job and predict the workload that it is going to stress on the Grid infrastructure. The prediction is achieved with the use of a trained Artificial Neural Network which has been implemented with the use of the open source software package Joone. The approach has been implemented and validated within the framework of GRIA IST project for a specific industrial based application namely, 3D image rendering. The evaluation of the approach showed very promising results not only for the adoption of an open source package in a commercial application but also concerning the accuracy of the prediction and the benefit that it can provide in Grids for business.

关键词： Industrial applications

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Analyzing the robustness of scheduling algorithms using divisible load theory on heterogeneous systems

Analyzing the robustness of scheduling algorithms using divi...

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2013 IEEE 12th international symposium on Parallel and distributed computing, ISPDC 2013

作者： Balasubramaniam, Mahadevan Banicescu, Ioana Ciorba, Florina M. Department of Computer Science and Engineering Mississippi State University MS United States Technische Universität Dresden Dresden Germany

ISBN: (纸本)9780769550183

Arbitrarily divisible workloads are present in a large class of scientific applications, such as N-body simulations, Monte Carlo simulations, CFD applications, and others. Divisible load theory (DLT) provides a tractable approach to the scheduling of arbitrarily divisible workloads. High performance parallel and distributed systems may operate in an unreliable environment, and a robust system is expected to deliver a certain level of performance when operating in such an environment. To the best of our knowledge, this is the first work to study and analyze the robustness of DLT algorithms. Using simulations, a study of the resiliency of DLT to variations in certain system features, such as the network latency, the network bandwidth, and the processor availability on a single level tree topology is presented. The simulation results demonstrate the robustness of the DLT algorithms under certain application and system characteristics. © 2013 IEEE.

关键词： Scheduling algorithms

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Development of a novel finder pattern for effective color 2D-barcode detection

Development of a novel finder pattern for effective color 2D...

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IEEE international symposium on Parallel and distributed Processing with applications

作者： Kato, Hiroko Tan, Keng T. Chai, Douglas Edith Cowan University School of Computer and Information Science Mount Lawley WA 6050 2 Bradford Street Australia School of Computer and Information Science Mount Lawley WA 6050 2 Bradford Street Australia School of Engineering Joondalup WA 6027 270 Joondalup Drive Australia

ISBN: (纸本)9780769534718

With a camera mobile phone, which has become a, "must-have" device, 2D-barcode works as an interface to bridge the physical and digital world. As the notion of ubiquitous computing has permeated, developing a new 2D-barcode and its applications has been a growing trend worldwide. A 2D-barcode symbol consists of two broad areas: data area and guide area. The components of the latter is collectively called "finder pattern" and used in locating the 2D-barcode symbol. The failure of finding the target symbol prevents a barcode reader from successfully decoding the barcode. Hence, designing a functional finder pattern is Me of the key for improving the robustness of barcode reading, and thus, the entire 2D-barcode system. We have designed a novel finder pattern integrated with a color 2D-barcode for camera mobile phone applications. Through the development and evaluation of the finder pattern for effective color 2D-barcode detection, this paper discusses keys to improve the functionality and reliability of finder patterns, which should be kept in mind when designing a finder pattern for any 2D-barcode symbol.

关键词： selectors Bar code scanners Bar Codes Ubiquitous computing Coding systems Color Mind

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POSTER: An intelligent framework to parallelize hadoop phases 18

POSTER: An intelligent framework to parallelize hadoop phase...

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27th ACM international symposium on High-Performance Parallel and distributed computing, HPDC 2018

作者： Maleki, Neda Rahmani, Amir Masoud Conti, Mauro Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Mathematics University of Padua Padua Italy

ISBN: (纸本)9781450358996

Hadoop-stock is a reliable, scalable, and open source implementation of the MapReduce framework to process data-intensive applications in a distributed and parallel environment. In a common environment between multiple users with various types of applications, due to the lower number of resources than the number of jobs, there will be multi-wave jobs. Shuffling as the longest phase of running a job has the most adverse effect (network traffic) on the job execution time. On one hand, due to the dependency of shuffle phase to reduce task, the shuffle phase could not start until the reduce task being scheduled. On the other hand, the static scheduling of reduce tasks results in loss of reduce slots. This paper presents our ongoing effort in the designing an intelligent service in which the sort/merge and shuffle phases are completely independent of map and reduce phases and could act in parallel with map and reduce phases. This parallelism mitigates the job completion time. © 2018 Association for computing Machinery.

关键词： MapReduce

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Predict Amount of General Program Supported by NSFC

Predict Amount of General Program Supported by NSFC

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Rongjiao Zheng Shesheng Zhang Xing Wang Department of Statistics Wuhan University of Technology Wuhan China

It is useful for researcher to predict amount of general program supported by NSFC. This paper explores the history data of amount of general program supported by NSFC, builds a regression equation of general program amount, analyses regression parameters by employing variance analysis method, and obtains predicting amount domain of general program supported by NSFC. The result of example shows the predicting accuracy is over 98%.

关键词： Decision support systems distributed computing business

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Editorial

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Recent Patents on Computer science 2014年第1期7卷 1-2页

作者： McHeick, Hamid Computer Science Department University of Quebec at Chicoutimi ChicoutimiQC Canada

Hamid Mcheick is currently an associate professor in computer science department at the University of Quebec At Chicoutimi (UQAC), Quebec, Canada. He holds a PhD degree in software engineering and distributed system from Montreal University, Canada. He holds a master degree from the University of Quebec at Montreal, Canada in information retrieval system. Professor Mcheick is interested in distributed software architecture, mobiles applications and pervasive computing, as well as in separation of concerns (component, services, aspect, etc.). He works also in ubiquitous computing, service composition of business process. He has given many talks in congresses and conferences as a keynote or invited speaker. He is a chief in editor and guest editor of several international journals. His research is supported by many research grants he has received from the Canadian government, University of Montreal, CRIM (Centre de Recherche informatique de Montreal), University of UQAM, and University of Quebec At Chicoutimi, Canada. The missions of Bentham Journal volume aim to bring, for both academics as well as for industrial practitioners, a set of articles discussing and answering the recent patents on core topics of software computing and intelligence and improving the state-of-the-art of worldwide research in the areas of computer science issues by publishing high-quality articles in this area, and by exposing the readers to various methodologies, tools, technologies, algorithms, results, and types of articles. It is our pleasure to highlight in June 2014, the 7th volume of the Journal Recent Patents on Computer science (CSENG) that includes a selected and reviewed research papers. This issue includes papers of Software Defined Networks for Data Center Optimization, Intelligent techniques and systems in credit risk analysis and forecasting, Cultivating Software Solutions Development in the Scientific Academia, Information Security and Threats in Mobile Appli

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Marco: A middleware architecture for distributed multimedia collaboration

Marco: A middleware architecture for distributed multimedia ...

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Seventh IEEE international symposium on Multimedia, ISM 2005

作者： Shih, Chia-Yen Hu, Jie Lee, Jinhwan Klefstad, Raymond Tolbert, Doug Laboratory on Distributed Object Computing Department of Electrical Engineering and Computer Science University of California Irvine CA 92697 United States Unisys Corporation Mission Viejo CA 92691 United States

ISBN: (纸本)0769524893

A distributed, real-time, collaborative visualization (DRCV) system for four dimensional datasets like fMRI images, can be a valuable tool to support scientific and medical research. Software applications supporting DRCV are lagging due to complex challenges, such as real-time rendering, coordinated, real-time navigation, dataset management, data location transparency, heterogeneous node management, data replication policies, and so on. In addition, people using DRCV tools are tightened to computing devices with specific configuration, which lower opportunities for remote visualization collaborations. We present a novel middleware to facilitate development of DRCV applications which guarantee high data availability and scalability in terns of collaborative groups and members, and are adaptable to computing devices with varying display capabilities We also show our prototype implementation of this middleware with performance measurements. © 2005 IEEE.

关键词： Multimedia systems

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PERMAVOST '22: Workshop on Performance engineering, Modelling, Analysis, and Visualization Strategy 22

PERMAVOST '22: Workshop on Performance Engineering, Modellin...

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31st international symposium on High-Performance Parallel and distributed computing (HPDC)

作者： Liem, Radita Solorzano, Ana Veroneze Scully-Allison, Connor Rhein Westfal TH Aachen Aachen Germany Northeastern Univ Boston MA USA Univ Arizona Tucson AZ USA

ISBN: (纸本)9781450391993

Modern software engineering is getting increasingly complicated. Especially in the HPC field, we are dealing with cutting edge infrastructure and a novel problem with unprecedented scale. The ability to monitor and analyze the performance of such applications and infrastructure is imperative for the future of improvement, design, and maintenance. In the current era, the writing and maintenance of these applications have ceased to be the job solely of computer scientists and has grown to encompass a wide variety of experts in mathematics, science, and other engineering disciplines. The fact that many developers from these disciplines have not received a formal education in computer science and rely increasingly on the tools created by computer scientists to analyze and optimize their code shows that there's a need for a forum to work together.

关键词： Performance analysis and methodology performance engineering

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Multi-party k-means clustering with privacy consideration

Multi-party k-means clustering with privacy consideration

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IEEE international symposium on Parallel and distributed Processing With applications

作者： Yu, Teng-Kai Lee, D.T. Chang, Shih-Ming Zhan, Justin Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan National Center for the Protection of the Financial Infrastructure Dakota State University Madison SD United States Institute of Information Science Academia Sinica Nankang Taipei Taiwan Taiwan Information Security Center Research Center for Information Technology Innovation Academia Sinica Nankang Taipei Taiwan

ISBN: (纸本)9780769541907

The k-means clustering algorithm is a widely used scheme to solve the clustering problem which classifies a given set of n data points in m-dimensional space into k clusters, whose centers are obtained by the centroids of the points in the same cluster. The problem with privacy consideration has been studied, when the data is distributed among different parties and the privacy of the distributed data is to be preserved. In this paper, we apply the concept of parallel computing to solve the privacy-preserving multi-party k-means clustering problem, when the data is vertically partitioned and horizontally partitioned respectively among different parties. We present algorithms for solving the problems for these two data partition models that run in O(nk) time and in O(m(k + log(n/k))) time respectively. The time complexities of the algorithms are much better than others without parallel computing. © 2010 IEEE.

关键词： Problem solving

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