2011 IEEE Third International Conference on Cloud Computing Technology and Science

Nov. 29 2011-Dec. 1 2011

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  • [Front cover]

    Publication Year: 2011, Page(s): C1
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  • [Title page i]

    Publication Year: 2011, Page(s): i
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  • [Title page iii]

    Publication Year: 2011, Page(s): iii
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  • [Copyright notice]

    Publication Year: 2011, Page(s): iv
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  • Table of contents

    Publication Year: 2011, Page(s):v - xiii
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  • Message from the General Chairs

    Publication Year: 2011, Page(s): xiv
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  • Message from the Program Committee Chairs

    Publication Year: 2011, Page(s): xv
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  • Message from the Cloud Computing Association - CloudCom.org

    Publication Year: 2011, Page(s): xvi
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  • Message from the Workshop Chair

    Publication Year: 2011, Page(s): xvii
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  • Conference Committees

    Publication Year: 2011, Page(s):xviii - xxiii
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  • External Reviewers

    Publication Year: 2011, Page(s):xxiv - xxv
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  • Workshops Committees

    Publication Year: 2011, Page(s):xxvi - xxvii
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  • Can Cloud Computing Be Used for Planning? An Initial Study

    Publication Year: 2011, Page(s):1 - 8
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (564 KB) | HTML iconHTML

    Cloud computing is emerging as a prominent computing model. It provides a low-cost, highly accessible alternative to other traditional high-performance computing platforms. It also has many other benefits such as high availability, scalability, elasticity, and free of maintenance. Given these attractive features, it is very desirable if automated planning can exploit the large, affordable computat... View full abstract»

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  • Ordinal Optimized Scheduling of Scientific Workflows in Elastic Compute Clouds

    Publication Year: 2011, Page(s):9 - 17
    Cited by:  Papers (15)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (680 KB) | HTML iconHTML

    Elastic compute clouds are best represented by the virtual clusters in Amazon EC2 or in IBM RC2. This paper proposes a simulation based approach to scheduling scientific workflows onto elastic clouds. Scheduling multitask workflows in virtual clusters is a NP-hard problem. Excessive simulations in months of time may be needed to produce the optimal schedule using Monte Carlo simulations. To reduce... View full abstract»

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  • Scientific Workflow Makespan Reduction through Cloud Augmented Desktop Grids

    Publication Year: 2011, Page(s):18 - 23
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (223 KB) | HTML iconHTML

    Scientific workflows are common in biomedical research, particularly for molecular docking simulations such as those used in drug discovery. Such workflows typically involve data distribution between computationally demanding stages which are usually mapped onto large scale compute resources. Volunteer or Desktop Grid (DG) computing can provide such infrastructure but has limitations resulting fro... View full abstract»

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  • Computational Neuroscience as a Service: Porting MIIND to the Cloud

    Publication Year: 2011, Page(s):24 - 31
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (619 KB) | HTML iconHTML

    In this paper, we investigate how cloud computing could benefit computational neuroscience. To that end, Multiple Interacting Instantiations of Neuronal Dynamics (MIIND), a computational neuroscience modelling toolkit, was ported to a private, university-owned cloud. The aim was to pave the way for making MIIND more accessible to non-specialist users in virtue of concealing its implementation cont... View full abstract»

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  • Byzantine Fault-Tolerant MapReduce: Faults are Not Just Crashes

    Publication Year: 2011, Page(s):32 - 39
    Cited by:  Papers (11)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (240 KB) | HTML iconHTML

    MapReduce is often used to run critical jobs such as scientific data analysis. However, evidence in the literature shows that arbitrary faults do occur and can probably corrupt the results of MapReduce jobs. MapReduce runtimes like Hadoop tolerate crash faults, but not arbitrary or Byzantine faults. We present a MapReduce algorithm and prototype that tolerate these faults. An experimental evaluati... View full abstract»

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  • Matchmaking: A New MapReduce Scheduling Technique

    Publication Year: 2011, Page(s):40 - 47
    Cited by:  Papers (35)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (410 KB) | HTML iconHTML

    MapReduce is a powerful platform for large-scale data processing. To achieve good performance, a MapReduce scheduler must avoid unnecessary data transmission by enhancing the data locality (placing tasks on nodes that contain their input data). This paper develops a new MapReduce scheduling technique to enhance map task's data locality. We have integrated this technique into Hadoop default FIFO sc... View full abstract»

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  • Scalable and Low-Latency Data Processing with Stream MapReduce

    Publication Year: 2011, Page(s):48 - 58
    Cited by:  Papers (19)  |  Patents (2)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (634 KB) | HTML iconHTML

    We present StreamMapReduce, a data processing approach that combines ideas from the popular MapReduce paradigm and recent developments in Event Stream Processing. We adopted the simple and scalable programming model of MapReduce and added continuous, low-latency data processing capabilities previously found only in Event Stream Processing systems. This combination leads to a system that is efficie... View full abstract»

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  • Optimizing Multiple Machine Learning Jobs on MapReduce

    Publication Year: 2011, Page(s):59 - 66
    Cited by:  Papers (4)  |  Patents (2)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (444 KB) | HTML iconHTML

    Recently, MapReduce has been used to parallelize machine learning algorithms. To obtain the best performance for these algorithms, tuning the parameters of the algorithms is required. However, this is time consuming because it requires executing a MapReduce program multiple times using various parameters. Such multiple executions can be assigned to a cluster in various ways, and the execution time... View full abstract»

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  • Applications and Evaluation of In-memory MapReduce

    Publication Year: 2011, Page(s):67 - 74
    Cited by:  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (287 KB) | HTML iconHTML

    In-memory storage techniques provide cloud applications with cheap, fast and large-scale RAM-based storage. By replicating data and providing adequate consistency control mechanisms, in-memory storage can simplify the design and implementation of highly scalable distributed applications. We argue that in-memory storage can increase the flexibility of the MapReduce parallel programming model withou... View full abstract»

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  • Parallel Outlier Detection Using KD-Tree Based on MapReduce

    Publication Year: 2011, Page(s):75 - 80
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (198 KB) | HTML iconHTML

    Distributed and Parallel algorithms have attracted a vast amount of interest and research in recent decades, to handle large-scale data set in real-world applications. In this paper, we focus on a parallel implementation of KD-Tree based outlier detection method to deal with large-scale data set. As one of the state-of-the-art outlier detection methods, KD-Tree based has been approved to be an eff... View full abstract»

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  • iHadoop: Asynchronous Iterations for MapReduce

    Publication Year: 2011, Page(s):81 - 90
    Cited by:  Papers (14)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (743 KB) | HTML iconHTML

    MapReduce is a distributed programming framework designed to ease the development of scalable data-intensive applications for large clusters of commodity machines. Most machine learning and data mining applications involve iterative computations over large datasets, such as the Web hyperlink structures and social network graphs. Yet, the MapReduce model does not efficiently support this important ... View full abstract»

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  • Comparing VM-Placement Algorithms for On-Demand Clouds

    Publication Year: 2011, Page(s):91 - 98
    Cited by:  Papers (69)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (948 KB) | HTML iconHTML

    Much recent research has been devoted to investigating algorithms for allocating virtual machines (VMs) to physical machines (PMs) in infrastructure clouds. Many such algorithms address distinct problems, such as initial placement, consolidation, or tradeoffs between honoring service-level agreements and constraining provider operating costs. Even where similar problems are addressed, each individ... View full abstract»

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  • Optimization-Based Virtual Machine Manager for Private Cloud Computing

    Publication Year: 2011, Page(s):99 - 106
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (411 KB) | HTML iconHTML

    In this paper, an optimal resource management framework for cloud computing environment is presented. Based on virtualization technology, the workload to be processed on a virtual machine can be moved (i.e., outsourced) from private cloud (i.e., in-house computer system) to the service provider in public cloud. The framework introduces the virtual machine manager (VMM) in private cloud operating t... View full abstract»

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