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44. Jahrestagung der Gesellschaft fur Informatik INFORMATIK 2014 - Big Data - Komplexitat meistern - Big Data - Mastering Complexity: 44th Annual Meeting of the Society for computer science, INFORMATICS 2014

作者： Uekermann, Benjamin Gatzhammer, Bernhard Mehl, Miriam Department of Computer Science Technische Universität München Boltzmannstraße 3 Garching85748 Germany Institute for Parallel and Distributed Systems Universität Stuttgart Universitätsstraße 38 Stuttgart70569 Germany

ISBN: (纸本)9783885796268

Partitioned coupling approaches are an important tool in order to achieve a decent time-to-solution for multi-physics problems with more than two physical fields or changing combinations of fields. We study different approaches to deduce coupling schemes for partitioned multi-physics scenarios, by means of a simple, but yet challenging fluid-structure-fluid model problem. To our knowledge, this is the first time that a fully implicit black-box coupling scheme for partitioned multi-physics scenarios is described. This allows the simulation of a new range of applications in a partitioned way.

关键词： Fluid structure interaction

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Location-Aware Multi-user Resource Allocation in distributed Clouds

Location-Aware Multi-user Resource Allocation in Distributed...

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10th Annual Conference of Advanced computer Architecture, ACA 2014

作者： Li, Jiaxin Li, Dongsheng Zheng, Jing Quan, Yong National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Information Center of Logistics Department Beijing China School of Computer Science National University of Defense Technology Changsha China

ISBN: (纸本)9783662444900

Resource allocation for multi-user across multiple data centers is an important problem in cloud computing environments. Many geographically-distributed users may request virtualized resources simultaneously. And the distances from users to allocated resources have much impact on the quality of service (QoS) in multiple data centers environment. Most existing methods do not take all these factors into account when allocating resources. They usually result in poor runtime performance of users' virtual computing environment and the remarkable difference of users' QoS. In this paper, we propose RAMD, a resource allocation algorithm based on multi-stage decision in multiple data centers. The RAMD algorithm allocate VMs to users, taking into account the correlation and interaction between multiple users, so as to minimize the sum of all users' service distances (i.e. determined by user location and network distance of virtual machines). Experimental results show that the algorithm can effectively deal with the cloud resource allocation for multi-user across multiple data centers. It can improve the runtime performance of users' virtualized resources and reduce the difference of QoS. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Resource allocation

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Using synthetic peptides to benchmark peptide identification software and search parameters for MS/MS data analysis

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EuPA Open Proteomics 2014年 5卷 21-31页

作者： Quandt, Andreas Espona, Lucia Balasko, Akos Weisser, Hendrik Brusniak, Mi-Youn Kunszt, Peter Aebersold, Ruedi Malmström, Lars Department of Biology Institute of Molecular Systems Biology ETH Zurich Switzerland SyBIT SystemsX.ch Switzerland MTA SZTAKI Laboratory of Parallel and Distributed Systems Budapest Hungary Institute for Systems Biology Seattle United States S3IT University of Zurich Switzerland Faculty of Science University of Zurich Switzerland

Tandem mass spectrometry and sequence database searching are widely used in proteomics to identify peptides in complex mixtures. Here we present a benchmark study in which a pool of 20,103 synthetic peptides was measured and the resulting data set was analyzed using around 1800 different software and parameter set combinations. The results indicate a strong relationship between the performance of an analysis workflow and the applied parameter settings. We present and discuss strategies to optimize parameter settings in order to significantly increase the number of correctly assigned fragment ion spectra and to make the analysis method robust. © 2014 The Authors.

关键词： Classical database search Data analysis Mass spectrometry Search engine Synthetic peptides

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Hierarchical categorization of open source software by online profiles

Hierarchical categorization of open source software by onlin...

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作者： Wang, Tao Wang, Huaimin Yin, Gang Yang, Cheng Li, Xiang Zou, Peng Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha 410073 China Department of Computer Science University of Western Ontario London ON N6A 5B7 Canada Academy of Equipment Beijing 100000 China

The large amounts of freely available open source software over the Internet are fundamentally changing the traditional paradigms of software development. Efficient categorization of the massive projects for retrieving relevant software is of vital importance for Internet-based software development such as solution searching, best practices learning and so on. Many previous works have been conducted on software categorization by mining source code or byte code, but were verified on only relatively small collections of projects with coarse-grained categories or clusters. However, Internet-based software development requires finer-grained, more scalable and language-independent categorization approaches. In this paper, we propose a novel approach to hierarchically categorize software projects based on their online profiles. We design a SVMbased categorization framework and adopt a weighted combination strategy to aggregate different types of profile attributes from multiple repositories. Different basic classification algorithms and feature selection techniques are employed and compared. Extensive experiments are carried out on more than 21,000 projects across five repositories. The results show that our approach achieves significant improvements by using weighted combination. Compared to the previous work, our approach presents competitive results with more finer-grained and multi-layered category hierarchy with more than 120 categories. Unlike approaches that use source code or byte code, our approach is more effective for large-scale and languageindependent software categorization. In addition, experiments suggest that hierarchical categorization combined with general keyword-based searching improves the retrieval efficiency and accuracy. Copyright © 2014 The Institute of Electronics, Information and Communication Engineers.

关键词： Open source software

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Concurrent and consistent virtual machine introspection with hardware transactional memory

Concurrent and consistent virtual machine introspection with...

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IEEE Symposium on High-Performance computer Architecture

作者： Yutao Liu Yubin Xia Haibing Guan Binyu Zang Haibo Chen Institute of Parallel and Distributed Systems Shanghai Jiao Tong University Department of Computer Science Shanghai Jiao Tong University

Virtual machine introspection, which provides tamperresistant, high-fidelity “out of the box” monitoring of virtual machines, has many prominent security applications including VM-based intrusion detection, malware analysis and memory forensic analysis. However, prior approaches are either intrusive in stopping the world to avoid race conditions between introspection tools and the guest VM, or providing no guarantee of getting a consistent state of the guest VM. Further, there is currently no effective means for timely examining the VM states in question. In this paper, we propose a novel approach, called TxIntro, which retrofits hardware transactional memory (HTM) for concurrent, timely and consistent introspection of guest VMs. Specifically, TxIntro leverages the strong atomicity of HTM to actively monitor updates to critical kernel data structures. Then TxIntro can mount introspection to timely detect malicious tampering. To avoid fetching inconsistent kernel states for introspection, TxIntro uses HTM to add related synchronization states into the read set of the monitoring core and thus can easily detect potential inflight concurrent kernel updates. We have implemented and evaluated TxIntro based on Xen VMM on a commodity Intel Haswell machine that provides restricted transactional memory (RTM) support. To demonstrate the effectiveness of TxIntro, we implemented a set of kernel rootkit detectors using TxIntro. Evaluation results show that TxIntro is effective in detecting these rootkits, and is efficient in adding negligible performance overhead.

关键词： Kernel Monitoring Educational institutions Abstracts Single photon emission computed tomography Virtual machine monitors Continuous wavelet transforms

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Data Avenue: Remote Storage Resource Management in WS-PGRADE/gUSE

Data Avenue: Remote Storage Resource Management in WS-PGRADE...

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International Workshop on science Gateways (IWSG)

作者： Ákos Hajnal Zoltán Farkas Péter Kacsuk Laboratory of Parallel and Distributed Systems Hungarian Academy of Sciences Budapest Hungary Computer Science and Software Engineering Department University of Westminster London United Kingdom

State-of-the-art gateways are connected to several distributed computing infrastructures (DCIs) and are able to run jobs and workflows simultaneously in all those different DCIs. However, the flexibility of accessing data storages belonging to different DCIs is a missing feature of current gateways. SZTAKI (Institute for computer science and Control) has developed a Data Avenue Blacktop service and aLiferay-based Data Avenue port let that open the door for integrating such features into science gateways. The paper explains the design considerations of the Data Avenue Blacktop service and its usage scenarios in science gateways through the Data Avenue port let.

关键词： Conferences Logic gates

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Replication-Based Fault-Tolerance for Large-Scale Graph Processing

Replication-Based Fault-Tolerance for Large-Scale Graph Proc...

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International Conference on Dependable systems and Networks (DSN)

作者： Peng Wang Kaiyuan Zhang Rong Chen Haibo Chen Haibing Guan Shanghai Key Laboratory of Scalable Computing and Systems Institute of Parallel and Distributed Systems Shanghai Jiao Tong University Department of Computer Science Shanghai Jiao Tong University

The increasing algorithm complexity and dataset sizes necessitate the use of networked machines for many graph-parallel algorithms, which also makes fault tolerance a must due to the increasing scale of machines. Unfortunately, existing large-scale graph-parallel systems usually adopt a distributed checkpoint mechanism for fault tolerance, which incurs not only notable performance overhead but also lengthy recovery time. This paper observes that the vertex replicas created for distributed graph computation can be naturally extended for fast in-memory recovery of graph states. This paper proposes Imitator, a new fault tolerance mechanism, that supports cheaply maintenance of vertex states by replicating vertex states to their replicas during normal message exchanges, and provides fast in-memory reconstruction of failed vertices from replicas in other machines. Imitator has been implemented by extending Hama, a popular open-source clone of Pregel. Evaluation shows that Imitator incurs negligible performance overhead (less than 5% for all cases) and can recover from failures of more than one million of vertices with less than 3.4 seconds.

关键词： Fault tolerance Fault tolerant systems computer crashes Checkpointing Synchronization Clustering algorithms Computational modeling

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Measuring and Controlling Unfairness in Decentralized Planning of Energy Demand

Measuring and Controlling Unfairness in Decentralized Planni...

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IEEE International Energy Conference

作者： Evangelos Pournaras Matteo Vasirani Robert E. Kooij Karl Aberer Department of Computer Science and Engineering Delft University of Technology Distributed Information Systems Laboratory EPFL

ISBN: (纸本)9781479924509

Demand-side energy management improves robustness and efficiency in Smart Grids. Load-adjustment and load-shifting are performed to match demand to available supply. These operations come at a discomfort cost for consumers as their lifestyle is influenced when they adjust or shift in time their demand. Performance of demand-side energy management mainly concerns how robustness is maximized or discomfort is minimized. However, measuring and controlling the distribution of discomfort as perceived between different consumers provides an enriched notion of fairness in demand-side energy management that is missing in current approaches. This paper defines unfairness in demand-side energy management and shows how unfairness is measurable and controllable by software agents that plan energy demand in a decentralized fashion. Experimental evaluation using real demand and survey data from two operational Smart Grid projects confirms these findings.

关键词： Unfairness Fairness Agent Planning Demand Smart Grid Load-adjustment Load-shifting

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Soft-Constrained Nonnegative Matrix Factorization via Normalization

Soft-Constrained Nonnegative Matrix Factorization via Normal...

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International Joint Conference on Neural Networks

作者： Long Lan Naiyang Guan Xiang Zhang Dacheng Tao Zhigang Luo Science and Technology on Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Centre for Quantum Computation & Intelligent Systems and the Faculty of Engineering and Information Technology University of Technology

ISBN: (纸本)9781479914821

Semi-supervised clustering aims at boosting the clustering performance on unlabeled samples by using labels from a few labeled samples. Constrained NMF (CNMF) is one of the most significant semi-supervised clustering methods, and it factorizes the whole dataset by NMF and constrains those labeled samples from the same class to have identical encodings. In this paper, we propose a novel soft-constrained NMF (SCNMF) method by softening the hard constraint in CNMF. Particularly, SCNMF factorizes the whole dataset into two lower-dimensional factor matrices by using multiplicative update rule (MUR). To utilize the labels of labeled samples, SCNMF iteratively normalizes both factor matrices after updating them with MURs to make encodings of labeled samples close to their label vectors. It is therefore reasonable to believe that encodings of unlabeled samples are also close to their corresponding label vectors. Such strategy significantly boosts the clustering performance even when the labeled samples are rather limited, e.g., each class owns only a single labeled sample. Since the normalization procedure never increases the computational complexity of MUR, SCNMF is quite efficient and effective in practices. Experimental results on face image datasets illustrate both efficiency and effectiveness of SCNMF compared with both NMF and CNMF.

关键词： Matrix decomposition Encoding Vectors Clustering methods Clustering algorithms Time complexity Face

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Box-constrained Projective Nonnegative Matrix Factorization via Augmented Lagrangian Method

Box-constrained Projective Nonnegative Matrix Factorization ...

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International Joint Conference on Neural Networks

作者： Xiang Zhang Naiyang Guan Long Lan Dacheng Tao Zhigang Luo Science and Technology on Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Centre for Quantum Computation & Intelligent Systems and the Faculty of Engineering and Information Technology University of Technology

ISBN: (纸本)9781479914821

Projective non-negative matrix factorization (PNMF) projects a set of examples onto a subspace spanned by a non-negative basis whose transpose is regarded as the projection matrix. Since PNMF learns a natural parts-based representation, it has been successfully used in text mining and pattern recognition. However, it is non-trivial to analyze the convergence of the optimization algorithms for PNMF because its objective function is non-convex. In this paper, we propose a Box-constrained PNMF (BPNMF) method to overcome this deficiency of PNMF. In particular, BPNMF introduces an auxiliary variable, i.e., the coefficients of examples, and incorporates the following two types of constraints: 1) each entry of the basis is non-negative and upper-bounded, i.e., box-constrained, and 2) the coefficients equal to the projected points of the examples. The first box constraint makes the basis to be bound and the second equality constraint keeps its equivalence to PNMF. Similar to PNMF, BPNMF is difficult because the objective function is non-convex. To solve BPNMF, we developed an efficient algorithm in the frame of augmented Lagrangian multiplier (ALM) method and proved that the ALM-based algorithm converges to local minima. Experimental results on two face image datasets demonstrate the effectiveness of BPNMF compared with the representative methods.

关键词： Accuracy Linear programming Training Convergence Optimization Face Educational institutions

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