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SecDM: Securing Data Migration between Cloud Storage Systems

SecDM: Securing Data Migration between Cloud Storage Systems

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IEEE international symposium on Dependable, Autonomic and Secure computing (DASC)

作者： Qingni Shen Lizhe Zhang Xin Yang Yahui Yang Zhonghai Wu Ying Zhang School of Software and Microelectronics & MoE Key Laboratory of Network and Software Assurance Peking University Beijing China

With the development of cloud computing, cloud security issues have recently gained traction in the research community. Although much of the efforts are focused on securing the operation system and virtual machine, or securing data storage inside a cloud system, this paper takes an alternative perspective to cloud security-the security of data migration between different clouds. First, we describe some threats when we are doing data migration. Second, we propose a security mechanism to deal with the security issues on data migration from one cloud to another. Third, we design a prototype to give the mechanism a brief implementation based on HDFS (Hadoop distributed File System) and we do a series of tests to evaluate our prototype. Here, the solutions to securing data migration between clouds mainly involve in SSL negotiation, migration ticket design and block encryption in distributed file system and cluster parallel computing.

关键词： Cloud computing Encryption Prototypes Companies Message authentication

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QR Factorization on a Multicore Node Enhanced with Multiple GPU Accelerators

QR Factorization on a Multicore Node Enhanced with Multiple ...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Emmanuel Agullo Cedric Augonnet Jack Dongarra Mathieu Faverge Hatem Ltaief Samuel Thibault Stanimire Tomov INRIA LaBRI University of Bordeaux 1 France Innovative Computing Laboratory University of Tennessee Knoxville TN USA

One of the major trends in the design of exascale architectures is the use of multicore nodes enhanced with GPU accelerators. Exploiting all resources of a hybrid accelerators-based node at their maximum potential is thus a fundamental step towards exascale computing. In this article, we present the design of a highly efficient QR factorization for such a node. Our method is in three steps. The first step consists of expressing the QR factorization as a sequence of tasks of well chosen granularity that will aim at being executed on a CPU core or a GPU. We show that we can efficiently adapt high-level algorithms from the literature that were initially designed for homogeneous multicore architectures. The second step consists of designing the kernels that implement each individual task. We use CPU kernels from previous work and present new kernels for GPUs that complement kernels already available in the MAGMA library. We show the impact on performance of these GPU kernels. In particular, we present the benefits of new hybrid CPU/GPU kernels. The last step consists of scheduling these tasks on the computational units. We present two alternative approaches, respectively based on static and dynamic scheduling. In the case of static scheduling, we exploit the a priori knowledge of the schedule to perform successive optimizations leading to very high performance. We, however, highlight the lack of portability of this approach and its limitations to relatively simple algorithms on relatively homogeneous nodes. Alternatively, by relying on an efficient runtime system, Star PU, in charge of ensuring data availability and coherency, we can schedule more complex algorithms on complex heterogeneous nodes with much higher productivity. In this latter case, we show that we can achieve high performance in a portable way thanks to a fine interaction between the application and the runtime system. We demonstrate that the obtained performance is very close to the theoretical upper bo

关键词： Tiles Kernel Graphics processing unit Multicore processing Libraries Runtime Algorithm design and analysis

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Proceedings - 9th international symposium on distributed computing and Applications to Business, Engineering and Science, DCABES 2010

Proceedings - 9th International Symposium on Distributed Com...

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9th international symposium on distributed computing and Applications to Business, Engineering and Science, DCABES 2010

ISBN: (纸本)9780769541105

The proceedings contain 157 papers. The topics discussed include: parallel computation of surrogate models for potential energy surfaces;parallel particle swarm optimization algorithm of inverse heat conduction problem;application of quantum-behaved particle swarm optimization in parameter estimation of option pricing;a parallel algorithm for solving a kind of special structured linear systems;parallel algorithm and its application of a nonhydrostatic semi-implicit semi-Lagrangian global model;an inframarginal analysis based resource allocation method in volunteer computing;energy-efficient distributed clustering algorithm based on coverage;on the modeling of complex systems - methodologies and applications;semantics enhanced composition planner for distributed resources;a numerical experiment on lie group method;damage localization by the change of structural flexibility;and prediction of water-quality based on wavelet transform using vector machine.

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Large Volume Testing for Executable Formal Specification Using Hadoop

Large Volume Testing for Executable Formal Specification Usi...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Shigeru Kusakabe Graduate School of Information Science & Electrical Engineering Kyushu University Fukuoka Japan

Formal methods are mathematically-based techniques for specifying, developing and verifying a component or system, in order to increase the confidence regarding to the reliability and robustness of the target. Formal methods can be used at different levels with different techniques, and one approach is to use model-oriented formal languages such as VDM languages in writing specifications. During model development, we can test executable specifications in VDM-SL and VDM++. In a lightweight formal approach, we test formal specifications to increase our confidence as we do in implementing software codes with conventional programming languages. While the specific level of rigor depends on the aim of the project, millions of tests may be conducted in developing highly reliable mission-critical software in a light-weight formal approach. In this paper, we introduce our approach to supporting large volume of testing for executable formal specifications using Hadoop, an implementation of MapReduce programming model. We are able to automatically distribute interpretation of specifications in VDM languages by using Hadoop. We also apply a property-based data-driven testing tool, Quick Check, over MapReduce so that specification can be checked with thousands of tests by using formally described pre/post conditions and invariant functions. As an advanced property-based data-driven testing approach, we also discuss model-based testing. We observed scalable performance in testing large amount of data for executable specifications in our approaches.

关键词： Testing Software Computational modeling Programming Cloud computing Reliability Object oriented modeling

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A Scalable Reverse Lookup Scheme Using Group-Based Shifted Declustering Layout

A Scalable Reverse Lookup Scheme Using Group-Based Shifted D...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Junyao Zhang Pengju Shang Jun Wang Department of Electrical Engineering and Computer Science University of Central Florida Orlando FL USA

Recent years have witnessed an increasing demand for super data clusters. The super data clusters have reached the petabyte-scale that can consist of thousands or tens of thousands storage nodes at a single site. For this architecture, reliability is becoming a great concern. In order to achieve a high reliability, data recovery and node reconstruction is a must. Although extensive research works have investigated how to sustain high performance and high reliability in case of node failures at large scale, a reverse lookup problem, namely finding the objects list for the failed node remains open. This is especially true for storage systems with high requirement of data integrity and availability, such as scientific research data clusters and etc. Existing solutions are either time consuming or expensive. Meanwhile, replication based block placement can be used to realize fast reverse lookup. However, they are designed for centralized, small-scale storage architectures. In this paper, we propose a fast and efficient reverse lookup scheme named Group-based Shifted Declustering (G-SD) layout that is able to locate the whole content of the failed node. G-SD extends our previous shifted declustering layout and applies to large-scale file systems. Our mathematical proofs and real-life experiments show that G-SD is a scalable reverse lookup scheme that is up to one order of magnitude faster than existing schemes.

关键词： Layout Peer to peer computing Redundancy Servers Heuristic algorithms Computer architecture

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Betweenness Centrality Approximations for an Internet Deployed P2P Reputation System

Betweenness Centrality Approximations for an Internet Deploy...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Dimitra Gkorou Johan Pouwelse Dick Epema Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technnology Netherlands

In the Barter cast reputation mechanism of the BitTorrent-based P2P client Tribler, peers compute local, subjective reputations of other peers by applying a flow-based algorithm to a locally maintained Barter cast graph with peers as nodes and bandwidth contributions as edges. We have previously shown that the computed reputations are more accurate when a peer takes the node with the highest Betweenness Centrality (BC) in its local Barter cast graph as the initial point in this algorithm rather than itself. BC is a powerful metric for identifying central nodes in complex network analysis, but its computation in large and dynamic networks is costly, %Unfortunately, BC computation in large and dynamic networks is costly, and previously proposed approximation methods are only designed for static networks. In this paper, first we assess the stability of the nodes with the highest BC values in growing synthetic random and scale-free, and Barter cast graphs. Next, we evaluate three BC approximation methods proposed in the literature in terms of their ability to identify the top-most central nodes. We show that these approximations are efficient and highly accurate in scale-free and Barter cast graphs, but less so in random graphs. Finally, we integrate the three BC approximations into Barter cast, and we evaluate the quality of the reputations they yield.

关键词： Peer to peer computing Approximation methods Accuracy Measurement Approximation algorithms Social network services Stability analysis

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An auto-tuning solution to data streams clustering in OpenCL

An auto-tuning solution to data streams clustering in OpenCL

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14th IEEE Int. Conf. on Computational Science and Engineering, CSE 2011, the 11th international symposium on Pervasive Systems, Algorithms, and Networks, I-SPAN 2011, and the 10th IEEE Int. Conf. on Ubiquitous computing and Communications, IUCC 2011

作者： Fang, Jianbin Varbanescu, Ana Lucia Sips, Henk Parallel and Distributed Systems Group Delft University of Technology Delft Netherlands

ISBN: (纸本)9780769544779

Due to its applicability to numerous types of data, including telephone records, web documents, and click streams, the data stream model has recently attracted attention. For analysis of such data, it is crucial to process the data in a single pass, or a small number of passes, using little memory. This paper provides an OpenCL implementation for data streams clustering, and then presents several optimizations for it, which make it more efficient in terms of memory usage. In order to maximize performance for different problem sizes and architectures, we also propose an auto-tuning solution. Experimental results show that our fully optimized implementation can perform 2.1x and 1.4x faster than the native OpenCL implementation on NVIDIA GTX480 and AMD HD5870, respectively, it can also achieve 1.4x to 3.3x speedup relative to the original CUDA implementation solution on GTX480. © 2011 IEEE.

关键词： Auto-tuning Clustering Data Streams OpenCL Performance Optimizations

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DHFS: A High-Throughput Heterogeneous File System Based on Mainframe for Cloud Storage

DHFS: A High-Throughput Heterogeneous File System Based on M...

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international symposium on parallel Architectures, Algorithms and Programming (PAAP)

作者： Hongtao Du Zhanhuai Li Computer College Northwestern Polytechnical University XiAn Shaanxi China

The file system for the Cloud Storage usually use the distributed structure which store the metadata and the user data respectively. The I/O nodes get the management information of file from the specific metadata server through the networks. Under this mode, the metadata server should be the performance bottleneck of the whole system when huge number nodes or the heavy workloads in Cloud Storage. Therefore, the capability of the metadata server become one of the key aspect for the whole system. In this paper, we present DHFS(Distribute Heterogeneous File System), a high throughput system for Cloud Storage, building metadata server on the high performance mainframe. For the different organization of the file system between mainframe and open system(Unix, Solaris, Windows, Linux, etc.), we implement the user-level library for the heterogeneous share of data which allow application in open system accessing files of mainframe. We also employ a series of strategies for the performance optimization.

关键词： Open systems Cloud computing File systems Throughput Writing Servers Delay

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GPGPU-based data parallel region growing algorithm for cell nuclei detection

GPGPU-based data parallel region growing algorithm for cell ...

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international symposium on Computational Intelligence and Informatics

作者： Sándor Szénási Zoltán Vámossy Miklós Kozlovszky John von Neumann Faculty of Informatics Óbuda University Budapest Hungary Laboratory of Parallel and Distributed Computing MTA SZTAKI Budapest Hungary

Nowadays microscopic analysis of tissue samples is done more and more by using digital imagery and special immunodiagnostic software. These are typically specific applications developed for one distinct field, but some subroutines are commonly repeated, for example several applications contain steps that can detect cell nuclei in a sample image. The aim of our research is developing a new data parallel algorithm that can be implemented even in a GPGPU environment and that is capable of counting hematoxylin eosin (HE) stained cell nuclei and of identifying their exact locations and sizes (using a variation of the region growing method). Our presentation contains the detailed description of the algorithm, the peculiarity of the CUDA implementation, and the evaluation of the created application (regarding its accuracy and the decrease in the execution time).

关键词： Graphics processing unit Kernel Instruction sets Computer architecture Algorithm design and analysis Microprocessors Synchronization

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Address Translation Optimization for Unified parallel C Multi-dimensional Arrays

Address Translation Optimization for Unified Parallel C Mult...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Olivier Serres Ahmad Anbar Saumil G. Merchant Abdullah Kayi Tarek El-Ghazawi NSF Center of High-Performance Reconfigurable Computing (CHREC) Department of Electrical and Computer Engineering George Washington University USA

Partitioned Global Address Space (PGAS) languages offer significant programmability advantages with its global memory view abstraction, one-sided communication constructs and data locality awareness. These attributes place PGAS languages at the forefront of possible solutions to the exploding programming complexity in the many-core architectures. To enable the shared address space abstraction, PGAS languages use an address translation mechanism while accessing shared memory to convert shared addresses to physical addresses. This mechanism is already expensive in terms of performance in distributed memory environments, but it becomes a major bottleneck in machines with shared memory support where the access latencies are significantly lower. Multi- and many-core processors exhibit even lower latencies for shared data due to on-chip cache space utilization. Thus, efficient handling of address translation becomes even more crucial as this overhead may easily become the dominant factor in the overall data access time for such architectures. To alleviate address translation overhead, this paper introduces a new mechanism targeting multi-dimensional arrays used in most scientific and image processing applications. Relative costs and the implementation details for UPC are evaluated with different workloads (matrix multiplication, Random Access benchmark and Sobel edge detection) on two different platforms: a many-core system, the TILE64 (a 64 core processor) and a dual-socket, quad-core Intel Nehalem system (up to 16 threads). Our optimization provides substantial performance improvements, up to 40x. In addition, the proposed mechanism can easily be integrated into compilers abstracting it from the programmers. Accordingly, this improves UPC productivity as it will reduce manual optimization efforts required to minimize the address translation overhead.

关键词： Instruction sets Optimization Arrays Electronics packaging Table lookup Tiles

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