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检索条件"任意字段=Proceedings of the first international symposium on Databases in parallel and distributed systems"

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Big Data Processing in Cloud Computing Environments

Big Data Processing in Cloud Computing Environments

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international symposium on parallel Architectures, Algorithms and Networks (ISPAN)

作者： Changqing Ji Yu Li Wenming Qiu Uchechukwu Awada Keqiu Li College of Information Science and Technology Dalian Maritime University Dalian China College of Physical Science and Technology Dalian University Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (纸本)9781467350648

With the rapid growth of emerging applications like social network analysis, semantic Web analysis and bioinformatics network analysis, a variety of data to be processed continues to witness a quick increase. Effective management and analysis of large-scale data poses an interesting but critical challenge. Recently, big data has attracted a lot of attention from academia, industry as well as government. This paper introduces several big data processing technics from system and application aspects. first, from the view of cloud data management and big data processing mechanisms, we present the key issues of big data processing, including cloud computing platform, cloud architecture, cloud database and data storage scheme. Following the Map Reduce parallel processing framework, we then introduce Map Reduce optimization strategies and applications reported in the literature. Finally, we discuss the open issues and challenges, and deeply explore the research directions in the future on big data processing in cloud computing environments.

关键词： Information management Data handling Data storage systems Cloud computing Computer architecture Data models distributed databases

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parallelGDB: A parallel Graph Database based on Cache Specialization 11

<i>ParallelGDB</i>: A Parallel Graph Database based on Cache...

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15th international Database Engineering and Applications symposium (IDEAS)

作者： Barguno, Luis Muntes-Mulero, Victor Dominguez-Sal, David Valduriez, Patrick UPC DAMA Barcelona Spain INRIA LIRMM Rocquencourt France

ISBN: (纸本)9781450306270

The need for managing massive attributed graphs is becoming common in many areas such as recommendation systems, proteomics analysis, social network analysis or bibliographic analysis. This is making it necessary to move towards parallel systems that allow managing graph databases containing millions of vertices and edges. Previous work on distributed graph databases has focused on finding ways to partition the graph to reduce network traffic and improve execution time. However, partitioning a graph and keeping the information regarding the location of vertices might be unrealistic for massive graphs. In this paper, we propose parallel-GDB, a new system based on specializing the local caches of any node in this system, providing a better cache hit ratio. parallelGDB uses a random graph partitioning, avoiding complex partition methods based on the graph topology, that usually require managing extra data structures. This proposed system provides an efficient environment for distributed graph databases.

关键词： Graph database Cache specialization Graph algorithms distributed and parallel computing distributed graphs

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DRESS Codes for the Storage Cloud: Simple Randomized Constructions

DRESS Codes for the Storage Cloud: Simple Randomized Constru...

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IEEE international symposium on Information Theory (ISIT)

作者： Pawar, Sameer Noorshams, Nima El Rouayheb, Salim Ramchandran, Kannan Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA

ISBN: (纸本)9781457705953

We introduce an efficient family of exact regenerating codes for data storage in large-scale distributed systems. We refer to these new codes as distributed Replication-based Exact Simple Storage (DRESS) codes. A key property of DRESS codes is their very efficient distributed and uncoded repair and growth processes that have minimum bandwidth, reads and computational overheads. This property is essential for large-scale systems with high reliability and availability requirements. DRESS codes will first encode the file using a Maximum Distance Separable (MDS) code, then place multiple replicas of the coded packets on different nodes in the system. We propose a simple and flexible randomized scheme for placing those replicas based on the balls-and-bins model. Our construction showcases the power of the probabilistic approach in constructing regenerating codes that can be efficiently repaired and grown.

关键词： Availability Bandwidth Color DRESS code Decision support systems MDS code Maintenance engineering Peer to peer computing Silicon availability requirement balls-and-bins model cloud computing cloud storage data storage distributed memory systems distributed replication-base exact simple storage code large-scale distributed system maximum distance separable code regenerating code construction reliability reliability requirement replicated databases storage area networks uncoded repair processes

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A Cost-intelligent Application-specific Data Layout Scheme for parallel File systems 11

A Cost-intelligent Application-specific Data Layout Scheme f...

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20th international symposium on High Performance distributed Computing

作者： Song, Huaiming Yin, Yanlong Chen, Yong Sun, Xian-He IIT Dept Comp Sci Chicago IL 60616 USA

ISBN: (纸本)9781450305525

I/O data access is a recognized performance bottleneck of high-end computing. Several commercial and research parallel file systems have been developed in recent years to ease the performance bottleneck. These advanced file systems perform well on some applications but may not perform well on others. They have not reached their full potential in mitigating the I/O-wall problem. Data access is application dependent. Based on the application-specific optimization principle, in this study we propose a cost-intelligent data access strategy to improve the performance of parallel file systems. We first present a novel model to estimate data access cost of different data layout policies. Next, we extend the cost model to calculate the overall I/O cost of any given application and choose an appropriate layout policy for the application. A complex application may consist of different data access patterns. Averaging the data access patterns may not be the best solution for those complex applications that do not have a dominant pattern. We then further propose a hybrid data replication strategy for those applications, so that a file can have replications with different layout policies for the best performance. Theoretical analysis and experimental testing have been conducted to verify the newly proposed cost-intelligent layout approach. Analytical and experimental results show that the proposed cost model is effective and the application-specific data layout approach achieved up to 74% performance improvement for data-intensive applications.

关键词： data layout data-access performance modeling data-intensive parallel file systems

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parallel investigations of the dynamics of neural microcircuits

Parallel investigations of the dynamics of neural microcircu...

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2011 10th international symposium on parallel and distributed Computing, ISPDC 2011

作者： Muntean, Ioan Lucian Joldos, Marius Ban, Ilinca Raluca Morariu, Iulia Maria Computer Science Department Technical University of Cluj-Napoca Romania

ISBN: (纸本)9780769545400

The advent of multicore and many core technologies opens new frontiers in computational sciences, making possible intensive computations on affordable computer systems. Our ongoing work is focused on the investigation of the suitability of many core and multicore systems to computational neuroscience simulations. We aim at providing a deeper understanding of the influence the various parallelisation techniques have on the dynamics of spiking neural microcircuits - as they show an almost chaotic behaviour. For that, we are developing a methodology for the evaluation of parallel implementations of biologically-based neural models and small world neural networks. Following our methodology, we designed a framework to assess the behaviour and performance of different parallel and sequential implementations of simulation models and techniques. We present first results obtained on multicore systems with OpenMP parallelisation of Hodgkin-Huxley based-models. © 2011 IEEE.

关键词： Application programming interfaces (API)

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Advances in Spatial and Temporal databases - 12th international symposium, SSTD 2011, proceedings

Advances in Spatial and Temporal Databases - 12th Internatio...

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12th international symposium on Advances in Spatial and Temporal databases, SSTD 2011

ISBN: (纸本)9783642229213

The proceedings contain 40 papers. The topics discussed include: FAST: a generic framework for flash-aware spatial trees;MIDAS: multi-attribute indexing for distributed architecture systems;thread-level parallel indexing of update intensive moving-object workloads;efficient processing of Top-k spatial keyword queries;retrieving k-nearest neighboring trajectories by a set of point locations;towards reducing taxicab cruising time using spatio-temporal profitability maps;computing the cardinal direction development between moving points in spatio-temporal databases;continuous probabilistic count queries in wireless sensor networks;geodetic point-in-polygon query processing in oracle spatial;MSSQ: manhattan spatial skyline queries;inverse queries for multidimensional spaces;efficient evaluation of k-NN queries using spatial mashups;and SeTraStream: semantic-aware trajectory construction over streaming movement data.

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Completely distributed particle filters for target tracking in sensor networks

Completely distributed particle filters for target tracking ...

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25th IEEE international parallel and distributed Processing symposium, IPDPS 2011

作者： Jiang, Bo Ravindran, Binoy Department of Electrical and Computer Engineering Virginia Tech. Blacksburg VA 24061 United States

ISBN: (纸本)9780769543857

Particle filters (or PFs) are widely used for the tracking problem in dynamic systems. Despite their remarkable tracking performance and flexibility, PFs require intensive computation and communication, which are strictly constrained in wireless sensor networks (or WSNs). Thus, distributed particle filters (or DPFs) have been studied to distribute the computational workload onto multiple nodes while minimizing the communication among them. However, weight normalization and resampling in generic PFs cause significant challenges in the distributed implementation. Few existing efforts on DPF could be implemented in a completely distributed manner. In this paper, we design a completely distributed particle filter (or CDPF) for target tracking in sensor networks, and further improve it with neighborhood estimation toward minimizing the communication cost. first, we describe the particle maintenance and propagation mechanism, by which particles are maintained on different sensor nodes and propagated along the target trajectory. Then, we design the CDPF algorithm by adjusting the order of PFs' four steps and leveraging the data aggregation during particle propagation. Finally, we develop a neighborhood estimation method to replace the measurement broadcasting and the calculation of likelihood functions. With this approximate estimation, the communication cost of DPFs can be minimized. Our experimental evaluations show that although CDPF incurs about 50% more estimation error than semi-distributed particle filter (or SDPF), its communication cost is lower than that of SDPF by as much as 90%. © 2011 IEEE.

关键词： Target tracking

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Security-driven fault tolerant scheduling algorithm for high dependable distributed real-time system

Security-driven fault tolerant scheduling algorithm for high...

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2011 4th international symposium on parallel Architectures, Algorithms and Programming, PAAP 2011

作者： Ping, Xia Zhou, Xingshe Department of Computer Science Northwestern Polytechnic University Xi'an China

ISBN: (纸本)9780769545752

Critical real time system must run under high dependable environment which guarantees a high level of reliability and security. Fault tolerance and security technique has played an important role in building such environment. In this paper, we provide an improved technique to schedule such tasks with high reliability and security requirement on high dependable distributed real time system. first, the paper analyzed the scheduling problem on high dependable distributed real time system and provided a refined scheduling model which accurately described processors, fault tolerant tasks and security overhead. Based on the scheduling model, it proposed a new and better scheduling algorithm - Security Driven Fault Tolerant Scheduling Algorithm (SDFT) which extended FTRMFF algorithm to tolerate N processors failure at one time, and it introduced a new global scheduler to improve efficiency of scheduling process. Moreover, the SDFT supported flexible security policy applied on real time tasks according to its security requirement and considered the effect of security overhead during scheduling. In addition, the paper adopted and extended the Completion Time Test (CTT) as the schedulability criteria. Simulation experiments showed the SDFT algorithm could achieve better reliability and security at the cost of less number of processors. © 2011 IEEE.

关键词： Real time systems

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On the performance variability of production cloud services 11

On the performance variability of production cloud services

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proceedings of the 2011 11th IEEE/ACM international symposium on Cluster, Cloud and Grid Computing

作者： Iosup, Alexandru Yigitbasi, Nezih Epema, Dick Parallel and Distributed Systems Group Delft University of Technology Mekelweg 4 2628 CD Delft Netherlands

ISBN: (纸本)9780769543956

Cloud computing is an emerging infrastructure paradigm that promises to eliminate the need for companies to maintain expensive computing hardware. Through the use of virtualization and resource time-sharing, clouds address with a single set of physical resources a large user base with diverse needs. Thus, clouds have the potential to provide their owners the benefits of an economy of scale and, at the same time, become an alternative for both the industry and the scientific community to self-owned clusters, grids, and parallel production environments. For this potential to become reality, the first generation of commercial clouds need to be proven to be dependable. In this work we analyze the dependability of cloud services. Towards this end, we analyze long-term performance traces from Amazon Web Services and Google App Engine, currently two of the largest commercial clouds in production. We find that the performance of about half of the cloud services we investigate exhibits yearly and daily patterns, but also that most services have periods of especially stable performance. Last, through trace-based simulation we assess the impact of the variability observed for the studied cloud services on three large-scale applications, job execution in scientific computing, virtual goods trading in social networks, and state management in social gaming. We show that the impact of performance variability depends on the application, and give evidence that performance variability can be an important factor in cloud provider selection. © 2011 IEEE.

关键词： Web services

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Fault Tolerant Data Acquisition through Dynamic Load Balancing

Fault Tolerant Data Acquisition through Dynamic Load Balanci...

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

作者： Michal Simon CERN Geneva Switzerland Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland

Modern detectors used in high energy physics experiments are complex instruments designed to register collisions of particles at a rate in the MHz range. Data that correspond to a single collision of particles, referred to as an event, are acquired from millions of readout channels, and filtered, first by dedicated hardware, and then by computing farms running sophisticated filtering algorithms. In case of data acquisition systems with single-stage software filtration, due to the high input rate (the order of 100 kHz), the data are usually distributed in a static way between filtering nodes. However, the static distribution determines strongly the system, and results in decreased fault tolerance. The main objective of the presented studies is to increase the system's overall fault tolerance through dynamic load balancing. The proposed method aims to balance the workload inside heterogeneous systems, as well as, homogeneous systems, where the imbalance could be caused by faults. Moreover, our research includes developing a scalable load balancing protocol along with a distributed asynchronous load assignment policy. As a case study we consider the Data Acquisition system of the Compact Muon Solenoid experiment at CERN's new Large Hadron Collider.

关键词： Data acquisition Load management Fault tolerance Fault tolerant systems Algorithm design and analysis distributed databases Filtering

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