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检索条件"任意字段=10th International Conference on Parallel and Distributed Computing, Applications and Technologies"

共 1765 条记录，以下是411-420 订阅

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On-Board-Unit Data: A Big Data Platform for Scalable storage and Processing

On-Board-Unit Data: A Big Data Platform for Scalable storage...

引用

international conference on Cloud technologies and applications (CloudTech)

作者： Giovanni Buroni Yann-Ael Le Borgne Gianluca Bontempi Karl Determe Computer Science Dept. Université Libre de Bruxelles Belgium Bruxelles Mobilite’ - Brussel Mobiliteit Bruxelles Belgium

ISBN: (纸本)9781728116389;9781728116372

this paper proposes and assesses a Big Data Platform for effective storage and analysis of On Board Unit (OBU) data related to the mobility of trucks in Belgium. the large volume and the streaming nature of the OBU data requires the setup of a big data platform for an efficient collection, storage and analysis. the solution relies on (i) the Hadoop distributed File System (HDFS) to store data, (ii) the Apache Parquet format for data compression and columnar storage, and (iii) Spark for parallel and streaming processing of data. Data replication, compression and columnar storage ensure robustness to node failure, data distribution, and faster access to data.

关键词： Big Data Sparks distributed databases Robustness Roads Engines Real-time systems

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A novel Optimization Technique for Mastering Energy Consumption in Cloud Data center 15

A novel Optimization Technique for Mastering Energy Consumpt...

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15th IEEE international Symposium on parallel and distributed Processing with applications (ISPA) / 16th IEEE international conference on Ubiquitous computing and Communications (IUCC)

作者： Ben Maaouia, Omar Fkaier, Hazem Jemni, Mohamed Cerin, Christophe Univ Tunis LATICE ENSIT Tunis Tunisia Univ Tunis El Manar FST Tunis Tunisia Univ Paris 13 LIPN UMR 7030 CNRS Villetaneuse France

ISBN: (纸本)9781538637906

Cloud computing is one of the most popular technologies nowadays because of its wide utilities and various benefits in several IT companies all over the world. However, in front of the increasing users' requests for computing services, cloud providers are encouraged to deploy large data centers, which consumes very large amount of energy and contribute to high operational costs. Among the effects, carbon dioxide emission rate is growing each day due to the huge amount of power consumption. this energy efficiency is an important issue in cloud computing, mainly due to the required electrical power to run these systems and to cool them. therefore, energy consumption has become a major concern for the widespread deployment of Cloud data centers. the growing importance for parallel applications in the Cloud introduces significant challenges in reducing energy consumption from hosted servers. this paper addresses the problem of placing independent applications on the physical servers (hosts) of a Cloud infrastructure. We proposed a novel heuristic to allocate applications so that total energy consumption is reduced. Our proposal respects various constraints e.g. the machines availability, capability and the duplication of applications. Experiments are illustrated to validate the potential of our approach.

关键词： Resource management for parallel and distributed systems Cloud computing Energy consumption Minimization

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A Scalable Real-Time Framework for DDoS Traffic Monitoring and Characterization 17

A Scalable Real-Time Framework for DDoS Traffic Monitoring a...

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10th international conference on Utility and Cloud computing (UCC) / 4th international conference on Big Data computing, applications and technologies (BDCAT)

作者： Huyn, Joojay Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90024 USA

ISBN: (纸本)9781450355490

Volumetric DDoS attacks continue to inflict serious damage. Many proposed defenses for mitigating such attacks assume that a monitoring system has already detected the attack. However, many proposed DDoS monitoring systems do not focus on efficiently analyzing high volume network traffic to provide important characterizations of the attack in real-time to downstream traffic filtering systems. We propose a scalable real-time framework for an effective volumetric DDoS monitoring system that leverages modern big data technologies for streaming analytics of high volume network traffic to accurately detect and characterize attacks.

关键词： network security apache spark ddos detection apache kafka streaming analytics distributed denial-of-service attacks data mining relational databases ddos monitoring

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A Transactional Model for parallel Programming of Graph applications on computing Clusters 10

A Transactional Model for Parallel Programming of Graph Appl...

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10th IEEE international conference on Cloud computing (CLOUD)

作者： Tripathi, Anand Padhye, Vinit Sunkara, Tara Sasank Tucker, Jeremy thirunavukarasu, BhagavathiDhass Pandey, Varun Sharma, Rahul R. Univ Minnesota Dept Comp Sci & Engn Minneapolis MN 55455 USA

ISBN: (纸本)9781538619933

We present here the results of our investigation of a transactional model of parallel programming on cluster computing systems. this model is specifically targeted for graph applications with the goal of harnessing unstructured parallelism inherently present in many such problems. In this model, tasks for vertex-centric computations are executed optimistically in parallel as serializable transactions. A key-value based globally shared object store is implemented in the main memory of the cluster nodes for storing the graph data. Task computations read and modify data in the distributed global store, without any explicitly programmed message-passing in the application code. Based on this model we developed a framework for parallel programming of graph applications on computing clusters. We present here the programming abstractions provided by this framework and its architecture. Using several graph problems we illustrate the simplicity of the abstractions provided by this model. these problems include graph coloring, k-nearest neighbors, and single-source shortest path computation. We also illustrate how incremental computations can be supported by this programming model. Using these problems we evaluate the transactional programming model and the mechanisms provided by this framework.

关键词： Computational modeling parallel processing parallel programming Adaptation models Message systems Data models

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Priority Based Resource Scheduling Techniques for a Resource Constrained Stream Processing System 17

Priority Based Resource Scheduling Techniques for a Resource...

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10th international conference on Utility and Cloud computing (UCC) / 4th international conference on Big Data computing, applications and technologies (BDCAT)

作者： Chakraborty, Rudraneel Majumdar, Shikharesh Carleton Univ Dept Syst & Comp Engn Ottawa ON Canada

ISBN: (纸本)9781450355490

A multitenant Storm cluster runs multiple stream processing applications and uses the default Isolation Scheduler to schedule them. Isolation Scheduler assigns resources to topologies based on static resource configuration and does not provide any means for prioritizing topologies based on their varying business requirements. thus, performance degradation, even complete starvation of topologies with high priority is possible when the cluster is resource constrained and comprises an inadequate number of resources. Two priority based resource scheduling techniques are proposed to overcome these problems. A performance analysis based on prototyping and measurements demonstrates the effectiveness of the proposed techniques.

关键词： big data resource scheduling resource allocation distributed systems resource management stream processing middleware

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Failure Analysis for distributed computing Environments 10

Failure Analysis for Distributed Computing Environments

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10th international conference on Utility and Cloud computing (UCC) / 4th international conference on Big Data computing, applications and technologies (BDCAT)

作者： Datskova, Olga Grigoras, Costin Shi, Weidong Univ Houston 4800 Calhoun Rd Houston TX 77004 USA European Org Nucl Res CH-1211 Geneva 23 Switzerland

ISBN: (纸本)9781450351959

distributed computing systems cover a broad range of computing infrastructures, which are heterogeneous, inter-connected and architected around stack-based deployments. Failure occurrences within such tightly-coupled systems while are expected, do not easily lend to predictive modeling due to the complex interactions between interconnected service layers. Ois work examines service level instabilities, occurring within data centers, participating in High Energy Physics (HEP) scienti_c research. We present a stability measure based on which a failure event selection process is deployed to detect periods of instability within individual data centers. Experts recognize that understanding conditions for failure is crucial when designing recovery procedures. For distributed computing systems risk and failure analysis facilitates implementation of measures for service availability, subsystem recovery and network redundancy.

关键词： Grid computing data centers reliability performance evaluation

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A distributed SHVC Video Transcoding System 10

A Distributed SHVC Video Transcoding System

引用

10th international conference on Ubi-media computing and Workshops (Ubi-Media)

作者： Hsu, Tz-Heng Wang, Zi-Ying Southern Taiwan Univ Sci & Technol Dept Comp Sci & Informat Engn Tainan Taiwan

ISBN: (纸本)9781538627617

Transcoding of a SHVC video is time-consuming especially with high definition resolutions. In this paper, we propose a distributed SHVC video transcoding system that can speed up the transcoding time of SHVC videos. By equipping distributed computing technologies, the proposed architecture can reduce the transcoding time of SHVC videos without the modifications of existing SHVC encoding algorithms and can satisfy the video requirements of high-definition television and high-resolution mobile devices.

关键词： Social Network Service Scalable Extensions of the High Efficiency Video Coding Video Streaming

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Multilevel concatenated codes with parallel construction 18

Multilevel concatenated codes with parallel construction

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18th international conference on parallel and distributed computing, applications and technologies, PDCAT 2017

作者： Chang, Hong Ma, Shang-Chih Lee, Chang-Hong Dept. of Electrical Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9781538631515

Using the multilevel concatenation, long block codes can be constructed from shorter component codes, resulting in much less decoding complexity. the component codes can also be constructed from multilevel concatenation so that a hierarchical multilevel concatenated structure is built. the decoding complexity of the hierarchical scheme can be further decreased. © 2017 IEEE.

关键词： Concatenated codes

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DIMSpan - Transactional Frequent Subgraph Mining with distributed In -Memory Dataflow Systems 17

DIMSpan - Transactional Frequent Subgraph Mining with Distri...

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10th international conference on Utility and Cloud computing (UCC) / 4th international conference on Big Data computing, applications and technologies (BDCAT)

作者： Petermann, Andre Junghanns, Martin Rahm, Erhard Univ Leipzig Leipzig Germany ScaDS Dresden Leipzig Dresden Germany

ISBN: (纸本)9781450355490

Transactional frequent subgraph mining identifies frequent structural patterns in a collection of graphs. this research problem has wide applicability and increasingly requires higher scalability over single machine solutions to address the needs of Big Data use cases. We introduce DIMSpan, an advanced approach to frequent subgraph mining that utilizes the features provided by distributed in-memory dataflow systems such as Apache Flink or Apache Spark. It determines the complete set of frequent subgraphs from arbitrary string-labeled directed multigraphs as they occur in social, business and knowledge networks. DIMSpan is optimized to runtime and minimal network traffic but memory-aware. An extensive performance evaluation on large graph collections shows the scalability of DIMSpan and the effectiveness of its optimization techniques.

关键词： distributed Frequent Subgraph Mining Shared Nothing Cluster

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Implementing algorithmic skeletons with bulk synchronous parallel ML 18

Implementing algorithmic skeletons with bulk synchronous par...

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18th international conference on parallel and distributed computing, applications and technologies, PDCAT 2017

作者： Loulergue, Frederic School of Informatics Computing and Cyber Systems Northern Arizona University FlagstaffAZ United States

ISBN: (纸本)9781538631515

Skeletal parallelism offers a good trade-off between programming productivity and execution efficiency. In this style of parallelism, an application is a composition of algorithmic skeletons. An algorithmic skeleton captures a pattern of parallel algorithm on a distributed data structure, and is also often associated with a sequential algorithm on a sequential data structure that is the counterpart of the parallel data structure. the algorithmic skeleton approach has been inspired by functional programming. It is therefore very natural to embed algorithmic skeletons in a functional programming language. In this paper we present a new algorithmic skeleton library for the statically typed functional language OCaml, and illustrate its use on some applications. this functional skeletal parallel programming library is implemented using the Bulk Synchronous parallel ML parallel programming library for OCaml. © 2017 IEEE.

关键词： parallel programming

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