distributed Denial of Service (DDoS) attacks pose a considerable threat to Cloud Computing, Internet of Things (IoT) and other services offered on the Internet. The victim server receives terabytes of data per second ...
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The proceedings contain 8 papers. The special focus in this conference is on Advanced parallelprocessing Technologies. The topics include: Improving memory access performance of in-memory key-value store using data p...
ISBN:
(纸本)9783319232157
The proceedings contain 8 papers. The special focus in this conference is on Advanced parallelprocessing Technologies. The topics include: Improving memory access performance of in-memory key-value store using data prefetching techniques;distributed data collection framework for failure prediction in tianhe supercomputers;optimizing the mapreduce framework for CPU-MIC heterogeneous cluster;stable matching scheduler for single-ISA heterogeneous multi-core processors;RPECA-rumor propagation based eventual consistency assessment algorithm;efficient implementation of MIPS code generator for the ionmonkey javascript compiler;effects of quenched disorder on liquid crystal and visual tracking based on convolutional deep belief network.
In light of recent advancements in Internet of Multimedia Things (IoMT) and 5G technology, both the variety and quantity of data have been rapidly increasing. Consequently, handling zero-shot cross-modal retrieval (ZS...
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This book constitutes the refereed proceedings of the 20th internationalconference on parallel and distributed Computing, Euro-Par 2014, held in Porto, Portugal, in August 2014. The 68 revised full papers presented w...
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ISBN:
(数字)9783319098739
ISBN:
(纸本)9783319098722
This book constitutes the refereed proceedings of the 20th internationalconference on parallel and distributed Computing, Euro-Par 2014, held in Porto, Portugal, in August 2014. The 68 revised full papers presented were carefully reviewed and selected from 267 submissions. The papers are organized in 15 topical sections: support tools environments; performance prediction and evaluation; scheduling and load balancing; high-performance architectures and compilers; parallel and distributed data management; grid, cluster and cloud computing; green high performance computing; distributed systems and algorithms; parallel and distributed programming; parallel numerical algorithms; multicore and manycore programming; theory and algorithms for parallel computation; high performance networks and communication; high performance and scientific applications; and GPU and accelerator computing.
This three-volume set LNCS 12452, 12453, and 12454 constitutes the proceedings of the 20th internationalconference on Algorithms and Architectures for parallelprocessing, ICA3PP 2020, in New York City, NY, USA, in O...
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ISBN:
(数字)9783030602451
ISBN:
(纸本)9783030602444
This three-volume set LNCS 12452, 12453, and 12454 constitutes the proceedings of the 20th internationalconference on Algorithms and Architectures for parallelprocessing, ICA3PP 2020, in New York City, NY, USA, in October 2020.;ICA3PP is covering the many dimensions of parallel algorithms and architectures, encompassing fundamental theoretical approaches, practical experimental projects, and commercial components and systems. As applications of computing systems have permeated in every aspects of daily life, the power of computing system has become increasingly critical. This conference provides a forum for academics and practitioners from countries around the world to exchange ideas for improving the efficiency, performance, reliability, security and interoperability of computing systems and applications. ICA3PP 2020 focus on two broadareas of parallel and distributed computing, i.e. architectures, algorithms and networks, and systems and applications.
The provisioning of high-performance computing infrastructure through cloud environments enables data intensive processing to be a viable solution. In this paper, we introduce a novel parallel computation model simila...
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ISBN:
(纸本)9781467372879
The provisioning of high-performance computing infrastructure through cloud environments enables data intensive processing to be a viable solution. In this paper, we introduce a novel parallel computation model similar to MapReduce framework. The proposed parallelized model incorporates a parallel execution strategy in worker nodes to decrease execution response times in cloud environments. The parallelized model adopts efficient local memory management techniques in the worker nodes to reduce memory transfer overheads. For evaluation, we compared the proposed framework with the state of art Hadoop MapReduce framework. From experiments on benchmark datasets, it turns out that the parallelized model reduces the execution times by about 45.86%. Those experimental results indicate the efficiency and the scalability of proposed framework on cloud environments.
GPUs have recently evolved into very fast parallel co-processors capable of executing general purpose computations extremely efficiently. At the same time, multi-core CPUs evolution continued and today's CPUs have...
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Tremendous growth in online applications require shortest path queries to solve its problem where the data volume is very high. Among the various shortest path algorithms considered, Dijkstra's shortest path algor...
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Technology enhancement makes the computing available everywhere and provides access to widely distributed resources which is known as Mobile Computing. Mobile computing enables innovative applications through the shar...
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ISBN:
(纸本)9781479976836
Technology enhancement makes the computing available everywhere and provides access to widely distributed resources which is known as Mobile Computing. Mobile computing enables innovative applications through the sharing of computing resources among mobile devices such as notebook, smart phones and Personal Digital Assistant (PDAs) without any pre-existing infrastructure. Mobile computing includes a number of technologies and devices. The state of the user, static or mobile, does not affect the information management capability of the mobile platform. A user can continue to access and perform data manipulation during the state of mobility by using mobile computing devices. Delivering data packets between pair of processors is a primary responsibility of any mobile computing network This activity is performed using a routing strategy Maximize reliability of the data packets always a major concern while transmitting the data from one point to another point. In mobile computing it is the process of determining the data path through the network that data packets will move from the one computing device to another computing device. Routing strategy for data transmission is an important factor for achieving high degree of reliability. Multiple data packets are move within a network from source to destination in order to execute on available processing units with the objective of getting maximum reliability of data packets. This research paper present a routing strategy to achieve maximum data packets transmission reliability in mobile computing network during the data transmission.
Volunteer Computing (VC) has been successfully applied to many compute-intensive scientific projects to solve embarrassingly parallel computing problems. There exist some efforts in the current literature to apply VC ...
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ISBN:
(数字)9789811063855
ISBN:
(纸本)9789811063855;9789811063848
Volunteer Computing (VC) has been successfully applied to many compute-intensive scientific projects to solve embarrassingly parallel computing problems. There exist some efforts in the current literature to apply VC to data-intensive (i.e. big data) applications, but none of them has confirmed the scalability of VC for the applications in the opportunistic volunteer environments. This paper chooses MapReduce as a typical computing paradigm in coping with big data processing in distributed environments and models it on DHT (distributed Hash Table) P2P overlay to bring this computing paradigm into VC environments. The modelling results in a distributed prototype implementation and a simulator. The experimental evaluation of this paper has confirmed that the scalability of VC for the MapReduce big data (up to 10 TB) applications in the cases, where the number of volunteers is fairly large (up to 10K), they commit high churn rates (up to 90%), and they have heterogeneous compute capacities (the fastest is 6 times of the slowest) and bandwidths (the fastest is up to 75 times of the slowest).
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