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检索条件"任意字段=3rd International Conference on Parallel and Distributed Processing and Applications"

共 3056 条记录，以下是411-420 订阅

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DEO: A Smart Dynamic Edge Offloading Scheme using processing Resources of Nearby Wireless Devices to Form an Edge Computing Engine 3

DEO: A Smart Dynamic Edge Offloading Scheme using Processing...

引用

3rd IEEE international conference on Cloud and Fog Computing Technologies and applications (IEEE Cloud Summit)

作者： Lami, Ihsan Alshahib Al-ameri, Ali Sch Comp Appl Comp Buckingham England

ISBN: (纸本)9781728131016

Edge computing reduces connectivity costs and network traffic congestion over cloud computing, by offering local resources (processing and storage) at one hop closer to the end-users. I.e. it reduces the Round-Trip Time (RTT) for offloading part of the processing workload from end-nodes/devices to servers at the edge. However, edge servers are normally pre-setup as part of the overall computing resource infrastructure, which is tough to predict for mobile/IoT deployments. This paper introduces a smart Dynamic Edge Offloading scheme, (we named it DEO), that forms the "edge computing resource" on-the-go, as needed from nearby available devices in a cooperative sharing environment. This is especially necessary for hosting mobile/IoT applications traffic at crowded/urban situations, and, for example, when executing a processing intensive Mobile Cloud Computing Service (MCCS) on a Smartphone (SP). DEO implementation is achieved by using a short-range wireless connectivity between available cooperative end-devices, that will form the edge computing resource. DEO includes an intelligent cloud-based engine, that will facilitate the engagement of the edge network devices. For example, if the end-device is a SP running an MCCS, DEO will partition the processing of the MCCS into sub-tasks, that will be run in parallel on the newly formed "edge resource network" of other nearby devices. Our experiments prove that DEO reduces the RTT and cost overhead by 62.8% and 75.5%, when compared to offloading to a local edge server or a cloud-based server.

关键词： mobile cloud computing services edge computing offloading parallel processing

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distributed Joint Optimization of Radio and Computational Resources for Mobile Cloud Computing 3

Distributed Joint Optimization of Radio and Computational Re...

引用

3rd IEEE international conference on Cloud Networking (CloudNet)

作者： Sardellitti, S. Scutari, G. Barbarossa, S. Sapienza Univ Rome Dept Informat Engn Elect & Telecommun Rome Italy Sapienza Univ Rome Dept Informat Engn Elect & Telecommun Rome Italy SUNY Buffalo Dept Elect Engn Buffalo NY USA

ISBN: (纸本)9781479927302

Mobile computation offloading of computational intensive tasks from mobile devices to surrogate cloud servers has been recently envisaged as a promising technique to enhance the computational capacity of the mobile devices. Within this framework we consider a MIMO multicell system wherein several Mobile Users (MUs) ask for computation offloading to a common cloud server through their femto-access points. We formulate the computation offloading problem as a joint optimization of the radio and computational resources in order to minimize the overall users' energy consumption while meeting the latency constraints imposed by the applications. To solve this non-convex problem we hinge on successive convex approximation techniques by showing that the original problem can be decomposed in parallel convex subproblems. Hence we devise an iterative algorithm which can be implemented in a distributed manner across the access points through dual/primal decomposition techniques requiring limited coordination/signaling with the cloud.

关键词： Computation offloading cloud computing distributed resource allocation successive convex approximation

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parallel and distributed particle collision simulation with decentralized control

引用

3rd international conference on Grid and Pervasive Computing

作者： Li, Ruipeng Jiang, Hai Su, Hung-Chi Zhang, Bin Jenness, Jeff Arkansas State Univ Dept Computat Sci State Univ AR 72467 USA Univ Arkansas Dept Chem & Phys Fayetteville AR 72701 USA

ISBN: (纸本)9783540680819

Scientific computing applications with highly demanding data capacity and computation power drive a computing platform migration from shared memory machines to multi-core/multiprocessor computer clusters. However, overheads in coordinating operations across computing nodes could counteract the benefit of having extra machines. Furthermore, the hidden dependency in applications slows down the simulation over non-shared memory machines. This paper proposed a framework to utilize multi-core/multiprocessor clusters for distributed simulation. Among several coordination schemes, decentralized control approach has demonstrated its effectiveness in reducing the communication overheads. A speculative execution strategy is applied to exploit parallelism thoroughly and overcome strong data dependency. Performance analysis and experiments are provided to demonstrate the performance gains.

关键词： parallel processing systems

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Poster Abstract: State Estimation and Sensor Fusion for Autonomous Driving in Mixed-Traffic Urban Environments 12

Poster Abstract: State Estimation and Sensor Fusion for Auto...

引用

3rd IEEE/ACM international conference on Cyber-Physical Systems (ICCPS)

作者： Adamey, Emrah Basciftci, Yuksel Ozan Gong, Peng Kurt, Arda Ozguner, Fusun Ozguner, Umit Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA

ISBN: (纸本)9780769546957

This poster investigates sensory data processing, filtering and sensor fusion methods for autonomous vehicles operating in real-life, urban environments with human and machine drivers, and pedestrians. Extended Kalman Filters were used to develop decentralized data fusion algorithms for communicating vehicles;Particle Filters were improved by assigning trust/confidence values in order to overcome faulty/compromised sensors;and the computational cost of particle filters were distributed by parallelizing the load using the developed Shared-Memory Systematic Resampling algorithm.

关键词： autonomous vehicles sensor fusion distributed data fusion extended Kalman filters particle filters parallel processing

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Research on distributed parallel Eclat Optimization Algorithm 3

Research on Distributed Parallel Eclat Optimization Algorith...

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3rd international conference on Artificial Intelligence and Big Data (ICAIBD)

作者： Huang Qiufeng Li Qiang Huang Shiya Chen Yingcong Southwest Univ Sci & Technol Sch Informat Engn Mianyang Sichuan Peoples R China

ISBN: (数字)9781728197418

ISBN: (纸本)9781728197418

Frequent Itemset Mining (FIM) is the core of tasks such as association rules and sequential pattern mining. With the increasing amount of data, traditional FIM algorithms become inefficient due to excessive resource requirements or high communication costs. In this paper, the Eclat algorithm in the frequent itemset mining algorithm is taken as the research point, and the parallel Eclat optimization algorithm BPEclat (Balanced parallel Eclat) based on Spark is proposed to solve the performance shortcoming of Eclat algorithm in serial processing large-scale data. The algorithm is improved and optimized from many aspects: combining the pre-pruning and post-pruning depth pruning strategies to reduce the calculation of irrelevant itemset, compressing the candidate set size;using the prefix term to divide the data set, and using the range partitioning idea to balance the calculations node load, improve the parallel computing power of the algorithm. The experimental results show that the proposed BPEclat algorithm reduces the candidate set size by 25.3% and the time consumption by 32.5%. Therefore, it is possible to process massive amounts of data more efficiently and reliably, and has good scalability and universality.

关键词： frequent itemsets bit storage spark parallel computing

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KOSHIK- A large-scale distributed computing framework for NLP

KOSHIK- A large-scale distributed computing framework for NL...

引用

3rd international conference on Pattern Recognition applications and Methods, ICPRAM 2014

作者： Exner, Peter Nugues, Pierre Department of Computer Science Lund University Lund Sweden

ISBN: (纸本)9789897580185

In this paper, we describe KOSHIK, an end-to-end framework to process the unstructured natural language content of multilingual documents. We used the Hadoop distributed computing infrastructure to build this framework as it enables KOSHIK to easily scale by adding inexpensive commodity hardware. We designed an annotation model that allows the processing algorithms to incrementally add layers of annotation without modifying the original document. We used the Avro binary format to serialize the documents. Avro is designed for Hadoop and allows other data warehousing tools to directly query the documents. This paper reports the implementation choices and details of the framework, the annotation model, the options for querying processed data, and the parsing results on the English and Swedish editions of Wikipedia. Copyright © 2014 SCITEPRESS.

关键词： MapReduce

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Compilation to parallel programs from constraints

Compilation to parallel programs from constraints

引用

Proceedings of the 1996 3rd international conference on High Performance Computing, HiPC

作者： John, Ajita Browne, J.C. Univ of Texas at Austin Austin United States

This paper describes the first results from research on the compilation of constraint systems into task level parallel programs in a procedural language. This is the only research of which we are aware which attempts to generate efficient parallel programs for numerical computation from constraint systems. Computations are expressed as constraint systems. A dependence graph is derived from the constraint system and a set of input variables. The dependence graph, which exploits the parallelism in the constraints, is mapped to the language CODE, which represents parallel computation structures as generalized dependence graphs. Finally, parallel C programs are generated. To extract parallel programs of appropriate granularity, the following features are included: (i) modularity, (ii) operations over structured types as primitives, (iii) sequential C functions. A prototype of the compiler has been implemented. The domain of matrix computations is targeted for applications. Initial results are very encouraging.

关键词： parallel processing systems

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Using Dedicated and Opportunistic Networks in Synergy for a Cost-effective distributed Stream processing Platform

Using Dedicated and Opportunistic Networks in Synergy for a ...

引用

14th international conference on parallel and distributed Systems

作者： Asaduzzaman, Shah Maheswaran, Muthucumaru Univ Ottawa SITE Ottawa ON K1N 6N5 Canada McGill Univ SOCS Montreal PQ H3A 2A7 Canada

ISBN: (纸本)9780769534343

This paper presents a case for exploiting the synergy of dedicated and opportunistic network resources in a distributed hosting platform for data stream processing applications. Our previous studies have demonstrated the benefits of combining dedicated reliable resources with opportunistic resources in case of high-throughput computing applications, where timely allocation of the processing units is the primary concern. Since distributed stream processing applications demand large volume of data transmission between the processing sites at a consistent rate, adequate control over the network resources is important here to assure a steady flow of processing. In this paper, we propose a system model for the hybrid hosting platform where stream processing servers installed at distributed sites are interconnected with a combination of dedicated links and public Internet. Decentralized algorithms have been developed for allocation of the two classes of network resources among the competing tasks with an objective towards higher task throughput and better utilization of expensive dedicated resources. Results from extensive simulation study show that with proper management, systems exploiting the synergy of dedicated and opportunistic resources yield considerably higher task throughput and thus, higher return on investment over the systems solely using expensive dedicated resources.

关键词： Throughput

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Scheduling Bag-of-Task-Chains in distributed Systems 14

Scheduling Bag-of-Task-Chains in Distributed Systems

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14th IEEE international Symposium on Autonomous Decentralized Systems (ISADS) / 3rd international Workshop on Service Assurance in System Wide Information Management (SASWIM)

作者： Stavrinides, Georgios L. Karatza, Helen D. Aristotle Univ Thessaloniki Dept Informat Thessaloniki 54124 Greece

ISBN: (纸本)9781728116723

distributed resources required for processing complex parallel applications are becoming larger in scale day-by-day. They need to be used efficiently in order to provide Quality of Service (QoS). Therefore, resource allocation and scheduling are of paramount importance. In this paper we investigate issues involved with the scheduling of parallel jobs that are bag-of-task-chains in distributed systems. Two task-chain scheduling techniques in two different cases of resource allocation are studied. The performance of the task-chain scheduling algorithms is evaluated via simulation, under different cases of system workload. The experimental results show that the performance of the task-chain scheduling strategies depends on the employed resource allocation techniques.

关键词： bag-of-task-chains scheduling simulation distributed systems performance

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A Three-Level parallel Algorithm For MrBayes 3.2 15

A Three-Level Parallel Algorithm For MrBayes 3.2

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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： Zhao, Mingjie Ren, Qiang Wang, Yilin Deng, Ruikang Ren, Mingming Wang, Gang Liu, Xiaoguang Nankai Univ Nankai Baidu Joint Lab Tianjin Peoples R China

ISBN: (纸本)9781538637906

MrBayes is a popular bioinformatics software that is widely used in phylogenetic analysis. The core algorithm of Mrbayes is Metropolis Coupled Markov Chain Monte Carlo (MC3). However, when dealing with large data sets, MC3 algorithm is too slow to meet researcher's requirements. Although several parallelizations have been proposed for MrBayes, such as MPI (Message Passing Interface) based MrBayes, GPU (Graphics processing Unit) based MrBayes, there is still no efficient parallel algorithm to fully utilize computing power of modern CPU and computer architecture. This paper (a) presents a new three-level hybrid parallel algorithm, include data-level parallelism (DLP), thread-level parallelism (TLP), and process-level parallelism (PLP), which can be used on most modern multi-core computers;(b) compares the performance of different combinations of parallel strategies on real-world protein data sets. The experimental results show that, this hybrid parallel algorithm does convert more computing powers into higher speedup. Furthermore, the proposed algorithm's speedup is near the speedup on one GPU at the same data sets. This algorithm is fit for practical use in phylogenetic inferences.

关键词： MrBayes SIMD hybrid parallel computing

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