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检索条件"任意字段=International Conference on Advances in Parallel and Distributed Computing"

共 24675 条记录，以下是4841-4850 订阅

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Enhancing Availability for the MEC Service: CVaR-based Computation Offloading

Enhancing Availability for the MEC Service: CVaR-based Compu...

引用

international conference on parallel and distributed Systems (ICPADS)

作者： Shengli Pan Zhiyong Zhang Tao Xue Guangmin Hu School of Computer Science CUG Wuhan P.R. China Cyberspace Security Key Laboratory of Sichuan Province CETCSC Chengdu P.R. China School of Resources & Environment UESTC Chengdu P.R. China

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

Mobile Edge computing (MEC) enables mobile users to offload their computation loads to nearby edge servers, and is seen to be integrated in the 5G architecture to support a variety of low-latency applications and services. However, an edge server might soon be overloaded when its computation resources are heavily requested, and would then fail to process all of its received computation loads in time. Unlike most of existing schemes that ingeniously instruct the overloaded edge server to transfer computation loads to the remote cloud, we make use of the spare computation resources from other local edge servers by specially taking the risk of network link failures into account. We measure such link failure risks with the financial risk management metric of Conditional Value-at-Risk (CVaR), and well constrain it to the offloading decisions using a Minimum Cost Flow (MCF) problem formulation. Numerical results validate the enhancement of the MEC service's availability by our risk-aware offloading scheme.

关键词： Measurement conferences Routing Servers Risk management Task analysis Edge computing

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Utilizing Block Chain Technology in Various Application Areas of Machine Learning

Utilizing Block Chain Technology in Various Application Area...

引用

2019 international conference on Machine Learning, Big Data, Cloud and parallel computing, COMITCon 2019

作者： Shrivastava, Vineet Kumar, Suresh Department of Computer Science and Engineering Manav Rachna International Insititute of Research Studies Faridabad India

ISBN: (纸本)9781728102115

The BlockChain is a New Era Technology that provides secure technological impacts on various exchanges. It totally revolves over the distributed database. That framework helps to eliminate the failure at any single point that may affect in saving time and for the reduction of cost. Till now most of the work regarding BlockChain is limited to inventing technologies that are related to the encrypted form of money. The famous crypto currency like Bitcoin and Etherium are examples that are utilizing this technology. These are widely known as cryptographic money. The exchanges are done with the help of the Blockchain technique eliminate the presence of middleman. With the advancement of various smart devices and the field of Artificial Intelligence and Machine Learning, the interdisciplinary collaboration with Blockchain Technology may be very beneficial for future researches. As Blockchain technology provides a decentralized structure and huge amount of data is to be analyzed in Machine learning area, there are huge exploring abilities in the collaboration of the technology. In this paper, various aspects of the utilization of the Blockchain technology framework in the context of machine learning are discussed. The application areas of Machine learning where the implementation of Blockchain is beneficial are also explored. © 2019 IEEE.

关键词： Blockchain

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The Performance Analysis of distributed Storage Systems Used in Scalable Web Systems 39th

The Performance Analysis of Distributed Storage Systems Used...

引用

39th international conference on Information Systems Architecture and Technology (ISAT)

作者： Oles, Dominik Nowak, Ziemowit Tieto Czech Sro 28 Rijna 3346-91 Ostrava 70200 Czech Republic Wroclaw Univ Sci & Technol Fac Comp Sci & Management Wybrzeze Wyspianskiego 27 PL-50370 Wroclaw Poland

ISBN: (纸本)9783319999814;9783319999807

Scalable web systems are directly related to distributed storage systems used to process large amounts of data (big data). An example of such a system is Hadoop with its many extensions supporting data storage such as SQL-on-Hadoop systems and the "Parquet" file format. Another kind of systems for storing and processing big data are NoSQL databases, such as HBase, which are used in applications requiring fast random access. The Kudu system was created to combine the advantages of Hadoop and HBase and enable both effective data set analysis and fast random access. As subject of the research, performance analysis of the mentioned systems was performed. The experiment was conducted in the Amazon Web Services public cloud environment, where the cluster of nine virtual machines was configured. For research purpose, containing about billion rows fragment of "Wikipedia Page Traffic Statistics" public dataset was used. The results of the measurements confirm that the Kudu system is a promising alternative to the commonly used technologies.

关键词： Big Data Hadoop HBase Kudu

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Cost-Optimized parallel Computations Using Volatile Cloud Resources 16th

Cost-Optimized Parallel Computations Using Volatile Cloud Re...

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16th international conference on the Economics of Grids, Clouds, Systems, and Services, GECON 2019

作者： Haussmann, Jens Blochinger, Wolfgang Kuechlin, Wolfgang Parallel and Distributed Computing Group Reutlingen University Reutlingen Germany Symbolic Computation Group University of Tuebingen Tuebingen Germany

ISBN: (纸本)9783030360269

In recent years, the parallel computing community has shown increasing interest in leveraging cloud resources for executing parallel applications. Clouds exhibit several fundamental features of economic value, like on-demand resource provisioning and a pay-per-use model. Additionally, several cloud providers offer their resources with significant discounts;however, possessing limited availability. Such volatile resources are an auspicious opportunity to reduce the costs arising from computations, thus achieving higher cost efficiency. In this paper, we propose a cost model for quantifying the monetary costs of executing parallel applications in cloud environments, leveraging volatile resources. Using this cost model, one is able to determine a configuration of a cloud-based parallel system that minimizes the total costs of executing an application. © Springer Nature Switzerland AG 2019.

关键词： Economics

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Clustering and parallel Processing on GPU to Accelerate Circuit Transient Analysis

Clustering and Parallel Processing on GPU to Accelerate Circ...

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international conference on Advanced computing, Networking and Informatics, ICANI 2018

作者： Jagtap, Shital V. Rao, Y.S. RAIT Nerul Navi Mumbai India SPIT Andheri Mumbai India

ISBN: (纸本)9789811326721

Today, in the age of digital era, electronic circuit is the key component and its design, testing is validated through simulator. But even though use of simulator is cost-effective, large circuit simulation is quite time consuming. Also various iterations in transient analysis might make simulation slow. In this paper, we have addressed parallel computing approach using Graphics Processing Unit (GPU). Forming clusters of executable procedures are very crucial, so that it can be mapped to graphics processor for parallel processing. In every circuit nodal analysis finds current, voltage etc. at various nodes periodically. Matrix operations, linear–nonlinear equations, integration, differential equations, numerical methods are some of the very basic operations required in circuit analysis. Data-code partitioning, parallel data mapping, reductions, fast memory access, parallelizing loops are the strategies adopted for parallel processing on GPU. More than 40% speed gain is achieved on circuit having at least four components along with transient analysis for more than thousand iterations. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Graphics processing unit

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Optimizing Multi-way Theta Join for Data Skew in Sub-second Stream computing

Optimizing Multi-way Theta Join for Data Skew in Sub-second ...

引用

international conference on parallel and distributed Systems (ICPADS)

作者： Xiaopeng Fan Xinchun Liu Yang Wang Youjun Wang Jing Li Hangzhou Institute of Advanced Technology Hangzhou China University of Science and Technology of China Hefei China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

In sub-second stream computing, the answer to a complex query usually depends on operations of aggregation or join on streams, especially multi-way theta join. Some attribute keys are not distributed uniformly, which is called the data intrinsic skew problem, such as taxi car plate in GPS trajectories and transaction records, or stock code in stock quotes and investment portfolios etc. In this paper, we define the concept of key redundancy for single stream as the degree of data intrinsic skew, and joint key redundancy for multi-way streams. We present an execution model for multi-way stream theta joins with a fine-grained cost model to evaluate its performance. We propose a solution named Group Join (GroJoin) to make use of key redundancy during transmission and execution in a cluster. GroJoin is adaptive to data intrinsic skew in the way that it depends on the grouping condition we find out, i.e., the selectivity of theta join results should be smaller than 25%. Experiments are carried out by our MS-Generator to produce multi-way streams, and the simulation results show that GroJoin can decrease at most 45% transmission overheads with different key redundancies and value-key proportionality coefficients, and reduce at most 70% query delay with different key distributions. We further implement GroJoin in Multi-Way Stream Theta Join by Spark Streaming. The experimental results demonstrate that there are about 40%~50% join latency reduced after our optimization with a very small computation cost.

关键词： Adaptation models Simulation Redundancy distributed databases Trajectory Sparks Public transportation

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Automatic test case generation method of parallel multi-population self-adaptive ant colony algorithm

Automatic test case generation method of parallel multi-popu...

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international conference on Intelligent computing, Communication and Devices, ICCD 2017

作者： Zheng, Tengfei A College of Information Zhejiang Sci-Tech University Hangzhou310018 China

ISBN: (纸本)9789811089435

The design of test case automatic generation technology is an important step in the implementation of software automated testing. It plays an important role in guiding the later testing work and is the fundamental guarantee for improving software quality and reliability. Ant colony optimization (ACA), as a robust optimization algorithm, has a strong ability of global optimization. In this paper, an automatic test case generation method of parallel multi-group adaptive ant colony algorithm is presented. This algorithm adopts multi-group parallel search, adopts adaptive migration rule based on population diversity, and updates pheromone strategy adaptively according to fitness function in population. Theoretical analysis and simulation experiments show that the application of this algorithm is superior to the generation of test cases. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Software reliability

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Incremental Analysis of Large-Scale System Logs for Anomaly Detection

Incremental Analysis of Large-Scale System Logs for Anomaly ...

引用

IEEE international conference on Big Data (Big Data)

作者： Astekin, Merve Ozcan, Selim Sozer, Hasan TUBITAK BILGEM Inst Informat Technol Kocaeli Turkey Ozyegin Univ Dept Comp Sci Istanbul Turkey

ISBN: (纸本)9781728108582

Anomalies during system execution can be detected by automated analysis of logs generated by the system. However, large scale systems can generate tens of millions of lines of logs within days. Centralized implementations of traditional machine learning algorithms are not scalable for such data. Therefore, we recently introduced a distributed log analysis framework for anomaly detection. In this paper, we introduce an extension of this framework, which can detect anomalies earlier via incremental analysis instead of the existing offline analysis approach. In the extended version, we periodically process the log data that is accumulated so far. We conducted controlled experiments based on a benchmark dataset to evaluate the effectiveness of this approach. We repeated our experiments with various periods that determine the frequency of analysis as well as the size of the data processed each time. Results showed that our online analysis can improve anomaly detection time significantly while keeping the accuracy level same as that is obtained with the offline approach. The only exceptional case, where the accuracy is compromised, rarely occurs when the analysis is triggered before all the log data associated with a particular session of events are collected.

关键词： log analysis distributed systems parallel processing anomaly detection big data machine learning

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High-Performance Mining of COVID-19 Open Research Datasets for Text Classification and Insights in Cloud computing Environments

High-Performance Mining of COVID-19 Open Research Datasets f...

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IEEE international conference on Utility and Cloud computing (UCC)

作者： Jie Zhao Maria A. Rodriguez Rajkumar Buyya Cloud Computing and Distributed Systems Laboratory School of Computing and Information Systems The University of Melbourne Australia

ISBN: (数字)9780738123943

ISBN: (纸本)9781665415637

The COVID-19 global pandemic is an unprecedented health crisis. Many researchers around the world have produced an extensive collection of literature since the outbreak. Analysing this information to extract knowledge and provide meaningful insights in a timely manner requires a considerable amount of computational power. Cloud platforms are designed to provide this computational power in an on-demand and elastic manner. Specifically, hybrid clouds, composed of private and public data centers, are particularly well suited to deploy computationally intensive workloads in a cost-efficient, yet scalable manner. In this paper, we developed a system utilising the Aneka Platform as a Service middleware with parallel processing and multi-cloud capability to accelerate the data process pipeline and article categorising process using machine learning on a hybrid cloud. The results are then persisted for further referencing, searching and visualising. The performance evaluation shows that the system can help with reducing processing time and achieving linear scalability. Beyond COVID-19, the application might be used directly in broader scholarly article indexing and analysing.

关键词： Cloud computing Unified modeling language Task analysis COVID-19 Data visualization Computer architecture Scalability

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distributed Artificial Neural Network Model for Neutron Flux Mapping in Nuclear Reactors

Distributed Artificial Neural Network Model for Neutron Flux...

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international conference on computing and Networking Technology (ICCNT)

作者： S. K. Bahuguna S. Mukhopadhyay A.P. Tiwari Bhabha Atomic Research Centre Homi Bhabha National Institute Mumbai

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168524

Neutron flux distribution inside the core of large size nuclear reactors is a function of space and time. An online Flux Mapping System (FMS) is needed to monitor the core during the reactor operation. FMS estimates the core flux distribution from the measurements of few in-core detectors using an appropriate algorithm. Here, a distributed Artificial Neural Network (D-ANN) model is developed using parallel-forward multi-layer perceptron architecture to capture the spatial core flux variation in a nuclear reactor. The proposed D-ANN model is tested with simulated test case data of Advanced Heavy Water Reactor (AHWR) for multiple operating conditions of the reactor. The model estimates the neutron flux in all horizontal mesh locations (2-D) from the multiple networks distributed spatially across AHWR core. Estimation error using the proposed D-ANN model is found to be significantly lower than that with lumped ANN model. Validation exercises establish that this D-ANN model could effectively capture the spatial variations in the reactor core and therefore could be utilized for efficient flux mapping. The real time implementation of D-ANN based flux mapping method is also proposed.

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