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检索条件"任意字段=2nd International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering"

共 1401 条记录，以下是1281-1290 订阅

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A Load Balancing Mechanism Based on Ant Colony and Complex Network Theory in Open cloud computing Federation

A Load Balancing Mechanism Based on Ant Colony and Complex N...

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2010 The 2nd international conference on Industrial Mechatronics and Automation (ICIMA 2010)

作者： Zehua Zhang and Xuejie Zhang School of Information Science and engineering,Yunnan University,Kunming,P.R.China

Although cloud computing is generally recognized as a technology which will has a significant impact on IT in the ***,cloud computing is still in its infancy,many crucial problems need to be solved for the realization of the fine scenery which theoretically depicted by cloud *** balancing is one of these problems;it plays a very important role in the realization of Open cloud computing *** proposal a load balancing mechanism based on ant colony and complex network theory in open cloud computing federation in this paper,it improves many aspects of the related Ant Colony algorithms which proposed to realize load balancing in distributed system,Furthermore,this mechanism take the characteristic of Complex Network into ***,the performance of this mechanism is qualitatively analyzed,and a prototype is developed to enable the quantitative analysis,simulation results manifest the analysis.

关键词： load balancing ant colony complex network cloud computing federation

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A Real-time Workload Driven Approach for the cloud

A Real-time Workload Driven Approach for the Cloud

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2010 2nd international conference on Modeling, Simulation,and Visualization Methods(WMSVM 2010)

作者： Yuanyan Gu College of Computer Information & engineering Zhejiang Gongshang University Hangzhou,China Yujia Ge College of Computer Information & engineering Zhejiang Gongshang University Hangzhou,China

Recently,cloud computing has shown promising foreground in distributed *** are many computer resources on the cloud,but how to schedule these resources reasonable is a very important and difficult *** this paper,we provide a real-time workload driven approach for the cloud,and the workload between slave and master on the cloud will keep balance *** are four important parts to get the goal of real-time workload driven functions for cloud,and we described and discussed the implementation of the *** is applicable in the larger-scale and real-time required functions particularly.

关键词： cloud computing Real-time Workload Driven Statistics

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Research on multi-chain blockchain consensus mechanism model under cloud computing platform

Research on multi-chain blockchain consensus mechanism model...

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Electrical, Automation and Computer engineering (ICEACE), international conference on

作者： Yuan Li Modern Finance Industry School Shandong Institute of Commerce and Technology Jinan China

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368215

This paper combines the advantages of cloud computing platform in resource scheduling, proposes a consensus mechanism model for multi-chain blockchain, and designs corresponding algorithms, aiming to optimize the overall performance of the system and the efficiency of cross-chain communication. The model achieves efficient communication and load balancing between chains by introducing the distributed resource scheduling scheme of cloud computing. In terms of consensus mechanism, this paper uses a dynamic adjustment strategy based on cloud computing resources to improve the throughput and consensus speed of the system. This paper conducts detailed simulation tests to verify the effectiveness of the model. The test results show that under different loads, the system shows significant improvements in consensus delay, resource utilization and cross-chain communication success rate. Especially under high load, the system throughput is increased by about 20%, the consensus delay is reduced by 15%, and the cross-chain communication success rate is increased by 10%. These data show that the model proposed in this paper can effectively use the resources of the cloud computing platform to provide more flexible resource scheduling and cross-chain communication support while improving the performance of the multi-chain blockchain system.

关键词： cloud computing Processor scheduling Computational modeling Simulation Throughput Dynamic scheduling Consensus protocol Delays Resource management Load modeling

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Stock market prediction using Hadoop Map-Reduce ecosystem

Stock market prediction using Hadoop Map-Reduce ecosystem

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international conference on computing for Sustainable Global Development (IndIACom)

作者： Arun Kumar Dubey Vanita Jain A.P Mittal Bharati VidyaPeeth's College of Engineering New Delhi BharatiVidyaPeeth's College of Engineering New Delhi NSIT Delhi

Now a day's Academia and Industry people are working on large amount of data, in petabyte, and they are using technique of Map Reduce for data analysis. The input for such framework is very large and main requirement for theses inputs are that all the files cannot be kept on single node. After putting all data on single machine, we have to process it parellely. Hadoop is a framework which enables applications to work on large amounts of data on clusters with thousands of nodes. A distributed file system (HDFS) stores the data on these nodes, enabling a high bandwidth across the cluster. Hadoop also implements a parallel computational algorithm, MapReduce, which divides the main task into small chunks and these work in parallel known as mapping, and all the results are combined into a final output, the reduce stage. This paper is based on Hadoop Based Stock forecasting using neural networks. Stock Market has high profit and high risk features thts why its prediction must be in the parallel of accuracy, the main issue about such data are , these are very complex nonlinear function and can only be learnt by a data mining method such as neural networks to recognize future market trend. This project focuses mainly on learning of feed-forward artificial neural network (ANN) on a hadoop framework. We have tried to utilize distributing capability of Hadoop ecosystem which is parallel too. Map-Reduce for managing training of large datasets on the neural network. Our experimental results basically show the speedup achieved by increasing number of processors to the hadoop cluster for an artificial neural network.

关键词： Training Biological neural networks parallel processing File systems distributed databases Indexes

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The parallel DCNN-LSTM Model for cloud computing Prediction Based on an Improved SG Filter

The Parallel DCNN-LSTM Model for Cloud Computing Prediction ...

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Image Processing and Computer Applications (ICIPCA), IEEE international conference on

作者： Bowen Yang Kaiming Jin Longhua Zou School of Computer Science and Engineering Beihang University Beijing China School of Economics Xiamen University Xiamen China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

It is crucial to accurately predict the workload of large-scale cloud computing resources in order to better allocate and utilize them, but this has always been seen as a challenging task. Recently, LSTM has been widely used in the field of time series prediction, but it is not sufficient to learn complex and noisy patterns. This work aims to solve the accuracy problem of cloud data prediction and proposes a parallel dilated convolutional network. Firstly, in order to smooth and remove noise from the data, an improved SG filter that avoids future data leakage is proposed. Afterwards, this work combines LSTM with dilated convolution and adopts a parallel architecture. The purpose is to enable the model to learn patterns and potential representations of data from multiple scales, and enhance model's generalization and robustness. Finally, this work validated the performance of the proposed model on real datasets, Google Cluster Trace. Experimental results have shown that the proposed model performs better than LSTM and some widely used variants.

关键词： cloud computing Computational modeling Time series analysis Predictive models Feature extraction Data models Robustness parallel architectures Convolutional neural networks Long short term memory

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Voltage regulation and harmonic compensation in stand-alone inverter-based distributed generation using non-sinusoidal hysteresis voltage control

Voltage regulation and harmonic compensation in stand-alone ...

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Workshop on Power Electronics and Intelligent Transportation System, PEITS

作者： Masume Khodsuz Abdolreza Sheikholeslami Department of Electrical and Computer Engineering Babol Noshirvani University of Technology Babol Iran

During the islanding mode, whenever the power grid is not available, distributed-generation (DG) produces sinusoidal output voltage and supplies nonlinear loads, thus voltage at point of common coupling (PCC) becomes non-sinusoidal. The situation gets particularly aggravated during the islanding mode, when the power grid is not available. This deteriorates the performance of other loads connected in parallel. In this paper a scheme based on non-sinusoidal hysteresis voltage control (NSHVC) is proposed to regulate and produce a sinusoidal voltage at the PCC. It will be demonstrated that without any current sensor or compensating device, the individual harmonic components and THD of the supply voltage at the PCC can be reduced to the desired level. Simulations have been performed to supply the viability of the proposed scheme and all of the results are presented.

关键词： Voltage control distributed control Hysteresis Pulse width modulation inverters Pulse width modulation converters Power system harmonics Pulse width modulation Wind energy generation Impedance Power electronics

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Intelligent cloud Data Indexing and Retrieval Through Random Forest Algorithms

Intelligent Cloud Data Indexing and Retrieval Through Random...

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international conference on Signal Processing, Communication, Power and Embedded System (SCOPES)

作者： K. Mohan Raj A. Karthikeyan Department of CSE. Mahendra Engineering College Namakkal Tamil Nadu India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Another significant research issue is that of data indexing and retrieval in cloud computing context given that the virtues of velocity, variety, and volume are escalating pervasively. Indexing techniques have in the past proved difficult for real-time cloud applications requirements. This research focuses on the utilization of developed Random Forest algorithms as a new conception of smart cloud data indexing and search. The reported method exploits the strict parallelism and scaling properties of Random Forest algorithms to design a distributed and robust indexing scheme that can efficiently process lager cloud data. During the indexing phase, feature extraction takes place followed by partitioning of the data, construction of several decision trees for search capability. For data retrieval experiments performed on benchmark cloud data sets, the results indicate that the proposed Random Forest-based indexing achieves average accuracy equal to 92.4%, the efficiency is 15-20% higher than traditional indexing methods. The method also stands high on parameters of data skew and node failure for providing reliable performance with frequent change in cloud-based infrastructure. The introduction of Random Forest algorithms into cloud data indexing serves as a boiling prospect in creating scalability and efficiency in the discovery process amidst the growing big data problem. The developments proposed within this research point to the future developments regarding intelligent cloud data management and create new opportunities for this topic within the field of cloud computing.

关键词： cloud computing Scalability Signal processing algorithms Signal processing parallel processing Real-time systems Partitioning algorithms Reliability Random forests Indexing

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Research on Virtual Power Plants Participating in Ancillary Service Market

Research on Virtual Power Plants Participating in Ancillary ...

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Electrical engineering and Control Science (IC2ECS), international conference on

作者： Guohui Lan Zitao Zhang Minxing Guo Li Lan Ran Lyu Su Wang Institute of Economics and Technology State Grid Shanghai Electric Power Company Shanghai China School of Electrical Engineering Southeast University Nanjing China

Under the “Double Carbon” strategy, the speed of new energy installation and grid connection has been continuously improved. In order to better absorb new energy power generation and ensure the safe and stable operation of the power system, it is urgent to provide ancillary service resources such as frequency regulation and peak shaving. The virtual power plant aggregates the distributed energy scattered in the power grid through advanced communication, computing, dispatching, market and other means, making it a “power generation system” that can be uniformly dispatched, and then follow the dispatching instructions and participate in the ancillary service market. Firstly, the relevant structure and development of virtual power plant is introduced. Secondly, the relevant types of virtual power plants participating in the ancillary service market are analyzed. Thirdly, the actual situation of virtual power plants participating in ancillary service markets for frequency regulation and peak shaving is compared and analyzed with reference to the market access conditions, compensation mechanism and allocation mechanism, which lays a theoretical foundation for different provinces to further improve the mechanism of virtual power plants participating in ancillary service markets. At last, according to China’s national conditions, suggestions on the construction of market mechanism for virtual power plants to participate in ancillary services are proposed.

关键词： Frequency modulation Urban areas Virtual power plants Dispatching Regulation Power grids Telecommunication computing

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Accelerating MatLab code using GPU: A review of tools and strategies

Accelerating MatLab code using GPU: A review of tools and st...

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international conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC)

作者： Baida Zhang Shuai Xu Feng Zhang Yuan Bi Linqi Huang National laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha China Department of Information Engineering Academy of Armored Forces Engineering Beijing China Inspur (Beijing) Electronic Information Industry Company Limited Beijing China Naval Academy of Armament Beijing China School of Resources and Safety Engineering Central South University Changsha China

Lots of toolboxes of accelerating MatLab using GPU are available now[1], but, users are confused by which toolbox is best suitable for a particular task. Three toolboxes-Jacket, GPUmat, and parallel computing Toolbox of MatLab are selected. For each toolbox, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which toolbox is appropriate for a given task. Strategies of whether a function should execute on GPU are given after a formula analysis. The analysis is also a framework for program automatically decides which function is cost-efficient to execute on GPU. A series of benchmark of different types of computing, including data transfer between GPU and CPU, data matrix Generation, matrix operation and GPU functions were tested in all three toolboxes. And the results show that Jacket is the best one. Some advices to improve the performance of toolboxes are given in the end.

关键词： Graphics processing unit Acceleration parallel processing Computer architecture conferences Licenses MATLAB

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An Overview of Experimental Load Tracking, Billing and Energy Consumption Monitoring Device using IoT

An Overview of Experimental Load Tracking, Billing and Energ...

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Applied Artificial Intelligence and computing (ICAAIC), international conference on

作者： Linda Cecilia Tracy Maule K. Raj Thilak B. Gayathri Devi P. Anbarasu Avvaru Nohit Venkata Seetha Sai Balaji N. Franklin Department of Electrical and Electronics Engineering Sri Eshwar College of Engineering Coimbatore India Goldwin Industries Coimbatore India

In an electricity network, a smart grid's aim is to provide electronic power conditioning and production control and twitches in energy usage. These issues have been severely recognized in developing countries such as India in which there exists a large monotonous gap between required and available energy bringing the need for load-shedding. To promote electrical savings, smart grids have now been known to employ Demand Side Management (DSM). Future DSM systems should be designed such that it exploit's technologies such as Web of Things (WOT) and utility computing. This research study provides a planning structure, discovery and integration of a WOT and utility-based Electricity Monitoring System (EMS), with the ability to generate a fill profile of the end user. The designed EMS is easily installed at the premises of the consumer where it performs tasks like monitoring the consumption of the end user and uploads the sensed data to Think-Speak cloud over Information Transfer Protocol.

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