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共 4336 条记录，以下是1431-1440 订阅

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Impact of network Topology on the Performance of DML: Theoretical Analysis and Practical Factors

Impact of Network Topology on the Performance of DML: Theore...

引用

IEEE Annual Joint conference: INFOCOM, IEEE Computer and Communications Societies

作者： Shuai Wang Dan Li Jinkun Geng Yue Gu Yang Cheng Department of Computer Science and Technology Tsinghua University

To deal with the increasingly larger input data and model sizes, it has become necessary to scale the training of machine learning models to multiple nodes, even a server cluster, which we call distributed machine learning, or DML. However, DML utilizes more computation power at the cost of high communication overhead, which may limit the overall performance in turn. In this paper, we study the impact of network topology on the DML performance both in theory and in practice. We compare two representative network topologies, namely, Fat-Tree which is widely-used in modern data centers, and BCube, which is a low-cost and server-centric network topology, both running on top of RDMA. The results show that Fat-Tree not only has theoretically higher global synchronization time (GST) than BCube, but its practical GST (by NS-3 based simulation) is also considerably larger than the theoretical one. By analyzing the large-scale simulation traces, we find that the root cause for the gap in Fat-Tree comes from the load imbalance among the multiple parallel paths as well as the inevitable PFC frames, both of which do not appear in BCube. For a cluster of around 250 servers, BCube achieves 53%\sim 70% lower GST than Fat-Tree from the simulation. As a result, we suggest using server-centric network topology such as BCube, instead of the common Fat-Tree network, to build a special-purpose DML cluster, due to its parallel synchronization, RDMA friendliness, natural load balance, as well as low economical cost.

关键词： Synchronization network topology Servers Computational modeling parallel processing Transport protocols Data models

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Trust Evaluation Model in Edge Computing based on Integrated Trust 18

Trust Evaluation Model in Edge Computing Based on Integrated...

引用

International conference on Algorithms, Computing and Artificial Intelligence (ACAI)

作者： Ma, Xinyu Li, Xiaoyong Beijing Univ Posts & Telecommun Minist Educ Key Lab Trustworthy Distributed Comp & Serv Beijing Peoples R China

ISBN: (纸本)9781450366250

With the rapid development of Internet of Things and the popularization of 4G/5G wireless network, traditional cloud computing has been unable to deal with the massive data generated by network edge devices efficiently. Therefore, edge computing arises at the historic moment. The operation of applications, data and services are handled by the central node to the logical edge node of the network in edge computing. Due to the open characteristics of edge computing, such as content awareness, real-time computing and parallel processing, data security and privacy issues that already exist in cloud computing environment become more prominent. This paper studies issues related to data security and privacy protection in edge computing. We propose a model based on integrated trust to evaluate the credibility of edge nodes. Then the effectiveness and reliability of the model are verified by experimental simulation.

关键词： Edge computing Trust evaluation Integrated trust

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Model-Aware parallelization Strategy for Deep Neural networks' distributed Training

Model-Aware Parallelization Strategy for Deep Neural Network...

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International conference on Advanced Cloud and Big Data (CBD)

作者： Zhaoyi Yang Fang Dong Southeast University Nanjing China

It is standard practice today to parallelize deep neural networks' (DNNs') training across distributed clusters. The most common parallelization strategy is data parallelism, but its speedup is limited by tremendous communication overhead. To alleviate this problem, we employ hybrid parallelism, where the size of data to be transferred is decreased. Hybrid parallelism divides machines into groups. Machines in the same group maintain different parts of the model and carry out training tasks collaboratively, and all the groups perform computations simultaneously. based on hybrid parallelism, we propose a Model-aware parallelization Strategy, with the purpose of completing the training task as quickly as possible. To determine which strategy to use, we formulate a combinatorial optimization problem and use a greedy heuristic algorithm to decide how to group machines and how to partition the network. We evaluate our Model-aware parallelization Strategy with six frequently-used deep neural networks. Experimental results show that, for large networks such as VGG, the Model-aware parallelization Strategy reduces completion time by more than 20 percent.

关键词： parallel processing Computational modeling Training Data models Neural networks Graphics processing units Shape

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Blockchain-based Secure and Reliable distributed Deduplication Scheme 18th

Blockchain-Based Secure and Reliable Distributed Deduplicati...

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18th International conference on Algorithms and Architectures for parallel processing (ICA3PP)

作者： Li, Jingyi Wu, Jigang Chen, Long Li, Jiaxing Guangdong Univ Technol Guangzhou Peoples R China Guangdong Key Lab Big Data Anal & Proc Guangzhou 510006 Peoples R China

ISBN: (纸本)9783030050511;9783030050504

Due to the explosive growth of data volume on the Internet, deduplication techniques have been wildly used in cloud storage to save both disk space and network bandwidth. However, conventional deduplication schemes lead to problems with data reliability that can be attributed to the algorithm implementation where there is only one copy for each file stored in the cloud. Furthermore, the participation of trusted third party in most of the previous work has brought about the security challenge as single point of failure. In this paper, we propose a blockchain based deduplication scheme with high reliability and confidentiality in which the files are distributed to multiple servers and the information of files is recorded on the time-stamped blockchain whose central authorities are replaced to automatically decentralized smart contracts. based on the proposed scheme, we present relevant protocols to achieve secure cloud storage derived from the consensus and incentive mechanism. Security analysis demonstrates that our deduplication scheme can achieve the proposed security goals while it has limited overhead proved by simulation experiments.

关键词： Deduplication Blockchain distributed Cloud storage Security

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The Library for Hadoop Deflate Compression based on FPGA Accelerator with Load Balance

The Library for Hadoop Deflate Compression Based on FPGA Acc...

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IEEE International conference on parallel and distributed Computing, Applications and Technologies (PDCAT)

作者： Haixin Du Jiankui Zhang Shihao Sha Cai Ye Qiuming Luo College of Computer Science and Software Engineering Shenzhen University NHPCC/Guangdong Key Laboratory of popular HPC and Guangdong Province Engineering Center of China-made High Performance Data Computing System Shenzhen China

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

Hadoop application will produce lots of intermediate results in the map/reduce process that requires disk I/O and network transmission. By compressing the large-scale data of intermediate result, it will greatly improve disk access efficiently and reduce program run time. Hardware-accelerated solutions have become more desirable. This paper design a multi-FPGA compression accelerator on the Hadoop platform, and the system performance analysis compared with a software-only solution that mainly uses CPU to processing. The testing programs are zpipe, TestDFSIO and Terasort. In contrast with the software-only solution. The max speedup of zpipe is 6.55X (single FPGA) and 10.24X (dual FPGA), the max speedup of TestDFSIO is 6.28X (single FPGA) and 6.28X (dual FPGA), and the max speedup of Terasort application is up to 3.25X(single FPGA) and 3.35X(dual FPGA).

关键词： Handheld computers conferences Computer science Software engineering distributed databases Indexes

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Post-Evaluation Model of Telecommunication network Construction based on AHP

Post-Evaluation Model of Telecommunication Network Construct...

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IEEE International Symposium on parallel and distributed processing with Applications and IEEE International conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Xinzhou Cheng Xingtong Liu Qingqing Zhang Lexi Xu Xiaodong Cao Yu Qin Heng Zhang Fan Zhang Jie Gao China United Network Communications Company Network Technology Research Institute Beijing China The High School attached to Beijing Jiaotong University Beijing China China Mobile Pingdingshan Branch Henan China

ISBN: (纸本)9781728152103

The construction of telecommunication network is a complex engineering, requiring telecom operators to focus on the operational effects after construction, as well as summarize the construction experiences for further improvements. This paper innovatively proposes indicators framework of network construction and develops a post-evaluation model based on analytic hierarchy process (AHP) method. Furthermore, the proposed post-evaluation model is used to analyze the network operation effect after construction in two cities, the result verifies that the proposed post-evaluation model has promising performance and practical effect for operators.

关键词： analytic hierarchy process telecommunication networks Telecommunication networks Analytic hierarchy process networking Operators

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FD-MOBILENET: IMPROVED MOBILENET WITH A FAST DOWNSAMPLING STRATEGY 25

FD-MOBILENET: IMPROVED MOBILENET WITH A FAST DOWNSAMPLING ST...

引用

25th IEEE International conference on Image processing (ICIP)

作者： Qin, Zheng Zhang, Zhaoning Chen, Xiaotao Wang, Changjian Peng, Yuxing Natl Univ Def Technol Sci & Technol Parallel & Distributed Lab Changsha Hunan Peoples R China Natl Univ Def Technol Coll Comp Changsha Hunan Peoples R China

ISBN: (纸本)9781479970612

We present Fast-Downsampling MobileNet (FD-MobileNet), an efficient and accurate network for very limited computational budgets (e.g., 10-140 MFLOPs). Our key idea is applying a fast downsampling strategy to MobileNet framework. In FD-MobileNet, we perform 32x downsampling within 12 layers, only half the layers in the original MobileNet. This design brings three advantages: (i) It remarkably reduces the computational cost. (ii) It increases the information capacity and achieves significant performance improvements. (iii) It is engineering-friendly and provides fast actual inference speed. Experiments on ILSVRC 2012 and PASCAL VOC datasets demonstrate that FD-MobileNet consistently outperforms MobileNet and achieves comparable results with ShuffleNet under different computational budgets, for instance, surpassing MobileNet by 5.5% on the ILSVRC 2012 top-1 accuracy and 8.3% on the VOC 2007 mAP under a complexity of 12 MFLOPs. On an ARM-based device, FD-MobileNet achieves 1.11x inference speedup over MobileNet and 1.82x over ShuffleNet under the same complexity.

关键词： Computer vision convolutional neural network deep learning

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Convolutional Neural network-based Approach for Citrus Diseases Recognition

Convolutional Neural Network-Based Approach for Citrus Disea...

引用

IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Caixia Dong Zheling Xu Luanyuan Dai Weinan Liu Quan Chen Yizhang Liu Changcai Yang Tengyue Zou College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou China International Business Department Fuzhou Melbourne Polytechnic Fuzhou China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

For the development of agricultural modernization, an automatic recognition network for crop diseases with the advantages of high efficiency, non-destructiveness and continuity is indispensable. In this paper, we construct a 13-layers convolutional neural network for common citrus diseases recognition, which improving the efficiency and reducing overfitting by using a stack of small kernel and dropout, etc. Finally, we conduct comparative experiments with other networks. The experiment results show that the performances of our network are better than CNN and AlexNet. It indicates that the network we constructed is an effective citrus diseases recognition method, which can provide technical support for the identification and prevention of citrus diseases.

关键词： Diseases Agriculture Kernel Feature extraction Forestry Convolutional neural networks

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Big Data Analytics based on PANFIS MapReduce 3

Big Data Analytics based on PANFIS MapReduce

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3rd INNS conference on Big Data and Deep Learning (INNS BDDL)

作者： Za'in, Choiru Pratama, Mahardhika Lughofer, Edwin Ferdaus, Meftahul Cai, Qing Prasad, Mukesh La Trobe Univ Melbourne Vic Australia Nanyang Technol Univ Singapore Singapore Johannes Kepler Univ Linz Linz Australia Univ New South Wales Canberra ACT Australia Univ Technol Sydney Sydney NSW Australia Univ Technol Sydney FEIT Sch Software Ctr Artificial Intelligence Sydney NSW Australia

In this paper, a big data analytic framework is introduced for processing high-frequency data stream. This framework architecture is developed by combining an advanced evolving learning algorithm namely Parsimonious network Fuzzy Inference System (PANFIS) with MapReduce parallel computation, where PANFIS has the capability of processing data stream in large volume. Big datasets are learnt chunk by chunk by processors in MapReduce environment and the results are fused by rule merging method, that reduces the complexity of the rules. The performance measurement has been conducted, and the results are showing that the MapReduce framework along with PANFIS evolving system helps to reduce the processing time around 22 percent in average in comparison with the PANFIS algorithm without reducing performance in accuracy. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： Big data stream analytic distributed evolving algorithm Scalable real-time data mining parallel learning Rule merging strategy

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Blockchain Dividing based on Node Community Clustering in Intelligent Manufacturing CPS

Blockchain Dividing Based on Node Community Clustering in In...

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IEEE International conference on Blockchain (Blockchain)

作者： Suisheng Li Hong Xiao Hao Wang Tao Wang Jingwei Qiao Shaofeng Liu College of Computer Guangdong University of Technology Guangzhou China Department of Computer Science Norwegian Univ. of Sci.&Tech. Norway College of Automation Guangdong University of Technology Guangzhou China

The blockchain technology becomes a key facilitator for Intelligent Manufacturing as it enables intelligent nodes to participate in global manufacturing networks with secure ledgers and smart contracts features. However, Traditional centralized storage cannot meet performance and security requirements and fully distributed storage consumes a large amount of computing, storage and network resources, which is inefficient and difficult to implement. In this paper, we propose a clustering strategy on node community clustering by constructing a trust model based on the decentralization of blockchain technology. We introduce a multi-chain storage structure. Our experiments show that the proposed strategy reduces data synchronization time and storage space, improves system performance by enabling efficient parallel processing.

关键词： Blockchain Manufacturing Memory Synchronization Production Database systems

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