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检索条件"机构=Science and Technology on Parallel and Distributed Laboratory"

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GPS: A General Framework for parallel Queries over Data Streams in Cloud

GPS: A General Framework for Parallel Queries over Data Stre...

引用

IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Xiaoyong Li Yijie Wang Yu Zhao Yuan Wang Xiaoling Li Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha Hunan P. R. China

parallel query processing over data streams in cloud computing environments has attracted considerable attention recently in various fields, due to the huge potential value of analyzing massive data or big data in a large number of streaming applications. Nevertheless, existing studies on queries primarily focus on the algorithms for the specific query types with the lack of the general framework for processing various queries. Moreover, existing parallel frameworks in cloud such as MapReduce and its variations are not suitable for many complex queries over complex data streams. In this paper, we extensively discuss the problem of designing the general framework for parallel queries over data streams in cloud. Particularly, we propose and implement a framework called GPS, which can be well adapted to various queries over complex data streams like the uncertain data streams. Furthermore, we further propose a hierarchical and general parallel model for queries over data streams based on the proposed framework, which is more flexible than the MapReduce model. The skyline queries over uncertain data streams based on our proposed framework with real deployment are conducted as an example to verify the performances of our proposals.

关键词： Peer-to-peer computing parallel processing Data models Query processing Global Positioning System Object oriented modeling distributed databases

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Research of Context Situation Awareness technology

Research of Context Situation Awareness Technology

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IEEE International Symposium on Object/Component/Service-Oriented Real-Time distributed Computing Workshops (ISORCW)

作者： Dianxi Shi Zhendong Wu Bo Ding Huining Yan National Key Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha China

Context situation, which means a snapshot of the status of the real world, is formed by integrating a large amount of contexts collected from various resources. How to get the context situation and use the situation to provide better services is a challenging issue. In this paper, we focused on this challenge on the basis of the mobile cloud computing architecture. An abstract model is proposed in this paper to uniformly collect the context and send them to cloud. A rule-based large-scale context aggregation algorithm is also proposed which utilizes the MapReduce computing paradigm. Finally, a large-scale context management framework based on the abstract model and the context aggregation algorithm is proposed, and a real-time traffic demo is implemented to verify the validity of the framework.

关键词： Context Mobile handsets Real time systems Sensors Servers Mobile communication Context modeling

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Notice of Retraction: parallel block multigrid preconditioner for 3D Navier-Stokes equations on unstructured grids

Notice of Retraction: Parallel block multigrid preconditione...

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International Conference on Computer Application and System Modeling (ICCASM)

作者： Zongzhe Li Lu Yao Wei Cao Yongxian Wang Zhenghua Wang School of Computer Science Parallel and Distributed Processing for National Laboratory National University of Defense Technology Changsha Hunan China

This article has been retracted by the publisher.

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Feature and Performance Comparison of FaaS Platforms 14

Feature and Performance Comparison of FaaS Platforms

引用

14th IEEE International Conference on Software Engineering and Service science, ICSESS 2023

作者： Ma, Penghui Shi, Peichang Yi, Guodong College of Computer Science National University of Defense Technology National Key Laboratory of Parallel and Distributed Processing Changsha410073 China College of Computer Science National University of Defense Technology Key Laboratory of Software Engineering for Complex Systems Changsha410073 China Xiangjiang Lab Changsha410073 China School of Advanced Interdisciplinary Studies Hunan University of Technology and Business Changsha410073 China

ISBN: (纸本)9798350336269

With serverless computing offering more efficient and cost-effective application deployment, the diversity of serverless platforms presents challenges to users, including platform lock-in and costly migration. Moreover, due to the black box nature of function computing, traditional performance benchmarking methods are not applicable, necessitating new studies. This article presents a detailed comparison of six major public cloud function computing platforms and introduces a benchmarking framework for function computing performance. This framework aims to help users make comprehensive comparisons and select the most suitable platform for their specific needs. © 2023 IEEE.

关键词： Benchmarking

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DCIGAN: A distributed Class-Incremental Learning Method Based on Generative Adversarial Networks

DCIGAN: A Distributed Class-Incremental Learning Method Base...

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

作者： Hongtao Guan Yijie Wang Xingkong Ma Yongmou Li Science and Technology on Parallel and Distributed Laboratory College of Computer National University of Defense Technology Changsha Hunan P.R.China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Class incremental learning has received wide attention due to its better adaptability to the changing characteristics of online learning. However, data cannot be shared between organizations in the data isolated islands scenario. Existing solutions cannot adapt to incremental classes without aggregating data. In this paper, we propose a distributed class-incremental learning framework, called DCIGAN. It uses GAN generators to store the information of past data and continuously update GAN parameters with new data. In particular, we proposed CIGAN to ensure that the distribution of generated pseudo data is as close as possible to the real data on a single node, which guarantees the accuracy of class-incremental learning. Furthermore, we propose GF, a generators fusion method to integrate local generators of multi-nodes into a new global generator. To evaluate the performance of DCIGAN, we conduct experiments on six datasets under various parameter settings on both two and multi nodes distributed scenarios. Extensive experiments confirm that DCIGAN outperform the general baselines and achieve classification accuracy closes to the method of data aggregating.

关键词： Generators Generative adversarial networks distributed databases Diseases Servers Hospitals Training

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Non-negative low-rank and group-sparse matrix factorization 21

Non-negative low-rank and group-sparse matrix factorization

引用

21st International Conference on MultiMedia Modeling, MMM 2015

作者： Wu, Shuyi Zhang, Xiang Guan, Naiyang Tao, Dacheng Huang, Xuhui Luo, Zhigang Science and Technology on Parallel Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha Hunan410073 China Department of Computer Science and Technology College of Computer National University of Defense Technology Changsha Hunan410073 China Centre for Quantum Computation & Intelligent Systems and the Faculty of Engineering and Information Technology University of Technology Sydney 235 Jones Street UltimoNSW2007 Australia

ISBN: (纸本)9783319144412

Non-negative matrix factorization (NMF) has been a popular data analysis tool and has been widely applied in computer vision. However, conventional NMF methods cannot adaptively learn grouping structure froma *** paper proposes a non-negative low-rank and group-sparse matrix factorization (NLRGS) method to overcome this deficiency. Particularly, NLRGS captures the relationships among examples by constraining rank of the coefficients meanwhile identifies the grouping structure via group sparsity regularization. By both constraints, NLRGS boosts NMF in both classification and clustering. However, NLRGS is difficult to be optimized because it needs to deal with the low-rank constraint. To relax such hard constraint, we approximate the low-rank constraint with the nuclear norm and then develop an optimization algorithm for NLRGS in the frame of augmented Lagrangian method(ALM). Experimental results of both face recognition and clustering on four popular face datasets demonstrate the effectiveness of NLRGS in quantities. © Springer International Publishing Switzerland 2015.

关键词： Non-negative matrix factorization

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MIX: A Joint Learning Framework for Detecting Both Clustered and Scattered Outliers in Mixed-Type Data

MIX: A Joint Learning Framework for Detecting Both Clustered...

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IEEE International Conference on Data Mining (ICDM)

作者： Hongzuo Xu Yijie Wang Yongjun Wang Zhiyue Wu National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

Mixed-type data are pervasive in real life, but very limited outlier detection methods are available for these data. Some existing methods handle mixed-type data by feature converting, whereas their performance is downgraded by information loss and noise caused by the transformation. Another kind of approaches separately evaluates outlierness in numerical and categorical features. However, they fail to adequately consider the behaviours of data objects in different feature spaces, often leading to suboptimal results. As for outlier form, both clustered outliers and scattered outliers are contained in many real-world data, but a number of outlier detectors are inherently restricted by their outlier definitions to simultaneously detect both of them. To address these issues, an unsupervised outlier detection method MIX is proposed. MIX constructs a joint learning framework to establish a cooperation mechanism to make separate outlier scoring constantly communicate and sufficiently grasp the behaviours of data objects in another feature space. Specifically, MIX iteratively performs outlier scoring in numerical and categorical space. Each outlier scoring phase can be iteratively and cooperatively enhanced by the prior knowledge given by another feature space. To target both clustered and scattered outliers, the outlier scoring phases capture the essential characteristic of outliers, i.e., evaluating outlierness via the deviation from the normal model. We show that MIX significantly outperforms eight state-of-the-art outlier detectors on twelve real-world datasets and obtains good scalability.

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High-performance Audio Matching with Features Learned by Convolutional Deep Belief Network

High-performance Audio Matching with Features Learned by Con...

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2016 IEEE 13th International Conference on Signal Processing（ICSP2016）

作者： Weijiang Feng Naiyang Guan Zhigang Luo Institute of Software College of Computer National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology

Audio matching automatically retrieves all excerpts that have the same content as the query audio clip from given audio recordings. The extracted feature is critical for audio matching and the Chroma Energy Normalized Statistics(CENS) feature is the state-of-the-arts. However, CENS might behave unsatisfactorily on some audio because it is a handcraft feature. In this paper, we propose to utilize the features learned by Convolutional Deep Belief Network(CDBN) to enhance the performance of audio matching. Benefit from the strong generalization ability of CDBN, our method works better than CENS based methods on most audio datasets. Since the features learned by CDBN are binary-valued, we can develop a more efficient audio matching algorithm by taking the advantage of this property. Experimental results on both TIMIT dataset and a simulated music dataset confirm effectiveness of the proposed CDBN based method comparing with the traditional CENS feature based algorithm.

关键词： audio matching convolutional deep belief network content-based audio retrieval

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App-Net: A Hybrid Neural Network for Encrypted Mobile Traffic Classification

App-Net: A Hybrid Neural Network for Encrypted Mobile Traffi...

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IEEE Conference on Computer Communications Workshops, INFOCOM Wksps

作者： Xin Wang Shuhui Chen Jinshu Su College of Computer Science and Technology National University of Defense Technology National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology

ISBN: (数字)9781728186955

ISBN: (纸本)9781728186962

With the exponential growth of mobile traffic data, mobile traffic classification is in a great need. It is an essential step to improve the performance of network services such as QoS and security monitoring. However, the widespread use of encrypted protocols, especially the TLS protocol, has posed great challenges to traditional traffic classification techniques. As the rule-based deep packet inspection approaches are ineffective for encrypted traffic classification, various machine learning methods have been studied and used. Recently, deep learning solutions which enable automatic feature extraction are also proposed to classify encrypted traffic. In this paper we propose App-Net, an end-to-end hybrid neural network, to learn effective features from raw TLS flows for mobile app identification. App-Net is designed by combining RNN and CNN in a parallel way. So that it can learn a joint flow-app embedding to characterize both flow sequence patterns and unique app signatures. We evaluate App-Net on a real-world dataset that covering 80 apps. The results show that our method can achieve an excellent performance and outperform the state-of-the-art methods.

关键词： Cryptography Feature extraction Payloads Recurrent neural networks Machine learning Convolutional neural networks

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Flexible Virtual Channel Power-Gating for High-Throughput and Low-Power Network-on-Chip

Flexible Virtual Channel Power-Gating for High-Throughput an...

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Euromicro Symposium on Digital System Design

作者： Feng Wang Xiantuo Tang Qinglin Wang Zuocheng Xing Hengzhu Liu Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha Hunan P. R. China

Power-gating is a representative circuit level technique to mitigate leakage power. While in low-power Network-on-Chip (NoC) design, the former fine-grained power-gating methods will decrease network performance due to serial wake-up latency and head-of-line blocking. Therefore, we propose a flexible Virtual Channel (VC) management scheme for fine-grained power-gating to achieve high throughput and low-power. The proposed power-gating method with the early wake-up is evaluated by using some synthetic workloads. When compared with an optimized early wake-up power-gating technique, it can improve performance effectively in medium and high network loads, and increases the network throughput by 15.7%~44.1% for different synthetic loads, while keeps network power consumption as low as the optimized method. For the PARSEC application traces of token based protocol, it can significantly decrease packet latency by 20.3% on average, however only increases less than 3.6% peak power when compared with the optimized method.

关键词： Throughput Radiation detectors Routing Process control Pipelines Power demand Ports (Computers)

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