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International Conference on Software Engineering (ICSE)

作者： Jingwen Zhou Zhenbang Chen Ji Wang College of Computer National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781479919352

Currently, the performance problems of software systems gets more and more attentions. Among various diagnosis methods based on system traces, principal component analysis (PCA) based methods are widely used due to the high accuracy of the diagnosis results and requiring no specific domain knowledge. However, according to our experiments, we have validated several shortcomings existed in PCA-based methods, including requiring traces with a same call sequence, inefficiency when the traces are long, and missing performance problems. To cope with these issues, we introduce a segmentation based online diagnosis method in this poster.

关键词： Principal component analysis Software systems Accuracy Measurement Monitoring Computer architecture Conferences

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IDOpenFlow: An OpenFlow switch to support identifier-locator split communication

IDOpenFlow: An OpenFlow switch to support identifier-locator...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Yannan Yang Yaping Liu Zhihong Liu School of Computer National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781467386456

The traditional identifier locator split network has many issues such as inflexibility, hard to innovate and difficult to deploy. SDN (Software Defined Network) provides a new direction for designing flexible identifier locator split network. The recent identifier locator split network based on SDN use the OpenFlow switch directly via rewritting the address, which lacks the scalability and utilizes locator address ineffectively. An OpenFlow switch named IDOpenFlow is proposed to support the communication based on identifier. IDOpenFlow switch provides the communication mechanism via encapsulating the packets, which has good scalability and utilizing locator address effectively. IDOpenFlow switch encapsulates and decapsulates packets according flow entries which are installed by SDN controller. Moreover, the prototype system shows that IDOpenFlow effectively supports the communication for both the fixed node and the mobile node. With respect to the issues of software forwarding performance, a high-performance IDOpenFlow switch based on Intel DPDK (which is named A-IDOpenFlow) is proposed. The results of Ixia test tool show that: 1) for packets more than 128 bytes, A-IDOpenFlow switch supports the communication based on identifier at rate of 10Gbit/s; 2) for small packet of 64 bytes, the rate of A-IDOpenFlow is 7.25 times faster than the rate of IDOpenFlow.

关键词： Switches Protocols Software Routing Servers Scalability

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ME-TCAM: Memory-Efficient Ternary Content Addressable Memory Based on Multipumping-Enabled LUTRAM on FPGA

ME-TCAM: Memory-Efficient Ternary Content Addressable Memory...

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Intelligent Computing and Human-Computer Interaction (ICHCI), International Conference on

作者： Zilin Shi Hui Yang Rulin Liu Jinli Yan Peng Qiao Baosheng Wang College of Computer Science and Technology National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) National University of Defense Technology Changsha China

Static random access memory (SRAM) on field programmable gate arrays (FPGAs) can be emulated to offer ternary content addressable memory (TCAM) functionality. However, SRAM-based TCAM wastes storage resources. This is due to the limited capacity of the physical addresses in the SRAM unit. This work proposes a LUTRAM-based TACM scheme on the FPGA called Memory-Efficient TCAM (ME-TCAM). METCAM divides SRAM unit into multiple virtual blocks mapping to a portion of the TCAM table to store the more address information of the TCAM table. Operation on SRAM block means that increasing the overall emulated TCAM bits/SRAM. Moreover, ME-TCAM exploits Xilinx primitives to conFigure lookup tables (LUTs) as 32 × 2 lookup table RAMs (LUTRAMs). We implement ME-TCAM using LUTRAM with a size of 512 × 48 and 1024 × 144 on a Virtex-7 FPGA device. Compared with the state-of-the-art research DUR, ME-TCAM achieves at least 2.6 times more memory efficiency.

关键词： Human computer interaction Associative memory Memory management Random access memory Logic gates Table lookup parallel architectures

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Semantic web service composition using answer set planning

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International Journal of Advancements in Computing technology 2011年第5期3卷 20-31页

作者： Qian, Jun-Yan Huang, Guo-Wang Zhao, Ling-Zhong National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China Department of Computer science and engineering Guilin University of Electronic Technology Guilin 541004 China

This paper presents a method that adapting planning description to bring the semantic information into play for service composition through action language C. It shows how service descriptions can be expressed by preconditions and effects and the action language C provides a richer syntax and semantic for complex service descriptions. We also presents the algorithm of Translating semantic Web service described by OWL-S to action language C. Thanks to the structured description and the powerful expression of C, we only consider the initial Situation and the desired goal ignoring details of transition and planning. At last we use satisfiability planning to solve the planning problem by translating the action language into disjunctive logic program.

关键词： Web services

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A push-based prefetching for cooperative caching RAM Grid

A push-based prefetching for cooperative caching RAM Grid

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International Conference on parallel and distributed Systems (ICPADS)

作者： Rui Chu Nong Xiao Lei Chen Xicheng Lu National Laboratory for Parallel and Distributed Processing ChangSha Hunan China Hong Kong University of Science and Technology Kowloon Hong Kong China

As an innovative distributed computing technique for sharing the memory resources in high-speed network, RAM Grid exploits the distributed free nodes, and provides remote memory for the nodes which are short of memory. One of the RAM Grid systems named DRACO, tries to provide cooperative caching to improve the performance of the user node which has mass disk I/O but lacks local memory. However, the performance of DRACO is constrained with the network communication cost. In order to hide the latency of remote memory access and improve the caching performance, we proposed using push- based prefetching to enable the caching providers to push the potential useful memory pages to the user nodes. Specifically, for each caching provider, it employs sequential pattern mining techniques, which adapts to the characteristics of memory page access sequences, on locating useful memory pages for prefetching. We have verified the effectiveness of the proposed method through system analysis and trace-driven simulations.

关键词： Prefetching Cooperative caching Random access memory Read-write memory Delay High-speed networks File systems Costs Laboratories distributed processing

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Automatic Detecting Performance Bugs in Cloud Computing Systems via Learning Latency Specification Model

Automatic Detecting Performance Bugs in Cloud Computing Syst...

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2014 IEEE 8th International Symposium on Service Oriented System Engineering

作者： Haibo Mi Huaimin Wang Zhenbang Chen Yangfan Zhou Science and Technology on Parallel & Distributed Processing Laboratory National Univ. of Defense Technology Shenzhen Research Institute The Chinese Univ. of Hong Kong

Performance bugs that don't cause fail-stop errors but degradation of system performance have been one of the most fundamental issues in the production platform. How to effectively online detect bugs becomes more and more urgent for engineers. Performance bugs usually manifest themselves as the anomalous call structures of request traces or anomalous latencies of invoked methods. In this paper, we propose an automatic performance bug online detecting approach, CloudDoc. CloudDoc maintains a performance model mined from execution traces that are collected in the normal period. The performance model captures the characteristics of call structures of request traces together with corresponding latencies. With the performance model, CloudDoc periodically detects whether performance bugs occur or not. All suspicious call structures or latency-abnormal invoked methods are presented to engineers. We report two case studies to demonstrate the effectiveness of CloudDoc in helping engineers identify performance bugs.

关键词： Computer bugs Computational modeling Cloud computing Electronic mail Merging System performance Servers

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ST-PINN: A Self-Training Physics-Informed Neural Network for Partial Differential Equations

ST-PINN: A Self-Training Physics-Informed Neural Network for...

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International Joint Conference on Neural Networks (IJCNN)

作者： Junjun Yan Xinhai Chen Zhichao Wang Enqiang Zhoui Jie Liu Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha China

Partial differential equations (PDEs) are an essential computational kernel in physics and engineering. With the advance of deep learning, physics-informed neural networks (PINNs), as a mesh-free method, have shown great potential for fast PDE solving in various applications. To address the issue of low accuracy and convergence problems of existing PINNs, we propose a self-training physics-informed neural network, ST-PINN. Specifically, ST-PINN introduces a pseudo label based self-learning algorithm during training. It employs governing equation as the pseudo-labeled evaluation index and selects the highest confidence examples from the sample points to attach the pseudo labels. To our best knowledge, we are the first to incorporate a self-training mechanism into physics-informed learning. We conduct experiments on five PDE problems in different fields and scenarios. The results demonstrate that the proposed method allows the network to learn more physical information and benefit convergence. The ST-PINN outperforms existing physics-informed neural network methods and improves the accuracy by a factor of 1.33x-2.54x.

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Classification of Tiangong-1 hyperspectral remote sensing image via contextual sparse coding

Classification of Tiangong-1 hyperspectral remote sensing im...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Qi Lv Yong Dou Xin Niu Jiaqing Xu Jinbo Xu School of Computer National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781467372220

The hyperspectral remote sensing is one of the frontier techniques in the remote sensing research fields. Applying the sparse coding model to the hyperspectral remote sensing image processing is a hot topic in hyperspectral information processing. To improve the accuracy of hyperspectral image classification, we propose a classification method based on the spatial-spectral join-t contextual sparse coding. Firstly, a dictionary is obtained by training using samples selected from the ground-truth reference data. Then, the sparse coefficients of each pixel are calculated based on the learned dictionary. Afterward, the sparse coefficients are input to the classifier and the final classification result is obtained. The visible and near-infrared hyperspectral remote sensing image collected by Tiangong-1 in Chaoyang District of Beijing is used to evaluate the performance of the proposed approach. Experimental results show that the proposed method yields the best classification performance with the overall accuracy of 95.74% and the Kappa coefficient of 0.9476 in comparison with other classification methods.

关键词： Hyperspectral image Remote sensing Sparse coding Sparse coding Classification Methods remote sensing hyperspectral remote sensing hyperspectral imagery Image classification

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NR-MPI: A non-stop and fault resilient MPI supporting programmer defined data backup and restore for E-scale super computing systems

Supercomputing Frontiers and Innovations

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Supercomputing Frontiers and Innovations 2016年第1期3卷 4-21页

作者： Suo, Guang Lu, Yutong Liao, Xiangke Xie, Min Cao, Hongjia State Key Laboratory of High Performance Computing National University of Defense Technology Changsha Hunan Province China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha Hunan Province China

Fault resilience has became a major issue for HPC systems, particularly, in the perspective of future E-scale systems, which will consist of millions of CPU cores and other components. MPI-level fault tolerant constructs, such as ULFM, are being proposed to support software level fault tolerance. However, there are few systematic evaluations by application programmers using benchmarks or pseudo applications. This paper proposes NR-MPI, a Non-stop and Fault Resilient MPI, supporting programmer defined data backup and restore. To help programmers write fault tolerant programs, NR-MPI provides a set of friendly programming interfaces and a state transition diagram for data backup and restore. This paper focuses on design, implementation and evaluation of NR-MPI. Specifically,this paper puts emphases on failure detection in MPI library, friendly programming interface extending for NR-MPI and examples of fault tolerant programs based NRMPI. Furthermore, to support failure recovery of applications, NR-MPI implements data backup interfaces based on double in-memory checkpoint/*** conduct experiments with both NPB benchmarks and Sweep3D on TH supercomputer in NSCC-TJ. Experimental results show that NR-MPI based fault tolerant programs can recover from failures online without restarting, and the overhead is small even for applications with tens of thousands of cores. © The Authors 2016.

关键词： Message passing

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A C-SVM Based Anomaly Detection Method for Multi-Dimensional Sequence over Data Stream

A C-SVM Based Anomaly Detection Method for Multi-Dimensional...

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International Conference on parallel and distributed Systems (ICPADS)

作者： Han Bao Yijie Wang Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha Hunan P. R. China

ISBN: (纸本)9781509053827

Anomaly detection over multi-dimensional data stream has attracted considerable attention recently in various fields, such as network, finance and aerospace. In many cases, anomalies are composed of a sequence of multi-dimensional data, and it's necessary to detect this type of anomalies accurately and efficiently over data stream. Existing online methods of anomaly detection merely focus on the single-dimensional sequence. What's more, current studies about multi-dimensional sequence are mainly concentrated on static database. However, the anomaly detection for multi-dimensional sequence over data stream is much more difficult, due to the complexity of multidimensional sequence processing, the dynamic nature of data stream and the unbalance between normal and abnormal data. Facing these challenges, we propose an anomaly detection method for multi-dimensional sequence over data stream based on cost sensitive support vector machine (C-SVM) called ADMS. First, to improve the accuracy and efficiency, the ADMS transforms multi-dimensional sequences into feature vectors in a lossless way and prunes worthless features of these vectors. And then, the ADMS can detect abnormal sequences over dynamically imbalanced data stream by lively testing these vectors based on C-SVM. Experiments show that the false negative rate (FNR) of the ADMS is lower than 5%, the false positive rate (FPR) is lower than 7%, and the throughput is improved 42% by pruning worthless features. In addition, the AMDS performs well when there are concept drifts over the data stream.

关键词： Feature extraction Training Transforms Heuristic algorithms Testing Hidden Markov models Databases

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