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International Conference on Cloud computing and Big Data (CCBD)

作者： Weiping Zhu Hongliang Lu Xiaohui Cui International School of Software Wuhan University China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology China Department of Computing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9781479966226

Internet of things is an emerging technology that aims to connect various smart objects in our daily life. It facilitates the information exchange and control among objects. In the Internet of things, it is important to discover various relations among objects for analyzing and mining useful knowledge. Existing works on relation discovery mainly focus on centralized processing. It is not suitable for Internet of things due to unavailable of server, one-point failure, computation bottleneck, and security and business concerns. In this paper, we propose a distributed approach to discover the relations among objects in Internet of things. We first build the distritbuted system model which may include multiple relation discovery tasks. Based on that, we design an approach utilizing distributed spanning tree to extract the relations. Rather than sending all the information to the server, the objects only need to send the information to a local leader object in our algorithm. We also discuss more about how to improve the performance of the proposed approach and how to relax the system constraints. Extensive simulation have been done and the results show that the proposed approach outperforms existing approaches in terms of the data amount of transmission.

关键词： Internet of things Synchronization Buildings Servers Algorithm design and analysis Delays

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TRANSDUCTIVE NONNEGATIVE MATRIX FACTORIZATION FOR SEMI-SUPERVISED HIGH-PERFORMANCE SPEECH SEPARATION

TRANSDUCTIVE NONNEGATIVE MATRIX FACTORIZATION FOR SEMI-SUPER...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Naiyang Guan Long Lan Dacheng Tao Zhigang Luo Xuejun Yang Science and Technology on Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Changsha Hunan 410073 China Centre for Quantum Computation and Intelligent Systems FEIT University of Technology Sydney Sydney NSW 2007 Australia State Key Laboratory of High Performance Computing National University of Defense Technology Changsha Hunan 410073 China

ISBN: (纸本)9781479928941

Regarding the non-negativity property of the magnitude spectrogram of speech signals, nonnegative matrix factorization (NMF) has obtained promising performance for speech separation by independently learning a dictionary on the speech signals of each known speaker. However, traditional NM-F fails to represent the mixture signals accurately because the dictionaries for speakers are learned in the absence of mixture signals. In this paper, we propose a new transductive NMF algorithm (TNMF) to jointly learn a dictionary on both speech signals of each speaker and the mixture signals to be separated. Since TNMF learns a more descriptive dictionary by encoding the mixture signals than that learned by NMF, it significantly boosts the separation performance. Experiments results on a popular TIMIT dataset show that the proposed TNMF-based methods outperform traditional NMF-based methods for separating the monophonic mixtures of speech signals of known speakers.

关键词： Speech Dictionaries as Topic Loudspeakers NMF protocol signals mixtures factorization HIGH-PERFORMANCE

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NR-MPI: A Non-stop and Fault Resilient MPI

NR-MPI: A Non-stop and Fault Resilient MPI

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International Conference on parallel and distributed Systems (ICPADS)

作者： Guang Suo Yutong Lu Xiangke Liao Min Xie Hongjia Cao 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, in particular in the perspective of future E-scale systems, which will consist of millions of CPU cores and other components. Fault tolerant MPI was proposed to offer support of software level fault tolerance approaches. However, the widely used MPI implementations, such as MPICH and Mvapich2, provide limited support for fault tolerance. This paper proposes NR-MPI, a Non-stop and Fault Resilient MPI. NR-MPI implements the semantics of FT-MPI based on MPICH. Specifically, this paper focuses on failure detection in MPI library, online failure recovery of communicators for multiple failures, friendly programming interface extending for NR-MPI. Furthermore, to support failure recovery of applications, NR-MPI implements data backup and restore interfaces based on double in-memory checkpoint/restart. We conduct experiments with NPB benchmarks on TH-1A supercomputer. 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.

关键词： Fault tolerance Fault tolerant systems Libraries Resource management Context Programming Semantics

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Speculative symbolic execution

Speculative symbolic execution

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2012 IEEE 23rd International Symposium on Software Reliability Engineering, ISSRE 2012

作者： Zhang, Yufeng Chen, Zhenbang Wang, Ji National Laboratory for Parallel and Distributed Processing Department of Computing Science National University of Defense Technology Changsha China

ISBN: (纸本)9780769548883

Symbolic execution is an effective path oriented and constraint based program analysis technique. Recently, there is a significant development in the research and application of symbolic execution. However, symbolic execution still suffers from the scalability problem in practice, especially when applied to large-scale or very complex programs. In this paper, we propose a new fashion of symbolic execution, named Speculative Symbolic Execution (SSE), to speed up symbolic execution by reducing the invocation times of constraint solver. In SSE, when encountering a branch statement, the search procedure may speculatively explore the branch without regard to the feasibility. Constraint solver is invoked only when the speculated branches are accumulated to a specified number. In addition, we present a key optimization technique that enhances SSE greatly. We have implemented SSE and the optimization technique on Symbolic Pathfinder (SPF). Experimental results on six programs show that, our method can reduce the invocation times of constraint solver by 20.7% to 48.7% (with an average of 29.9%), and save the search time from 23.6% to 43.6% (with an average of 30%). © 2012 IEEE.

关键词： Java programming language

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The demand for consistent web-based workflow editors 8

The demand for consistent web-based workflow editors

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8th Workshop on Workflows in Support of Large-Scale Science, WORKS 2013

作者： Gesing, Sandra Atkinson, Malcolm Klampanos, Iraklis Galea, Michelle Berthold, Michael R. Barbera, Roberto Scardaci, Diego Terstyanszky, Gabor Kiss, Tamas Kacsuk, Peter University of Notre Dame Ctr for Research Computing University of Edinburgh Informatics Forum 111 Information Technology Center Notre DameIN46556 United States University of Edinburgh Informatics Forum EdinburghEH8 9AB United Kingdom Universität Konstanz FB Informatik and Informationswissenschaft Box 712 Konstanz78457 Germany Italian National Institute of Nuclear Physics Catania95123 Italy University of Westminster 115 New Cavendish Street LondonW1W 6UW United Kingdom Laboratory of Parallel and Distributed Systems MTA SZTAKI Kende Street 13-17 Budapest1111 Hungary

ISBN: (纸本)9781450325028

This paper identifies the high value to researchers in many disciplines of having web-based graphical editors for scientific workflows and draws attention to two technological transitions: good quality editors can now run in a browser and workflow enactment systems are emerging that manage multiple workflow languages and support multi-lingual workflows. We contend that this provides a unique opportunity to introduce multi-lingual graphical workflow editors which in turn would yield substantial benefits: workflow users would find it easier to share and combine methods encoded in multiple workflow languages, the common framework would stimulate conceptual convergence and increased workflow component sharing, and the many workflow communities could share a substantial part of the effort of delivering good quality graphical workflow editors in browsers. The paper examines whether such a common framework is feasible and presents an initial design for a web-based editor, tested with a preliminary prototype. It is not a fait accompli but rather an urgent rallying cry to explore collaboratively a generic web-based framework before investing in many divergent individual implementations.

关键词： Interoperability

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Detecting faults by tracing companion states in cloud computing systems

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Jisuanji Xuebao/Chinese Journal of Computers 2012年第5期35卷 856-870页

作者： Rao, Xiang Wang, Huai-Min Chen, Zhen-Bang Zhou, Yang-Fan Cai, Hua Zhou, Qi Sun, Ting-Tao National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China Shenzhen Research Institute The Chinese University of Hong Kong Shenzhen China Department of Computing Platform Alibaba Cloud Computing Company Hangzhou 310011 China

A common way to construct a fault model is injecting the fault into the system and observing the subsequent symptoms, e. g. event logs. However, fault features would vary during the propagation period, and present different symptoms at different stage of the fault propagation process. The exiting detection window based feature extraction methods can only identify the early symptoms of a fault, but fail to detect the latter symptoms and cause false alarms. To solve the problem, we present a fault feature extraction method, called Companion State Tracer (CSTracer), which consists of 3 integrated steps: (1) pre-process logs to remove the unrelated logs;(2) construct a general identifier for the early symptoms of a fault;(3) construct a finite state machine model for the fault to trace the latter symptoms. CSTracer can persistently monitor a fault after the fault has been identified. We have justified the effectiveness of CSTracer in an enterprise cloud system. Compared with the existing, the results show that CSTracer has a better detection accuracy.

关键词： Fault detection

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Performance problems online detection in cloud computing systems via analyzing request execution paths

Performance problems online detection in cloud computing sys...

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Workshop on Proactive Failure Avoidance, Recovery, and Maintenance

作者： Mi, Haibo Wang, Huaimin Yin, Gang Cai, Hua Zhou, Qi Sun, Tingtao National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Computing Platform Alibaba Cloud Computing Company Hangzhou China

ISBN: (纸本)9781457703751

It is quite a headache for developers to online detect performance problems in large-scale cloud computing systems. The behavior and the hidden connections among the huge amount of runtime request execution paths in cloud computing systems usually contain useful information for performance problem detection. In this paper, we propose an approach to rapidly diagnose the source of performance degradation in large-scale non-stop cloud computing systems. The approach first groups the user requests into categories with a fast clustering algorithm;then applies the principal components analysis to extract the primary methods;finally compares the normal and abnormal behaviors of the primary methods to localize the main cause of performance problems. We conduct extensive experiments over a real-world enterprise system providing services for the public. The results show that our approach can locate the prime causes of performance problems accurately and efficiently.1 © 2011 IEEE.

关键词： Cloud computing

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Identifying faults in large-scale distributed systems by filtering noisy error logs

Identifying faults in large-scale distributed systems by fil...

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Workshop on Proactive Failure Avoidance, Recovery, and Maintenance

作者： Rao, Xiang Wang, Huaimin Shi, Dianxi Chen, Zhenbang Cai, Hua Zhou, Qi Sun, Tingtao National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China Computing Platform Alibaba Cloud Computing Corporation Hangzhou China

ISBN: (纸本)9781457703751

Extracting fault features with the error logs of fault injection tests has been widely studied in the area of large scale distributed systems for decades. However, the process of extracting features is severely affected by a large amount of noisy logs. While the existing work tries to solve the problem by compressing logs in temporal and spatial views or removing the semantic redundancy between logs, they fail to consider the co-existence of other noisy faults that generate error logs instead of injected faults, for example, random hardware faults, unexpected bugs of softwares, system configuration faults or the error rank of a log severity. During a fault feature extraction process, those noisy faults generate error logs that are not related to a target fault, and will strongly mislead the resulted fault features. We call an error log that is not related to a target fault a noisy error log. To filter out noisy error logs, we present a similarity-based error log filtering method SBF, which consists of three integrated steps: (1) model error logs into time series and use haar wavelet transform to get the approximate time series;(2) divide the approximate time series into sub time series by valleys;(3) identify noisy error logs by comparing the similarity between the sub time series of target error logs and the template of noisy error logs. We apply our log filtering method in an enterprise cloud system and show its effectiveness. Compared with the existing work, we successfully filter out noisy error logs and increase the precision and the recall rate of fault feature extraction.1 © 2011 IEEE.

关键词： Errors

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Using complex network features for fast clustering in the web 11

Using complex network features for fast clustering in the we...

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Proceedings of the 20th international conference companion on World wide web

作者： Tang, Jintao Wang, Ting Wang, Ji Lu, Qin Li, Wenjie School of Computer National University of Defense Technology Changsha China Department of Computing Hong Kong Polytechnic University Hong Kong Hong Kong National Laboratory for Parallel and Distributed Processing Changsha China

ISBN: (纸本)9781450305181

Applying graph clustering algorithms in real world networks needs to overcome two main challenges: the lack of prior knowledge and the scalability issue. This paper proposes a novel method based on the topological features of complex networks to optimize the clustering algorithms in real-world networks. More specifically, the features are used for parameter estimation and performance optimization. The proposed method is evaluated on real-world networks extracted from the web. Experimental results show improvement both in terms of Adjusted Rand index values as well as runtime efficiency. © 2011 Authors.

关键词： Parameter estimation

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A wikipedia based semantic graph model for topic tracking in blogosphere

A wikipedia based semantic graph model for topic tracking in...

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22nd International Joint Conference on Artificial Intelligence, IJCAI 2011

作者： Tang, Jintao Wang, Ting Lu, Qin Wang, Ji Li, Wenjie College of Computer National University of Defense Technology Changsha China Department of Computing Hong Kong Polytechnic University Hong Kong Hong Kong National Laboratory for Parallel and Distributed Processing Changsha China

ISBN: (纸本)9781577355120

There are two key issues for information diffusion in blogosphere: (1) blog posts are usually short, noisy and contain multiple themes, (2) information diffusion through blogosphere is primarily driven by the "word-of- mouth" effect, thus making topics evolve very fast. This paper presents a novel topic tracking approach to deal with these issues by modeling a topic as a semantic graph, in which the semantic relatedness between terms are learned from Wikipedia. For a given topic/post, the name entities, Wikipedia concepts, and the semantic relatedness are extracted to generate the graph model. Noises are filtered out through the graph clustering algorithm. To handle topic evolution, the topic model is enriched by using Wikipedia as background knowledge. Furthermore, graph edit distance is used to measure the similarity between a topic and its posts. The proposed method is tested by using the real-world blog data. Experimental results show the advantage of the proposed method on tracking the topic in short, noisy texts.

关键词： Blogs

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