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International Conference on Advanced Information Networking and Applications

作者： Bhathiya Wickremasinghe Rodrigo N. Calheiros Rajkumar Buyya The Cloud Computing and Distributed Systems (CLOUDS) Laboratory Department of Computer Science and Software Engineering The University of Melbourne Pontifical Catholic University of Rio Grande do Sul

ISBN: (纸本)9781424466955;9780769540184

Advances in Cloud computing opens up many new possibilities for Internet applications developers. Previously, a main concern of Internet applications developers was deployment and hosting of applications, because it required acquisition of a server with a fixed capacity able to handle the expected application peak demand and the installation and maintenance of the whole software infrastructure of the platform supporting the application. Furthermore, server was underutilized because peak traffic happens only at specific times. With the advent of the Cloud, deployment and hosting became cheaper and easier with the use of pay-peruse flexible elastic infrastructure services offered by Cloud providers. Because several Cloud providers are available, each one offering different pricing models and located in different geographic regions, a new concern of application developers is selecting providers and data center locations for applications. However, there is a lack of tools that enable developers to evaluate requirements of large-scale Cloud applications in terms of geographic distribution of both computing servers and user workloads. To fill this gap in tools for evaluation and modeling of Cloud environments and applications, we propose CloudAnalyst. It was developed to simulate large-scale Cloud applications with the purpose of studying the behavior of such applications under various deployment configurations. CloudAnalyst helps developers with insights in how to distribute applications among Cloud infrastructures and value added services such as optimization of applications performance and providers incoming with the use of Service Brokers.

关键词： Cloud Computing Modeling Simulation

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Evolutionary Self-learning Scheduling Approach for Wireless Sensor Network

Evolutionary Self-learning Scheduling Approach for Wireless ...

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2010 International Conference on intelligent Computation Technology and Automation(2010 智能计算技术与自动化国际会议 ICICTA 2010)

作者： Jianjun Niu State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China Beijing Special Engineering Design and Resear

Energy efficiency is very important for wireless sensor network (WSN). This paper presents an evolutionary self-learning scheduling approach (ESSA) to reduce energy consumption for WSN. The ESSA is based on a new proposed scheme - evolutionary Q-learning with continuousaction (EQC) approach, which combines an extension of Q-learning method with particle swarm optimization (PSO) algorithm. The action space of EQC is partitioned into lots of subintervals. And each endpoint of the subintervals is characterized by a discrete action value and a Q-value. The continuous action value is the weighted average of discrete actions according to their Q values. The PSO algorithm is combined to let an agent profit the experience of other agents. We valid the ESSA in a MAC protocol and simulation results show that the ESSA is an effective method and performs much better than SMAC protocol.

关键词： Wirless Sensor Network Scheduling Q-Learning Particle Swarm Optimization

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Locally indistinguishable subspaces spanned by three-qubit unextendible product bases

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Physical Review A 2010年第3期81卷 032329-032329页

作者： Runyao Duan Yu Xin Mingsheng Ying Centre for Quantum Computation and Intelligent Systems (QCIS) Faculty of Engineering and Information Technology University of Technology Sydney New South Wales 2007 Australia State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing 100084 People's Republic of China Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Physics Tsinghua University Beijing 100084 People's Republic of China

We study the local distinguishability of general multiqubit states and show that local projective measurements and classical communication are as powerful as the most general local measurements and classical communication. Remarkably, this indicates that the local distinguishability of multiqubit states can be decided efficiently. Another useful consequence is that a set of orthogonal n-qubit states is locally distinguishable only if the summation of their orthogonal Schmidt numbers is less than the total dimension 2n. Employing these results, we show that any orthonormal basis of a subspace spanned by arbitrary three-qubit orthogonal unextendible product bases (UPB) cannot be exactly distinguishable by local operations and classical communication. This not only reveals another intrinsic property of three-qubit orthogonal UPB but also provides a class of locally indistinguishable subspaces with dimension 4. We also explicitly construct locally indistinguishable subspaces with dimensions 3 and 5, respectively. Similar to the bipartite case, these results on multipartite locally indistinguishable subspaces can be used to estimate the one-shot environment-assisted classical capacity of a class of quantum broadcast channels.

关键词： Quantum computers

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Multipartite-to-bipartite entanglement transformations and polynomial identity testing

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Physical Review A 2010年第5期81卷 052310-052310页

作者： Eric Chitambar Runyao Duan Yaoyun Shi Physics Department University of Michigan 450 Church Street Ann Arbor Michigan 48109-1040 USA Centre for Quantum Computation and Intelligent Systems (QCIS) Faculty of Engineering and Information Technology University of Technology Sydney New South Wales 2007 Australia and State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing 100084 People’s Republic of China Department of Electrical Engineering and Computer Science University of Michigan 2260 Hayward Street Ann Arbor Michigan 48109-2121 USA

We consider the problem of deciding if some multiparty entangled pure state can be converted, with a nonzero success probability, into a given bipartite pure state shared between two specified parties through local quantum operations and classical communication. We show that this question is equivalent to the well-known computational problem of deciding if a multivariate polynomial is identically zero. Efficient randomized algorithms developed to study the latter can thus be applied to our question. As a result, a given transformation is possible if and only if it is generically attainable by a simple randomized protocol.

关键词： Quantum entanglement

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Tensor rank of the tripartite state |W〉⊗n

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Physical Review A 2010年第1期81卷 014301-014301页

作者： Nengkun Yu Eric Chitambar Cheng Guo Runyao Duan State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing 100084 China and Centre for Quantum Computation and Intelligent Systems (QCIS) Faculty of Engineering and Information Technology University of Technology Sydney New South Wales 2007 Australia Physics Department University of Michigan 450 Church Street Ann Arbor Michigan 48109-1040 USA

Tensor rank refers to the number of product states needed to express a given multipartite quantum state. Its nonadditivity as an entanglement measure has recently been observed. In this Brief Report, we estimate the tensor rank of multiple copies of the tripartite state |W〉=13(|100〉+|010〉+|001〉). Both an upper bound and a lower bound of this rank are derived. In particular, it is proven that the rank of |W〉⊗2 is 7, thus resolving a previously open problem. Some implications of this result are discussed in terms of transformation rates between |W〉⊗n and multiple copies of the state |GHZ〉=12(|000〉+|111〉).

关键词： Tensors

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Particle swarm optimization of high-frequency transformer

Particle swarm optimization of high-frequency transformer

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Annual Conference of Industrial Electronics Society

作者： Hengsi Qin Jonathan W. Kimball Ganesh K. Venayagamoorthy Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA Real-Time Power and Intelligent Systems Laboratory Missouri University of Science and Technology Rolla MO USA

A high frequency transformer is a critical component in a dual active bridge converter (DAB) used in a power electronics-based solid state transformer. Operation of a DAB converter requires its transformer to have a specific amount of winding leakage inductance. The demand for high efficiency requires minimization of transformer copper loss and core loss. Furthermore, available window area limits the winding arrangement of transformer. A hybrid of particle swarm optimization (PSO) and differential evolution (DE) is proposed to solve this multi-objective problem with constraints. DE provides diversity to pbest of each particle, which is missing in standard PSO. The differential evolution particle swarm optimization (DEPSO) algorithm is applied to find optimal transformer designs for DAB converters. Results show the DEPSO method is a generic effective way to find optimal high frequency transformer design.

关键词： Windings Inductance Magnetic cores Converters Resistance Particle swarm optimization Optimization

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International Conference on Image Analysis and Signal Processing, IASP

作者： Yanmin Luo Minghong Liao Zhipeng Zhan College of Computer Science & Technology Huaqiao University Quanzhou China Congnitive Science Department Fujian Key Laboratory of the Brain-Like Intelligent Systems Xiamen University Xiamen China Software School of Xiamen University Xiamen China

Time-series data mining is a very important element of data mining. As a typical time-series data, video data has been widely used for many applications such as film, medical, sports and traffic areas. In this paper we integrates several technologies into the video moving object time-series data mining. We propose a similarity analysis and clustering algorithm for videos based on the moving trajectory time series data wavelet transform of the moving object in the videos. Utilizing the image difference technique, our algorithm detects the moving objects from the scene surveillance video, calculates the centroid of the moving object, and uses the centroid series to character the moving trajectory of the object. Then we use wavelet analysis method to achieve dimensionality reduction and get the first k wavelet coefficient to substitute the original motion time-serial data. Based on the Euclidean distance, we utilize two judgement rules to determine the similarity of time-serial data and cluster them, use the rules to perform the similarity search and clustering of video. We apply the algorithm to athletic sports videos analysis.

关键词： Time series analysis Algorithm design and analysis Clustering algorithms Wavelet analysis Wavelet transforms Data mining Biomedical imaging Object detection Layout Surveillance

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TRACER: A Trace Replay Tool to Evaluate Energy-Efficiency of Mass Storage systems

TRACER: A Trace Replay Tool to Evaluate Energy-Efficiency of...

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IEEE International Conference on Cluster Computing

作者： Zhuo Liu Fei Wu Xiao Qin Changsheng Xie Jian Zhou Jianzong Wang Wuhan National Laboratory of Optoelectronics Key Laboratory of Data Storage Systems Ministry of Education of China School of Computer Science and Tech Huazhong University of Science and Technology Wuhan Hubei China Department of Computer Science and Software Engineering Aubum University Auburn AL USA

Improving energy efficiency of mass storage systems has become an important and pressing research issue in large HPC centers and data centers. New energy conservation techniques in storage systems constantly spring up; however, there is a lack of systematic and uniform way of accurately evaluating energy-efficient storage systems and objectively comparing a wide range of energy-saving techniques. This research presents a new integrated scheme, called TRACER, for evaluating energy-efficiency of mass storage systems and judging energy-saving techniques. The TRACER scheme consists of a toolkit used to measure energy efficiency of storage systems as well as performance and energy metrics. In addition, TRACER contains a novel and accurate workload-control module to acquire power varying with workload modes and I/O load intensity. The workload generator in TRACER facilitates a block-level trace replay mechanism. The main goal of the workload-control module is to select a certain percentage (e.g., anywhere from 10% to 100%) of trace entries from a real-world I/O trace file uniformly and to replay filtered trace entries to reach any level of I/O load intensity. TRACER is experimentally validated on a general RAID5 enterprise disk array. Our experiments demonstrate that energy-efficient mass storage systems can be accurately evaluated on full scales by TRACER. We applied TRACER to investigate impacts of workload modes and load intensity on energy-efficiency of storage devices. This work shows that TRACER can enable storage system developers to evaluate energy efficiency designs for storage systems.

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Approximate Data Collection for Wireless Sensor Networks

Approximate Data Collection for Wireless Sensor Networks

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International Conference on Parallel and Distributed systems (ICPADS)

作者： Chao Wang Huadong Ma Yuan He Shuguang Xiong Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications Beijing China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong China Department of Computer Science and Technology Harbin Institute of Technology Harbin China

Data collection is a fundamental issue in wireless sensor networks. In many application scenarios for sensor networks, approximate data collection is a wise choice due to the constraints in communication bandwidth and energy budget. In this paper, we focus on efficient approximate data collection with given error bounds in wireless sensor networks. The key idea of our data collection approach ADC (Approximate Data Collection) is to divide a sensor network into clusters, discover local data correlations on each cluster head, and perform a global approximate data collection on the sink according to model parameters uploaded by cluster heads. Specifically, we propose a local estimation model to approximate the readings of several subsets of sensor nodes, and prove rated error-bounds of data collection using this model. In the process of model-based data collection, we formulate the problem of selecting the minimum subset of sensor nodes into a minimum dominating set problem which is known to be NP-hard, and use a greedy heuristic algorithm to find an approximate solution. We also propose a monitoring algorithm to adjust these subsets according to the changes of sensor readings. Our trace-driving simulation results show that our data collection approach ADC can notably reduce the communication cost with given error bounds.

关键词： Approximation methods Estimation Data models Correlation Approximation algorithms Clustering algorithms Wireless sensor networks

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Self-learning Scheduling Approach for Wireless Sensor Network

Self-learning Scheduling Approach for Wireless Sensor Networ...

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The 2010 2nd International Conference on Future computer and Communication(第二届未来计算机与通信国际会议 ICFCC 2010)

ISBN: (纸本)9781424458219;9781424458240

Energy efficiency is one of key issues of wireless sensor network (VVSN). In this paper, we propose a self-learning scheduling approach (SSA) to reduce energy consumption for wireless sensor network (WSN). This approach integrates sleep scheduling together with packet transmission scheduling to reduce energy consumption. It enables nodes to learn continuous transmission parameters and sleep parameter through interacting with the WSN. The continuous value of transmission parameter is achieved by our extension of Q-learning method, and the value of sleep parameter can be calculated from the transmission parameter. We valid this approach in a MAC protocol and compare some network performances between the SSA and SMAC protocol. The simulation results show that our SSA performs much better than SMAC protocol in these QoS metrics.

关键词： Wireless sensor network scheduling reinforcement learning

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