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Assessing the benefits of search-based approaches when designing self-adaptive systems: a controlled experiment

Journal of Software Engineering Research and Development 2015年第1期3卷 1-27页

作者： Sandro S Andrade Raimundo J de A Macêdo Distributed Systems Laboratory (LaSiD) Federal University of Bahia (UFBa) Institute of Mathematics Department of Computer Science Salvador-BA Brazil GSORT Distributed Systems Group Federal Institute of Education Science and Technology of Bahia (IFBa) Department of Computer Science Salvador-BA Brazil

The well-orchestrated use of distilled experience, domain-specific knowledge, and well-informed trade-off decisions is imperative if we are to design effective architectures for complex software-intensive systems. In particular, designing modern self-adaptive systems requires intricate decision-making over a remarkably complex problem space and a vast array of solution mechanisms. Nowadays, a large number of approaches tackle the issue of endowing software systems with self-adaptive behavior from different perspectives and under diverse assumptions, making it harder for architects to make judicious decisions about design alternatives and quality attributes trade-offs. It has currently been claimed that search-based software design approaches may improve the quality of resulting artifacts and the productivity of design processes, as a consequence of promoting a more comprehensive and systematic representation of design knowledge and preventing design bias and false intuition. To the best of our knowledge, no empirical studies have been performed to provide sound evidence of such claim in the self-adaptive systems domain. This paper reports the results of a quasi-experiment performed with 24 students of a graduate program in distributed and Ubiquitous Computing. The experiment evaluated the design of self-adaptive systems using a search-based approach proposed by us, in contrast to the use of a non-automated approach based on architectural styles catalogs. The goal was to investigate to which extent the adoption of search-based design approaches impacts on the effectiveness and complexity of resulting architectures. In addition, we also analyzed the approach’s potential for leveraging the acquisition of distilled design knowledge. Our findings show that search-based approaches can improve the effectiveness of resulting self-adaptive systems architectures and reduce their design complexity. We found no evidence regarding the approach’s potential for leveraging the acqu

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Two-dimensional euler PCA for face recognition 21

Two-dimensional euler PCA for face recognition

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21st International Conference on MultiMedia Modeling, MMM 2015

作者： Tan, Huibin 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 and Intelligent Systems and the Faculty of Engineering and Information Technology University of Technology Sydney 235 Jones Street UltimoNSW2007 Australia

ISBN: (纸本)9783319144412

Principal component analysis (PCA) projects data on the directions with maximal variances. Since PCA is quite effective in dimension reduction, it has been widely used in computer vision. However, conventional PCA suffers from following deficiencies: 1) it spends much computational costs to handle high-dimensional data, and 2) it cannot reveal the nonlinear relationship among different features of data. To overcome these deficiencies, this paper proposes an efficient two-dimensional Euler PCA (2D-ePCA) algorithm. Particularly, 2D-ePCA learns projection matrix on the 2D pixel matrix of each image without reshaping it into 1D long vector, and uncovers nonlinear relationships among features by mapping data onto complex representation. Since such 2D complex representation induces much smaller kernel matrix and principal subspaces, 2D-ePCA costs much less computational overheads than Euler PCA on large-scale dataset. Experimental results on popular face datasets show that 2D-ePCA outperforms the representative algorithms in terms of accuracy, computational overhead, and robustness. © Springer International Publishing Switzerland 2015.

关键词： Principal component analysis

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

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

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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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A novel collaborative approach for sinkhole detection in MANETs 13

A novel collaborative approach for sinkhole detection in MAN...

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13th International Conference on Ad-hoc Networks and Wireless, ADHOC-NOW 2014 accompanied by 2nd International Workshop on Emerging Technologies for Smart Devices, ETSD 2014, 2nd International Workshop on Marine Sensors and systems, MARSS 2014, Multimedia Wireless ad hoc Networks, MWaoN 2014, Security in Ad Hoc Networks, SecAN 2014, 2nd Smart Sensor Protocols and Algorithms, SSPA 2014 and 8th International Workshop on Wireless Sensor, Actuator and Robot Networks, WiSARN 2014

作者： Sánchez-Casado, Leovigildo Maciá-Fernández, Gabriel García-Teodoro, Pedro Aschenbruck, Nils Department of Signal Theory Telematics and Communications School of Computer Science and Telecommunications CITIC-UGR University of Granada C/Periodista Daniel Saucedo Aranda S/n Granada18071 Spain Distributed Systems Group Institute of Computer Science University of Osnabrück Albrechtstr. 28 Osnabrück49076 Germany

ISBN: (纸本)9783662463376

This paper presents a novel approach intended to detect sinkholes in MANETs running AODV. The study focuses on the detection of thewell-known sinkhole attack, devoted to attractmost of the surrounding network traffic by providing fake routes, and thus, invalidating alternative legitimate routes and disrupting the normal network *** detection approach relies on the existence of "contamination borders", formed by legitimate nodes under the influence of the sinkhole attack and, at the same time, neighbors of non-contaminated legitimate nodes. Thus, by collecting the routing information of the neighbors, these nodes are likely to be able to properly detect *** evaluate our approach in a simulation framework and the experimental results show the promising nature of this approach in terms of detection capabilities. © Springer-Verlag Berlin Heidelberg 2015.

关键词： Intrusion detection

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curlX: A middleware to enforce access control policies within a cloud environment

curlX: A middleware to enforce access control policies withi...

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IEEE Conference on Communications and Network Security (CNS)

作者： Meryeme Ayache Mohammed Erradi Bernd Freisleben Networking & Distributed Systems Research Group Mohammed V University Rabat Morocco Department of Mathematics & Computer Science University of Marburg Germany

Today cloud security and privacy concerns pose significant challenges. One particular challenge consists in how to take into consideration the user's security policies while accessing the outsourced data. In fact, for a given application, we may have a set of rules, as high level security policy, which needs to hold prior to any query execution. Therefore, the main problem that we tackle in this ongoing project is how to enforce this high level security policy in the cloud storage layer without conflicts. To address this challenges, we propose a middleware denoted curlX which consists of two main processes: the security policy enforcement process and the verification process.

关键词： Cloud computing Access control Data privacy Virtual machining Electronic mail

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UPC++ for bioinformatics: A case study using genome-wide association studies 16

UPC++ for bioinformatics: A case study using genome-wide ass...

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16th IEEE International Conference on Cluster Computing, CLUSTER 2014

作者： Kassens, Jan C. Gonzalez-Dominguez, Jorge Wienbrandt, Lars Schmidt, Bertil Department of Computer Science Christian-Albrechts-University of Kiel Germany Parallel and Distributed Architectures Group Johannes Gutenberg University-Mainz Germany

ISBN: (纸本)9781479955480

Modern genotyping technologies are able to obtain up to a few million genetic markers (such as SNPs) of an individual within a few minutes of time. Detecting epistasis, such as SNP-SNP interactions, in Genome-Wide Association Studies is an important but time-consuming operation since statistical computations have to be performed for each pair of measured markers. Therefore, a variety of HPC architectures have been used to accelerate these studies. In this work we present a parallel approach for multi-core clusters, which is implemented with UPC++ and takes advantage of the features available in the Partitioned Global Address Space and Object Oriented Programming models. Our solution is based on a well-known regression model (used by the popular BOOST tool) to test SNP-pairs interactions. Experimental results show that UPC++ is suitable for parallelizing data-intensive bioinformatics applications on clusters. For instance, it reduces the time to analyze a real-world dataset with more than 500,000 SNPs and 5,000 individuals from several days when using a single core to less than one minute using 512 nodes (12,288 cores) of a Cray XC30 supercomputer. © 2014 IEEE.

关键词： Bioinformatics

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ADVISE – A framework for evaluating cloud service elasticity behavior 12

ADVISE – A framework for evaluating cloud service elasticit...

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12th International Conference on Service-Oriented Computing, ICSOC 2014

作者： Copil, Georgiana Trihinas, Demetris Truong, Hong-Linh Moldovan, Daniel Pallis, George Dustdar, Schahram Dikaiakos, Marios Distributed Systems Group Vienna University of Technology Austria Computer Science Department University of Cyprus Cyprus

ISBN: (纸本)9783662453902

Complex cloud services rely on different elasticity control processes to deal with dynamic requirement changes and workloads. However, enforcing an elasticity control process to a cloud service does not always lead to an optimal gain in terms of quality or cost, due to the complexity of service structures, deployment strategies, and underlying infrastructure dynamics. Therefore, being able, a priori, to estimate and evaluate the relation between cloud service elasticity behavior and elasticity control processes is crucial for runtime choices of appropriate elasticity control processes. In this paper we present ADVISE, a framework for estimating and evaluating cloud service elasticity behavior. ADVISE gathers service structure, deployment, service runtime, control processes, and cloud infrastructure information. Based on this information, ADVISE utilizes clustering techniques to identify cloud elasticity behavior produced by elasticity control. Our experiments show that ADVISE can estimate the expected elasticity behavior, in time, for different cloud services thus being a useful tool to elasticity controllers for improving the quality of runtime elasticity control decisions. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Elasticity

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Using restricted transactional memory to build a scalable in-memory database 14

Using restricted transactional memory to build a scalable in...

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9th ACM European Conference on computer systems, EuroSys 2014

作者： Wang, Zhaoguo Qian, Hao Li, Jinyang Chen, Haibo School of Computer Science Fudan University China Institute of Parallel and Distributed Systems Shanghai Jiao Tong University China Department of Computer Science New York University United States

ISBN: (纸本)9781450327046

The recent availability of Intel Haswell processors marks the transition of hardware transactional memory from research toys to mainstream reality. DBX is an in-memory database that uses Intel's restricted transactional memory (RTM) to achieve high performance and good scalability across multicore machines. The main limitation (and also key to practicality) of RTM is its constrained working set size: An RTM region that reads or writes too much data will always be aborted. The design of DBX addresses this challenge in several ways. First, DBX builds a database transaction layer on top of an underlying shared-memory store. The two layers use separate RTM regions to synchronize shared memory access. Second, DBX uses optimistic concurrency control to separate transaction execution from its commit. Only the commit stage uses RTM for synchronization. As a result, the working set of the RTMs used scales with the meta-data of reads and writes in a database transaction as opposed to the amount of data read/written. Our evaluation using TPC-C workload mix shows that DBX achieves 506,817 transactions per second on a 4-core machine. Copyright © 2007 by the Association for Computing Machinery, Inc.

关键词： Concurrency control

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Correntropy Supervised Non-negative Matrix Factorization

Correntropy Supervised Non-negative Matrix Factorization

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International Joint Conference on Neural Networks

作者： Wenju Zhang Naiyang Guan Dacheng Tao Bin Mao Xuhui Huang Zhigang Luo Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha Hunan China 410073 Center for Quantum Computation and Intelligent Systems FEIT University of Technology Sydney Sydney NSW 2007 Australia College of Science National University of Defense Technology Changsha Hunan China 410073 Department of Computer Science and Technology College of Computer National University of Defense Technology Changsha Hunan China 410073

ISBN: (纸本)9781479919611

Non-negative matrix factorization (NMF) is a powerful dimension reduction method and has been widely used in many pattern recognition and computer vision problems. However, conventional NMF methods are neither robust enough as their loss functions are sensitive to outliers, nor discriminative because they completely ignore labels in a dataset. In this paper, we proposed a correntropy supervised NMF (CSNMF) to simultaneously overcome aforementioned deficiencies. In particular, CSNMF maximizes the correntropy between the data matrix and its reconstruction in low-dimensional space to inhibit outliers during learning the subspace, and narrows the minimizes the distances between coefficients of any two samples with the same class labels to enhance the subsequent classification performance. To solve CSNMF, we developed a multiplicative update rules and theoretically proved its convergence. Experimental results on popular face image datasets verify the effectiveness of CSNMF comparing with NMF, its supervised variants, and its robustified variants.

关键词： NMF subsequent classification NMF protocol matrix decomposition outliers Nonnegative LOSS FUNCTION images REDUCTION METHODS Dataset computer vision

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Density estimation with adaptive sparse grids for large data sets 14

Density estimation with adaptive sparse grids for large data...

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14th SIAM International Conference on Data Mining, SDM 2014

作者： Peherstorfer, Benjamin Pflüger, Dirk Bungartz, Hans-Joachim Aerospace Computational Design Laboratory AeroAstro Massachusetts Institute of Technology United States Simulation of Large Systems Group Institute for Parallel and Distributed Systems University of Stuttgart Germany Scientific Computing Group Department of Computer Science Technische Universität München Germany

ISBN: (纸本)9781510811515

Nonparametric density estimation is a fundamental problem of statistics and data mining. Even though kernel density estimation is the most widely used method, its performance highly depends on the choice of the kernel bandwidth, and it can become computationally expensive for large data sets. WTe present an adaptive sparse-grid-based density estimation method which discretizes the estimated density function on basis functions centered at grid points rather than on kernels centered at the data points. Thus, the costs of evaluating the estimated density function are independent from the number of data points. We give details on how to estimate density functions on sparse grids and develop a cross validation technique for the parameter selection. We show numerical results to confirm that our sparse-grid-based method is well-suited for large data sets, and, finally, employ our method for the classification of astronomical objects to demonstrate that it is competitive to current kernel-based density estimation approaches with respect to classification accuracy and runtime. Copyright © SIAM.

关键词： Data mining

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