Automated surface inspection (ASI) is a challenging task in industry, as collecting training dataset is usually costly and related methods are highly dataset-dependent. In this paper, a generic approach that requires small training data for ASI is proposed. First, this approach builds classifier on the features of image patches, where the features are transferred from a pretrained deep learning network. Next, pixel-wise prediction is obtained by convolving the trained classifier over input image. An experiment on three public and one industrial data set is carried out. The experiment involves two tasks: 1) image classification and 2) defect segmentation. The results of proposed algorithm are compared against several best benchmarks in literature. In the classification tasks, the proposed method improves accuracy by 0.66%-25.50%. In the segmentation tasks, the proposed method reduces error escape rates by 6.00%-19.00% in three defect types and improves accuracies by 2.29%-9.86% in all seven defect types. In addition, the proposed method achieves 0.0% error escape rate in the segmentation task of industrial data.

The virtualization technology enables multi-tier web application to be hosted in the Cloud. But the Service Level Agreement (SLA) is likely to be negatively affected when the network traffic is high as they may quickly overload the data center network and increase the response time of the system. Additionally, data centers experience high operational costs due to their considerable amount of energy consumption. The Virtual Machine (VM) technology and VM migration have been widely utilized to deal with such challenges. In this paper, we present design and implementation of an adaptive network-aware VM migration algorithm. The VM and target selection policies are based on the steady state network traffic in the system to minimize the negative effect of migration on other flows on the network. Moreover, we address the high energy consumption of the data center by employing an energy-aware VM placement algorithm. The effectiveness of our proposed VM migration algorithm is evaluated by extensive simulations in CloudSim using real workload traces. We compared two overloading detection policies in our experiments. The results show that our algorithm is able to improve the SLA violation (SLAV) and energy consumption up to 73% and 81%, respectively.

In today's big data era, there is a tremendously huge amount of data available. Layman users lack not only the knowledge and experience in data analytics to make sense of these data but also the computational resources for executing the analytics. In this paper, we propose and develop a layman analytics system (LAS), which provides the layman users with a scalable and ready-to-use analytics tool to automatically generate analytics workflows for classification tasks. The LAS is designed to benefit from existing open-source data analytics tools using generic ontological modeling of analytics operators from these tools as well as adaptive constraint refinement for metadata learning. Moreover, the LAS can be deployed on both public and private clouds to cater to the need of scalable computing and easy maintenance. To demonstrate the performance of the LAS, we conducted experiments with 114 data sets obtained from the University of California Irvine Machine Learning Repository. The workflows generated by the LAS were benchmarked against the OpenML whereby each data set has a range of classification accuracy obtained using classifiers designed and fine-tuned by data experts. The comparisons showed that 87 out of 114 data sets have exceeded the 50th percentile of the benchmark data. Among these 87 data sets, the LAS outperforms the 90th percentile of the benchmarks on 49 data sets.

Approaching a comprehensive performance benchmark for on-line transaction processing (OLTP) applications in a cloud environment is a challenging task. Fundamental features of clouds, such as the pay-as-you-go pricing model and unknown underlying configuration of the system, are contrary to the basic assumptions of available benchmarks such as TPC-W or RUBiS. In this paper, we introduce a systematic performance benchmark approach for OLTP applications on public clouds that use virtual machines(VMs). We propose WPress benchmark, which is based on the widespread blogging software, WordPress, as a representative OLTP application and implement an open source workload generator. Furthermore, we utilize a CPU micro-benchmark to investigate CPU performance of cloud-based VMs in greater detail. Average response time and total VM cost are the performance metrics measured by WPress. We evaluate small and large instance types of three real-life cloud providers, Amazon EC2, Microsoft Azure and Rackspace cloud. Results imply that Rackspace cloud has better average response times and total VM cost on small instances. However, Microsoft Azure is preferable for large instance type.

Formulation of appropriate data analytics workflows requires intricate knowledge and rich experiences of data analytics experts. This problem is further compounded by continuous advancement and improvement in analytical algorithms. In this paper, a generic non-domain specific solution for the creation of appropriate workflows targeted at supervised learning problems is proposed. Our adaptive workflow recommendation engine based on collaborative analytics matches analytics needs with relevant workflows in repository. It is capable of picking workflows with better performance as compared to randomly selected workflows. The recommendation engine is now augmented by a workflow optimizer that applies genetic programming to further improve the recommended workflows through iterative evolution, leading to better alternative workflows. This unique Collaborative Analytics Recommender System is tested on seven UCI benchmark datasets. It is shown that the final workflows produced by the system could closely approximate, in terms of accuracy, the best workflows that analytics experts could possibly design.

The concept of Data Value Chain (DVC) involves the chain of activities to collect, manage, share, integrate, harmonize and analyze data for scientific or enterprise insight. For some applications, it also entails the leverage of visualization and simulation. However, the curse of big data (volume, velocity, variety) makes it difficult to efficiently handle and understand the data in near real-time. To address these challenges, this paper proposed the Data Value Chain as a Service (DVCaaS) framework, a data-oriented approach for data handling, data security and analytics in the cloud environment.

A goal for the analysis of supercomputer logs is to establish causal relationships among events which reflect significant state changes in the system. Establishing these relationships is at the heart of failure diagnosis. In principle, a log analysis tool could automate many of the manual steps systems administrators must currently use to diagnose system failures. However, supercomputer logs are unstructured, incomplete and contain considerable ambiguity so that direct discovery of causal relationships is difficult. This paper describes the second generation FDiag log-based failure diagnostics framework that provides automation of the manual failure diagnosis process and determines with high confidence, the likely cause of the failure, the components involved and the event sequences which contain the times of the causal and terminal events. FDiag extracts relevant events from the system logs, performs correlation analysis on these events and from these correlations determines the components involved and the event sequences. The diagnostics capabilities of FDiag are validated by comparing its assessments on known instances of recurrent failures on the Ranger supercomputer at the University of Texas at Austin. We believe FDiag is the first log analyzer to demonstrate this level of diagnostics capability from the system logs of an open source software stack incorporating Linux and the Lustre file system. FDiag will be put into production use for support of failure diagnosis on Ranger in September, 2011.

System event logs are often the primary source of information for diagnosing (and predicting) the causes of failures for cluster systems. Due to interactions among the system hardware and software components, the system event logs for large cluster systems are comprised of streams of interleaved events, and only a small fraction of the events over a small time span are relevant to the diagnosis of a given failure. Furthermore, the process of troubleshooting the causes of failures is largely manual and ad-hoc. In this paper, we present a systematic methodology for reconstructing event order and establishing correlations among events which indicate the root-causes of a given failure from very large syslogs. We developed a diagnostics tool, FDiag, to extract the log entries as structured message templates and uses statistical correlation analysis to establish probable cause and effect relationships for the fault being analyzed. We applied FDiag to analyze failures due to breakdowns in interactions between the Lustre file system and its clients on the Ranger supercomputer at the Texas Advanced Computing Center (TACC). The results are positive. FDiag is able to identify the dates and the time periods that contain the significant events which eventually led to the occurrence of compute node soft lockups.

The discovery of low-energy stable and meta-stable molecular structures remains an important and unsolved problem in search and optimization. In this paper, we contribute two stochastic algorithms, the archiving molecular memetic algorithm (AMMA) and the archiving basin hopping algorithm (ABHA) for sampling low-energy isomers on the landscapes of pure water clusters (H2O)n. We applied our methods to two sophisticated empirical water cluster models, TTM2.1-F and OSS2, and generated archives of low-energy water isomers (H2O)n n=3-15. Our algorithms not only reproduced previously-found best minima, but also discovered new global minima candidates for sizes 9-15 on OSS2. Further numerical results show that AMMA and ABHA outperformed a baseline stochastic multistart local search algorithm in terms of convergence and isomer archival. Noting a performance differential between TTM2.1-F and OSS2, we analyzed both model landscapes to reveal that the global and local correlation properties of the empirical models differ significantly. In particular, the OSS2 landscape was less correlated and hence, more difficult to explore and optimize. Guided by our landscape analyses, we proposed and demonstrated the effectiveness of a hybrid local search algorithm, which significantly improved the sampling performance of AMMA on the larger OSS2 landscapes. Although applied to pure water clusters in this paper, AMMA and ABHA can be easily modified for subsequent studies in computational chemistry and biology. Moreover, the landscape analyses conducted in this paper can be replicated for other molecular systems to uncover landscape properties and provide insights to both physical chemists and evolutionary algorithmists.

In this paper, we study how resources within a large High Performance Computing (HPC) cluster can be dynamically partitioned to optimize client utility for multiple service classes. We model service effectiveness using both perceived service quality and resources required. Using empirical data obtained from A*STAR Computational Resource Center (A*CRC), we analyze how quality metrics and statistical characteristics of HPC jobs affect user satisfaction. We derive the optimal number of processors required to achieve the maximal overall client utility in M/G/1 based clusters. Based on measured job characteristics, we propose a Statistics-based Client Utility Optimization (SCUO) algorithm, which dynamically partitions the cluster into resource groups serving different service classes. Simulations show that our proposed algorithm is able to achieve better performance with both higher client utility and higher job admission rates.

Accurate rainfall intensity nowcasting has many applications such as flash flood defense and sewer management. Conventional computational intelligence tools do not take into account temporal information, and the series of rainfall is treated as continuous time series. Unfortunately, rainfall intensity is not a continuous time series as it has different dry periods in between raining seasons. Hence, conventional computational intelligence tools sometimes are not able to offer acceptable accuracy. An ensemble constitutes of classification, regression and reward models is proposed. The classification model identifies rain or no rain episodes, whereas the regression model predicts the rainfall intensity. The error of the regression model is then predicted by the reward regression model. Through that, the spatial information is captured by the classification model, and the temporal information is captured by the regression and reward models. Preliminary experimental results are encouraging.

The design and engineering of complex materials and products often requires intricate interactions between domain experts in science, material and engineering as well as the utilization of diverse software systems for discovery and optimization. If left as it is, design engineers would most likely be at a loss on how to engage the entire entourage of the multi- disciplinary processes as well as the compute-intensive and data-intensive nature of the activities involved. This paper describes a possible solution through the development of a Grid-based Problem Solving Environment for Engineering of Materials (GPEM). The GPEM aims to provide a one-stop platform where engineers will perform material discovery, design optimization and material characterization, with grid computing as the enabling technology. Upon describing the details of the process workflow and the adopted architecture design, the paper will present the current implementation of GPEM, in the design optimization of fractal structures.

10.1109/CCNC.2007.71

Dengue represents a serious health threat in the Tropics, owing to the year-round presence of Aedes mosquito vectors, and the lack of any anti-viral drugs or vaccines. Climatic factors are important in influencing the incidence of dengue. It is important to determine the relationships between climatic factors and disease incidence trends, which would be helpful for relevant environment and health agencies in planning appropriate pre-emptive control measures. Climatic factors and dengue case records vary over time. It is therefore difficult to justify the time-lag when a climatic factor affects the mosquito-to-human and human-to-mosquito loops. In this paper, we propose to use support vector machine (SVM) classifiers for analyzing the time- series dengue data and genetic algorithm (GA), to determine the time-lags and subset of climatic factors as effective factors influencing the spread of dengue. It is shown that the proposed model is able to detect important climatic factors and their time-lags which affect the disease, and the GA-based SVM classifiers could improve the classification accuracy significantly.

The convergence of grid computing technologies and Web services offers many opportunities to utilize resources distributed across the Internet and solves many issues of interoperability. As a result, enabling applications as Web services are required intensively. Hence, a framework for "gridifying" and running applications on service-oriented grids (GRASG) was built to offer developers a flexible and effective tool for "gridifying" applications and making use of distributed resources on grid environment without much effort from the developers. It allows users to quickly enable an application as a Web service and access this service in a simple fashion. Further, in order to make use of distributed resources, GRASG provides a metascheduling mechanism that is able to schedule jobs to grid resources using Web services protocol. These features reduce the time taken for application development and execution.

The Information Service is a core component in the Grid software infrastructure. It provides diverse information to users or other service components in Grid environments. In this paper, we propose an Information Service architecture model for information management in a Grid Virtual Organization (VO). This Information Service is a hierarchical structure which consists of the VO layer, site layer and resource layer: at the resource layer, information agents and pluggable information sensors are deployed on each resource monitored. This information agent and sensor approach provides a flexible framework that enables specific information to be captured; at the site layer, a site information service component with caching capability aggregates and maintains up-to-date information of all the resources monitored within an administrative domain; at the VO layer, a peer-to-peer approach is used to build a virtual network of site information services for information discovery and query in a large scale Grid VO. This decentralized approach makes information management scalable and robust. Our Information Service has been implemented based on the Globus Toolkit 4 as a Web service compliant to the Web Services Resource Framework (WSRF) specifications. The experimental results show that the Information Service presents satisfactory scalability in handling information for large scale Grids.

In this paper, we propose a novel resource scheduling strategy, referred to as the Multi-Resource Scheduling (MRS) algorithm, which is capable of handling several resources to be used among jobs that arrive at a Grid Computing Environment. We propose a model in which the job and resource characteristics are captured together and are used in the scheduling strategy. To do so, we introduce the concept of virtual map and resource potential. Based on the proposed model, simulations with realistic workload traces were conducted to quantify the performance. We compare our strategy with some of the commonly used algorithms, and show that MRS renders a higher performance in all cases. Our experimental results clearly show that MRS outperforms other strategies and we highlight the impact and importance of our strategy.

In this paper, we proposed to setup a distributed multimedia system which aggregates the capacity of multiple servers to provide customized multimedia services in a cost-effective way. Such a system enables clients to customize their services by specifying the service delay or the viewing times. We developed an experimental prototype in which media servers can cooperate in streams caching, replication and distribution. We applied a variety of stream distribution algorithms to the system and studied their performance under the real-life situations with limited network resources and varying request arrival pattern. The results show such a system can provide cost-effective services and be applied to practical environments.

The information service is a key component of a grid environment and critical to the operation of a computational grid. In this work, an OGSA (Open Grid Services Architecture) based information service that complies with OGSI (Open Grid Services Infrastructure) is presented. The main functionality of this information service is the provision of information essential for applications running on a computational grid such as resource information, job status, resource workload, service meta-information, and queue status. This OGSI-compliant information service is built on Globus Toolkit MDS-3, and it works with meta-scheduling services and local job scheduling systems to support resource discovery, job scheduling, and execution management. In this paper, the architecture of the Information Service and the models of information data organization are presented. Some implementation issues are discussed as well.

One of the main challenges in support vector machine (SVM) for data mining applications is to obtain explicit knowledge from the solutions of SVM for explaining classification decisions. This paper exploits the fact that the decisions from a non-linear SVM could be decoded into linguistic rules based on the information provided by support vectors and its decision function. Given a support vector of a certain class, cross points between each line, which is extended from the support vector along each axis, and SVM decision hyper-curve are searched first. A hyper-rectangular rule is derived from these cross points. The hyper-rectangle is tuned by a tuning phase in order to exclude those out-class data points. Finally, redundant rules are merged to produce a compact rule set. Simultaneously, important attributes could be highlighted in the extracted rules. Rule extraction results from our proposed method could follow decisions of SVM classifiers very well. Comparisons between our method and other rule extraction methods are also carried out on several benchmark data sets. Higher rule accuracy is obtained in our method with fewer number of premises in each rule.

Describes methods of increasing parallelism, thereby improving the performance, of waveform relaxation-based parallel circuit simulators. The key contribution is the use of parallel nonlinear relaxation and parallel model evaluation to solve large subcircuits that may lead to load balancing problems. These large subcircuits are further partitioned and solved on clusters of tightly-coupled multiprocessors. This paper describes a general hybrid/hierarchical approach for waveform relaxation, and then focuses on the implementation issues regarding parallel nonlinear relaxation and parallel model evaluation. A number of benchmark circuits are used to demonstrate the improved performance of this approach and its range of applicability in parallel circuit simulation.>

This paper describes a class of parallel algorithms for circuit simulation based on hierarchical relaxation that has been implemented on the Cedar multiprocessor. The Cedar machine is a reconfigurable, general-purpose supercomputer that was designed and implemented at the University of Illinois. A hierarchical circuit simulation scheme was developed to exploit the hierarchical organization of Cedar. The new algorithm and a number of key issues, such as multilevel circuit partitioning, data partitioning, cluster algorithm selection, and cluster algorithm implementation are described in this paper. Performance results on a variety of different configurations of Cedar are also presented that illustrate the benefits of the hierarchical approach over the non-hierarchical approach.