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Resource allocation is vital for improving system performance in big data processing. The resource demand for various applications can be heterogeneous in cloud computing. Therefore, a resource gap occurs while some resource capacities are exhausted and other resource capacities on the same server are still available. This phenomenon is more apparent when the computing resources are more heterogeneous. Previous resource-allocation algorithms paid limited attention to this situation. When such an algorithm is applied to a server with heterogeneous resources, resource allocation may result in considerable resource wastage for the available but unused resources. To reduce resource wastage, a resource-allocation algorithm, called the minimizing resource gap (MRG) algorithm, for heterogeneous resources is proposed in this study. In MRG, the gap between resource usages for each server in cloud computing and the resource demands among various applications are considered. When an application is launched, MRG calculates resource usage and allocates resources to the server with the minimized usage gap to reduce the amount of available but unused resources. To demonstrate MRG performance, the MRG algorithm was implemented in Apache Spark. CPU- and memory-intensive applications were applied as benchmarks with different resource demands. Experimental results proved the superiority of the proposed MRG approach for improving the system utilization to reduce the overall completion time by up to 24.7% for heterogeneous servers in cloud computing. [ABSTRACT FROM AUTHOR]

This paper presents our experimental studies on improving the speed of solving nonogram puzzles. Our approach is based on the algorithm developed by Wu et al. A puzzle solving program, named LalaFrogKK, implementing the algorithm has won several nonogram tournaments since 2011. The algorithm consists of three major parts: propagation based on line solving, fully probing, and backtracking. Fully probing, running before backtracking, contributes to solving a nonogram puzzle in two ways. The first is to paint more pixels after propagation. The second is testing each unpainted pixel for providing backtracking with more accurate guidance when choosing pixels to guess. However, we found that different probing sequences can lead to significant differences in the numbers of painted pixels after fully probing. Therefore, in this paper, we investigate into the effects of different fully probing sequences, and propose several new fully probing mechanisms. Experimental results show that the new mechanisms can significantly outperform the original fully probing method. One of the new fully probing mechanisms achieves a speedup of more than two times in solving nonogram puzzles. [ABSTRACT FROM AUTHOR]

Nonogram is a typical two-dimensional logical puzzle game in which each pixel involves two constraints associated to the intersecting row and column. The recently proposed approaches can efficiently solve many puzzles via logical deduction based on the 2-SAT formulas. This paper proposes a set of new logical properties for inferencing the consistent and inverse relations among pixels. We show that the pixels with consistent (or inverse) relation must be painted to a same (or an opposite) color in the solution puzzle. Consequently, the pixels can be aggregated into groups, thereby the space of search tree of the Nonogram backtracking algorithm can be reduced. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a Platform of Extensible Workflow Simulation Service (Pewss), which we have developed to provide a cloud service for aiding research work in workflow scheduling. The simulation has been a major tool for performance evaluation and comparison in workflow scheduling research. However, researchers usually have to develop their own simulation programs with limited functionality, simply outputting summarized performance results. Pewss has been developed for easing and improving current practices in conducting performance simulations during studying of existing workflow scheduling algorithms or designing of new scheduling algorithms. Pewss has been designed based on the Software as a Service (SaaS) model, adopting a multiuser Web‐based client/server architecture. Conducting simulation experiments on Pewss, researchers simply have to implement the scheduling algorithm under study instead of a whole simulation environment, allowing them to focus on their research work without spending unnecessary efforts on the simulation implementation details. Pewss provides the visualization of a workflow execution schedule based on simulation results, offering a convenient way for researchers to gain an insight into the effectiveness, characteristics, and performance bottleneck of scheduling algorithms. As a multiuser environment, Pewss also provides functionality for researchers to facilitate comparative performance analysis and collaborative research works effectively. Pewss has been used in our research work on task‐parallel workflow scheduling and has been planned to be extended to support other types of workflow scheduling research problems, eg, mixed‐parallel workflows. [ABSTRACT FROM AUTHOR]

This paper proposes a novel nonisolated single-switch cascaded high step-up converter. The converter consists of coupled inductors, a clamp circuit, and cascaded capacitors to achieve high step-up voltage output. Only one switch is used in the proposed converter; the switch can reduce cost efficiently and simplify the control of the proposed converter. The converter also possesses an energy-recycle mechanism for recycling the spike energy of a leakage inductor. In addition, a clamp circuit is used to reduce voltage-stress across the switch, and a cascaded design is used to reduce voltage-stress across diodes and output capacitor. Thus, the proposed converter can select a low-voltage stress switch for reducing circuit loss and improving the efficiency of the converter. Finally, in this study, a 400-W nonisolated cascaded high step-up converter was implemented, of which the input and output voltages are 48 and 400 V, respectively. A microcontroller dsPIC30F4011 was used to control the converter and verify system effects and feasibility. The maximum efficiency of the proposed converter is 95% and the efficiency under a full load is 93%. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper presents a novel bidirectional DC-to-DC converter charge/discharge controller for solar energy illumination system. The bidirectional converter architecture integrates the synchronous rectification Single Ended Primary Inductor Converter (SEPIC) converter and an active clamp flyback converter. In addition to fully use the properties of the shared components and compensate for the shortcomings of conventional two-stage illumination systems, the proposed system has the advantages of soft-switching, simple structure, and high efficiency. During daytime, a SEPIC converter with synchronous rectification function was used to charge the lead acid battery through three-stage charging and maximum power point tracking. At night, the battery discharges, driving and dimming high-brightness LEDs using the active clamp flyback converter. Finally, a solar energy illumination system with both a 160 W charge/discharge controller and an 80 W LED driver was implemented to verify the feasibility and practicality of the proposed system. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]