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Usually, a large number of concurrent bag-of-tasks (BoTs) application execution requests are submitted to cloud data centers (CDCs), which needs to be optimally scheduled on the physical cloud resources to obtain maximal performance. In the current paper, NP-Hard cloud task scheduling (CTS) problem for scheduling concurrent BoT applications is modeled as an optimization problem involving minimization of makespan and energy consumption. Whale optimization algorithm (WOA) has been found effective in solving a wide range of optimization problems. However, standard WOA has certain deficiencies such as inadequate exploration ability, slow convergence, high computation complexity, and insufficient exploration–exploitation phase trade-off. These deficiencies eventually result in unacceptable results when the original WOA is applied over task scheduling optimization problems. To address these limitations, a multi-objective scheduling algorithm called OWPSO is suggested, which incorporates opposition-based learning (OBL) and particle swarm optimization (PSO) mechanisms into the standard WOA method. Firstly, the OBL method is applied to produce an optimal initial population to enhance the exploration and convergence speed of the proposed OWPSO approach in the successive generations. Secondly, PSO and OBL methods are incorporated in the exploration phase of the standard WOA approach to enhance exploration ability further. Thirdly, a fitness-based switching mechanism is added to provide an adequate exploration–exploitation phase trade-off. Finally, a discrete resource allocation heuristic is incorporated in the OWPSO to provide an efficient resource allocation. Simulation experiments over the CloudSim simulator reveal that OWPSO approach results in makespan reduction in the range of 1.68−18.38% (for CEA-Curie workloads), 2.10−24.32% (for HPC2N workloads), and energy consumption reduction in the range of 0.93−14.70% (for CEA-Curie workloads), and 0.73−25.94% (for HPC2N workloads) over other well-known meta-heuristics. Statistical tests and box plots further revealed the robustness of proposed OWPSO algorithm. [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]

Cloud computing is an emerging technology which relies on virtualization techniques to achieve the elasticity of shared resources for providing on-demand services. When the service demand increases, more resources are required to satisfy the service demand. Single cloud generally cannot provide unlimited services with limited physical resources; therefore, the federation of multiple clouds may be one possible solution. In such environment, different cloud providers may own different pricing and resource allocating strategies. Thus, how to select the most appropriate provider to host applications becomes an important issue for clients. However, as the requests of accessing distributed resources increase, the occurrences of competing the same resource may also increase. In this study, a Distributed Resource Allocation (DRA) approach is proposed to solve resource competition in the federated cloud environment. Each job is supposed to consist of one or more tasks, and the communication behavior between tasks could be profiled. The proposed approach groups tasks according to communication behavior to minimize communication overhead, and tries to allocate grouped tasks to achieve equilibrium when resource competition occurs. Experimental results show that the cloud provider could obtain more profits by outsourcing resources in the federated cloud with enough resources. [ABSTRACT FROM AUTHOR]

Parallel computation has been widely applied in a variety of large-scale scientific and engineering applications. Many studies indicate that exploiting both task and data parallelisms, i.e. mixed-parallel workflows, to solve large computational problems can get better efficacy compared with either pure task parallelism or pure data parallelism. Scheduling traditional workflows of pure task parallelism on parallel systems has long been known to be an NP-complete problem. Mixed-parallel workflow scheduling has to deal with an additional challenging issue of processor allocation. In this paper, we explore the processor allocation issue in scheduling mixed-parallel workflows of moldable tasks, called M-task, and propose an Iterative Allocation Expanding and Shrinking (IAES) approach. Compared to previous approaches, our IAES has two distinguishing features. The first is allocating more processors to the tasks on allocated critical paths for effectively reducing the makespan of workflow execution. The second is allowing the processor allocation of an M-task to shrink during the iterative procedure, resulting in a more flexible and effective process for finding better allocation. The proposed IAES approach has been evaluated with a series of simulation experiments and compared to several well-known previous methods, including CPR, CPA, MCPA, and MCPA2. The experimental results indicate that our IAES approach outperforms those previous methods significantly in most situations, especially when nodes of the same layer in a workflow might have unequal workloads. [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]

We conducted a chronoamperometric study to observe the electrochemical behaviors of the nucleation mechanism of electrodeposited Cu(In,Al)Se 2 films. A change from instantaneous nucleation to progressive nucleation was observed by increasing the copper concentration. SEM and EDS analyses demonstrated that the surface morphologies and chemical compositions of precursor and post-annealed Cu(In,Al)Se 2 films were influenced by the nucleation mechanism. They exhibited that Cu(In,Al)Se 2 films had roughly cauliflower-like and triangular structures with Cu-poor composition at the instantaneous nucleation mechanism, whereas smooth and round structures with Cu-rich composition at the progressive nucleation mechanism. Additionally, the quality, growth orientation, composition phase, and optical energy band gap of Cu(In,Al)Se 2 films were examined using X-ray diffraction (XRD) patterns, Raman spectra, and ( αhν ) 2 vs. hν plots (UV–vis). Thereafter, the performances of rudimentary Cu(In,Al)Se 2 solar cells fabricated at various copper concentrations were discussed, the conversion efficiency of electrodeposited Cu(In,Al)Se 2 solar cells were unpublished in previous literatures. Our studies demonstrated that the performances of Cu(In,Al)Se 2 solar cells were significantly related to the surface morphology and composition of Cu(In,Al)Se 2 absorber layer. The highest conversion efficiency of Cu(In,Al)Se 2 solar cells fabricated at a 2-mM copper concentration was 1.96% with open-circuit voltage, short-circuit current, fill factor, R sh and R s values of 0.189 V, 29.21 mA/cm 2 , 35.4%, 125 Ω and 2.82 Ω, respectively. [ABSTRACT FROM AUTHOR]

Abstract: In a heterogeneous multi-cluster (HMC) system, processor allocation is responsible for choosing available processors among clusters for job execution. Traditionally, processor allocation in HMC considers only resource fragmentation or processor heterogeneity, which leads to heuristics such as Best-Fit (BF) and Fastest-First (FF). However, those heuristics only favor certain types of workloads and cannot be changed adaptively. In this paper, a temporal look-ahead (TLA) method is proposed, which uses an allocation simulation process to guide the decision of processor allocation. Thus, the allocation decision is made dynamically according to the current workload and system configurations. We evaluate the performance of TLA by simulations, with different workloads and system configurations, in terms of average turnaround time. Simulation results indicate that, with precise runtime information, TLA outperforms traditional processor allocation methods and has up to an 87% performance improvement. [Copyright &y& Elsevier]

Abstract: In this paper, the cyclic voltammetric studies were used to realize the element's reduction potential and chemical reaction mechanism for presuming the formation routes of quaternary Cu(In,Al)Se2 crystals. Thereafter, the prior adjustment of deposited potential from −0.6V to −1.0V can be identified a suitable potential as co-electrodeposition. The material characteristics of Cu(In,Al)Se2 films are dominated by the percentage of aluminum content. Thus, the influence of aluminum and indium concentrations in solutions on the percentage composition, surface morphology, structural and crystal properties, and optical energy band gap of Cu(In,Al)Se2 films were investigated. Energy dispersive X-ray spectroscopy (EDS) indicated that the ratio of Al to (Al+In) in Cu(In,Al)Se2 films varied from 0.21 to 0.42 when adjusting aluminum and indium concentrations in solutions. Scanning electron microscopy (SEM) shows that the surface morphology changed from round-like structures into cauliflower-like structures and became rough when the aluminum concentration increased and indium concentration decreased in solutions. X-ray diffraction (XRD) patterns revealed three preferred growth orientations along the (112), (204/220), and (116/312) planes for all species. The (αhυ)2 versus hυ plots (UV–Visible) shows that the optical energy band gap of the Cu(In,Al)Se2 films can be successfully controlled from 1.17eV to 1.48eV by adjusting the aluminum and indium concentrations. Furthermore, the shift of the (112) peak in the XRD patterns and variation of optical band gap are evidence that the incorporation of aluminum atoms into the crystallitic CuInSe2 forms Cu(In,Al)Se2 crystals. [Copyright &y& Elsevier]

Workflow computing has become an essential part of many scientific and engineering fields, while workflow scheduling has long been a well-known NP-complete research problem. Major previous works can be classified into two categories: heuristic-based and guided random-search-based workflow scheduling methods. Monte Carlo Tree Search (MCTS) is a recently proposed search methodology with great success in AI research on game playing, such as Computer Go. However, researchers found that MCTS also has potential application in many other domains, including combinatorial optimization, task scheduling, planning, and so on. In this paper, we present a new workflow scheduling approach based on MCTS, which is still a rarely explored direction. Several new mechanisms are developed for the major steps in MCTS to improve workflow execution schedules further. Experimental results show that our approach outperforms previous methods significantly in terms of execution makespan. [ABSTRACT FROM AUTHOR]

Grid performance are usually measured by the average turnaround time of all jobs in the system. A job's turnaround time consists of two parts: queue waiting time and actual execution time, which in a heterogeneous grid environment, are severely affected by the resource fragmentation and speed heterogeneity factors. Most existing processor allocation methods focus on one of these two factors only. This paper proposes processor allocation methods, which consider both resource fragmentation and speed heterogeneity, to improve system performance of heterogeneous grids. Extensive simulation studies have been conducted to show that the proposed methods can effectively deliver better performance under most resource and workload conditions. [ABSTRACT FROM AUTHOR]

Abstract: The study addresses the optical properties and morphologies of Al-doped ZnO (AZO) films textured by the chemical wet-etching method using three different acid solutions: HCl, HNO3, and H3PO4. An initial AZO film was sputtered on a glass substrate by rf magnetron sputtering. The film surface was then textured by wet-etching using diluted HCl, HNO3, or H3PO4. The average transmittance of all the post-treated ZnO:Al films remains around 75–80% as measured by a UV–vis analyzer. A haze ratio calculation shows that the light scattering properties can be significantly controlled by varying the etchant species, acid concentration, and etching time. Further, the HNO3 etchant gives the highest haze ratio of 43.0% at a wavelength of 550nm. Compared with solar cells deposited on non-textured ZnO:Al films, a significant enhancement in the short-circuit current density of 17.8% for the a-Si solar cells with textured ZnO:Al films was achieved. [Copyright &y& Elsevier]

Abstract: In this study, the optical properties and morphologies of Al-doped ZnO (AZO) films textured by the chemical wet-etching method with three different acid species are investigated. An initial AZO film is sputtered on a glass substrate by rf magnetron sputtering. The film surface was then textured by wet-etching using diluted HCl, HNO3 or H3PO4. The average transmittance of all the post-treated ZnO:Al films remains around 75–80% as measured by a UV–vis analyzer. A haze ratio calculation shows that the light scattering properties can be significantly controlled by varying the etchant species, acid concentration, and etching time. Atomic force microscopy (AFM) was used to find the average roughness of the textured AZO films. In this study, the HNO3 etchant gives the highest haze ratio of 49.2% at a wavelength of 550nm. The textured ZnO:Al films with an electrical resistivity of 5.47×10−4 Ω-cm, carrier concentrations of 3.98×1020 cm−3 and mobility of 28.7cm2 V−1 s−1, can be obtained when etched in diluted HNO3 for 60s. It is found that the chemical wet-etched AZO glass substrate appears to be helpful in enhancing the short circuit current (J sc) when applied on silicon thin film solar cells. [Copyright &y& Elsevier]

This paper proposes waiting ratio as a basis in evaluating various scheduling methods for dynamic workloads consisting of multi-processor jobs on parallel computers. We evaluate commonly used methods as well as several methods proposed in this paper by simulation studies. The results indicate that some commonly used methods do not improve the waiting ratios as expected by intuition, while some methods proposed in this paper do greatly improve waiting ratios more than 10 times for some workload data, promising in leading to more reasonable waiting time and better user's satisfaction. [ABSTRACT FROM AUTHOR]

Recently, the superior and continuously improving cost-performance ratio of commodity hardware and software has made PC clustering a popular alternative for high-performance computing in both academic institutes and industrial organizations. The purpose of this work is to use PC clusters to solve a weather-prediction model in parallel mode, and the result also will be compared with those obtained on some conventional parallel platforms such as the Fujitsu VPP300, IBM SP2 (160 and 120 MHz), and HP SPP2200. Techniques of domain decomposition and data communication are used to exploit parallelism of the model. Interprocessor data communication is done by the Message Passing Interface communication library routines. Two versions of the parallel codes, one with longitude decomposition and the other with latitude decomposition, are tested and compared. Speedups of the parallel weather model on these machines with various numbers of processors show that substantial reductions in computation time can be achieved as compared with sequential runs. [ABSTRACT FROM AUTHOR]

Workflow computing has become an essential part of many scientific and engineering fields, while workflow scheduling has long been a well-known NP-complete research problem. Major previous works can be classified into two categories: heuristic-based and guided random-search-based workflow scheduling methods. Monte Carlo Tree Search (MCTS) is a recently proposed search methodology with great success in AI research on game playing, such as Computer Go. However, researchers found that MCTS also has potential application in many other domains, including combinatorial optimization, task scheduling, planning, and so on. In this paper, we present a new workflow scheduling approach based on MCTS, which is still a rarely explored direction. Several new mechanisms are developed for the major steps in MCTS to improve workflow execution schedules further. Experimental results show that our approach outperforms previous methods significantly in terms of execution makespan. [ABSTRACT FROM AUTHOR]

Workflow scheduling has long been an important research topic in the field of parallel computing. Clustering-based methods are one of the major types of workflow scheduling approaches which have been shown superior to other kinds of methods in many cases due to their advantage of minimizing inter-task communication costs. Most previous research dealt with single workflow scheduling and focused on how to cluster the tasks within a workflow into a set of task groups. Recent research showed that utilizing idle time gaps between scheduled tasks is a promising direction for efficient multiple workflow scheduling. Since executing multiple workflows simultaneously is an inevitable need in modern shared parallel computing platforms, efficient task group allocation becomes a critical issue. In this paper, we studied such issue and proposed an innovative dual-criteria task group allocation method which considers both task group's finish time and potential resource utilization to effectively improve overall multi-workflow execution performance. In addition, an adaptive task group rearrangement mechanism was adopted to further improve performance. The proposed method has been evaluated with a series of simulation experiments and compared to previous approaches. The experimental results show that our method outperforms previous approaches across different workload conditions and workflow properties in terms of average makespan. The performance improvement ranges from 5 to 29 % for different conditions, achieving the largest performance improvement for workflows of smaller CCR. [ABSTRACT FROM AUTHOR]

In this paper, Al-doped ZnO (AZO) films are deposited on glasses substrate by RF magnetron sputtering. The optical, electrical and morphological properties of AZO films textured by wet-etching with different etchants, H 3 PO 4 , HCl, and HNO 3 are studied. It is found that the textured structure could enhance the light scattering and light trapping ability of amorphous silicon solar cells. The textured AZO film etched with HNO 3 exhibits optimized optical properties (T% ≧ 80% over entire wavelength, haze ratio > 40% at 550 nm wavelength) and excellent electrical properties (ρ = 5.86 × 10 −4 Ωcm). Scanning electron microscopy and Atomic force microscopy are used to observe surface morphology and average roughness of each textured AZO films. Finally, the textured AZO films etched by H 3 PO 4 , HCl and HNO 3 were applied to front electrode layer for p–i–n amorphous silicon solar cells. The highest conversion efficiency of amorphous silicon solar cell fabricated on HNO 3 -etched AZO film was 7.08% with open-circuit voltage, short-circuit current density and fill factor of 895 mV, 14.92 mA/cm 2 and 0.56, respectively. It shows a significantly enhancement in the short-circuit current density and conversion efficiency by 16.2% and 20.2%, respectively, compared with the solar cell fabricated on as-grown AZO film. [ABSTRACT FROM AUTHOR]

Most modern parallel programs are written with the moldable property. However, most existing parallel computing systems treat such parallel programs as rigid jobs for scheduling, resulting in two drawbacks. The first is inflexibility and inefficiency in processor allocation, leading to resource fragmentation and thus poor performance. The second is about usage inconvenience, requiring users to figure out the best number of processors for executing a job. As HPC as a service emerges, moldable job scheduling has become an important research issue for achieving both high performance and user convenience. This paper presents our research work on developing new processor allocation approaches for moldable job scheduling based on two-level resource utilization calculation, preemptive job execution, and dual-criteria iterative improvement. A series of simulation experiments have been conducted to evaluate the proposed approaches and compare them to previous methods. The experimental results demonstrate significant performance improvement in terms of average turnaround time. [ABSTRACT FROM AUTHOR]

Abstract Cloud computing and services have greatly changed the way how people develop and use software, and also raised a lot of new research issues. In this paper, we investigate two such important issues, service deployment and service request scheduling, for composite cloud services in dynamic cloud environments. We present load-aware service deployment approaches for dynamic workload and a service request scheduling method based on task ranking mechanisms. The proposed approaches can effectively improve the execution performance of composite cloud services in dynamic cloud environments. A series of simulation experiments have been conducted to evaluate the proposed approaches. The experimental results show that our approaches outperform previous methods significantly in terms of average service response time. [ABSTRACT FROM AUTHOR]

With the notion of virtual servers, peers participating in a heterogeneous, structured peer-to-peer (P2P) network may host different numbers of virtual servers, and by migrating virtual servers, peers can balance their loads proportional to their capacities. The existing and decentralized load balance algorithms designed for the heterogeneous, structured P2P networks either explicitly construct auxiliary networks to manipulate global information or implicitly demand the P2P substrates organized in a hierarchical fashion. Without relying on any auxiliary networks and independent of the geometry of the P2P substrates, we present, in this paper, a novel load balancing algorithm that is unique in that each participating peer is based on the partial knowledge of the system to estimate the probability distributions of the capacities of peers and the loads of virtual servers, resulting in imperfect knowledge of the system state. With the imperfect system state, peers can compute their expected loads and reallocate their loads in parallel. Through extensive simulations, we compare our proposal to prior load balancing algorithms. [ABSTRACT FROM AUTHOR]