Enhanced Dynamic Hierarchical Replication and Weighted Scheduling Strategy in Data Grid

The Data Grid provides massive aggregated computing resources and distributed storage space to deal with data-intensive applications. Due to the limitation of available resources in the grid as well as production of large volumes of data, efficient use of the Grid resources becomes an important challenge. Data replication is a key optimization technique for reducing access latency and managing large data by storing data in a wise manner. Effective scheduling in the Grid can reduce the amount of data transferred among nodes by submitting a job to a node where most of the requested data files are available. In this paper two strategies are proposed, first a novel job scheduling strategy called Weighted Scheduling Strategy (WSS) that uses hierarchical scheduling to reduce the search time for an appropriate computing node. It considers the number of jobs waiting in a queue, the location of the required data for the job and the computing capacity of the sites Second, a dynamic data replication strategy, called Enhanced Dynamic Hierarchical Replication (EDHR) that improves file access time. This strategy is an enhanced version of the Dynamic Hierarchical Replication strategy. It uses an economic model for file deletion when there is not enough space for the replica. The economic model is based on the future value of a data file. Best replica placement plays an important role for obtaining maximum benefit from replication as well as reducing storage cost and mean job execution time. So, it is considered in this paper. The proposed strategies are implemented by OptorSim, the European Data Grid simulator. Experiment results show that the proposed strategies achieve better performance by minimizing the data access time and avoiding unnecessary replication.     

Predictive control of drying process using an adaptive neuro-fuzzy and partial least squares approach

In this paper, adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and partial least squares (PLS) approaches are applied to predictive control of a drying process. In the proposed approaches, the PLS analysis is used to pre-process actual data and to provide the necessary background to apply ANN and ANFIS approaches. A reasonable section of this study is assigned to the modeling with the aim at predicting the granule particle size and executing by ANFIS and ANN. ANN holds the promise of being capable of producing non-linear models, being able to work under noise conditions, and being fault tolerant to the loss of neurons or connections. Also, the ANFIS approach combines the advantages of fuzzy system and artificial neural network to design architecture and is capable of dealing with both limitation and complexity in the data set. The efficiencies of ANFIS and ANN approaches in prediction are compared and the superior approach is selected. Finally, by deploying the preferred approach, several scenarios are presented to be used in predictive control of spray drying as an accurate, fast running, and inexpensive tool. This is the first study that presents a flexible intelligent approach for predictive control of drying process by ANN, ANFIS, and PLS. The approach of this study may be easily applied to other production process.     

Combination of data replication and scheduling algorithm for improving data availability in Data Grids

Data Grid is a geographically distributed environment that deals with large-scale data-intensive applications. Effective scheduling in Grid can reduce the amount of data transferred among nodes by submitting a job to a node, where most of the requested data files are available. Data replication is another key optimization technique for reducing access latency and managing large data by storing data in a wisely manner. In this paper two algorithms are proposed, first a novel job scheduling algorithm called Combined Scheduling Strategy (CSS) that uses hierarchical scheduling to reduce the search time for an appropriate computing node. It considers the number of jobs waiting in queue, the location of required data for the job and the computing capacity of sites. Second a dynamic data replication strategy, called the Modified Dynamic Hierarchical Replication Algorithm (MDHRA) that improves file access time. This strategy is an enhanced version of Dynamic Hierarchical Replication (DHR) strategy. Data replication should be used wisely because the storage capacity of each Grid site is limited. Thus, it is important to design an effective strategy for the replication replacement. MDHRA replaces replicas based on the last time the replica was requested, number of access, and size of replica. It selects the best replica location from among the many replicas based on response time that can be determined by considering the data transfer time, the storage access latency, the replica requests that waiting in the storage queue and the distance between nodes. The simulation results demonstrate the proposed replication and scheduling strategies give better performance compared to the other algorithms.     

Network and data location aware approach for simultaneous job scheduling and data replication in large-scale data grid environments

Data Grid integrates graphically distributed resources for solving data intensive scientific applications. Effective scheduling in Grid can reduce the amount of data transferred among nodes by submitting a job to a node, where most of the requested data files are available. Scheduling is a traditional problem in parallel and distributed system. However, due to special issues and goals of Grid, traditional approach is not effective in this environment any more. Therefore, it is necessary to propose methods specialized for this kind of parallel and distributed system. Another solution is to use a data replication strategy to create multiple copies of files and store them in convenient locations to shorten file access times. To utilize the above two concepts, in this paper we develop a job scheduling policy, called hierarchical job scheduling strategy （HJSS）, and a dynamic data replication strategy, called advanced dynamic hierarchical replication strategy （ADHRS）, to improve the data access efficiencies in a hierarchical Data Grid. HJSS uses hierarchical scheduling to reduce the search time for an appropriate computing node. It considers network characteristics, number of jobs waiting in queue, file locations, and disk read speed of storage drive at data sources. Moreover, due to the limited storage capacity, a good replica replacement algorithm is needed. We present a novel replacement strategy which deletes files in two steps when free space is not enough for the new replica： first, it deletes those files with minimum time for transferring. Second, if space is still insufficient then it considers the last time the replica was requested, number of access, size of replica and file transfer time. The simulation results show that our proposed algorithm has better performance in comparison with other algorithms in terms of job execution time, number of intercommunications, number of replications, hit ratio, computing resource usage and storage usage.     

Hierarchical data replication strategy to improve performance in cloud computing

Cloud computing environment is getting more interesting as a new trend of data management. Data replication has been widely applied to improve data access in distributed systems such as Grid and Cloud. However, due to the finite storage capacity of each site, copies that are useful for future jobs can be wastefully deleted and replaced with less valuable ones. Therefore, it is considerable to have appropriate replication strategy that can dynamically store the replicas while satisfying quality of service (QoS) requirements and storage capacity constraints. In this paper, we present a dynamic replication algorithm, named hierarchical data replication strategy (HDRS). HDRS consists of the replica creation that can adaptively increase replicas based on exponential growth or decay rate, the replica placement according to the access load and labeling technique, and finally the replica replacement based on the value of file in the future. We evaluate different dynamic data replication methods using CloudSim simulation. Experiments demonstrate that HDRS can reduce response time and bandwidth usage compared with other algorithms. It means that the HDRS can determine a popular file and replicates it to the best site. This method avoids useless replications and decreases access latency by balancing the load of sites.     

Characterization of the produced electrospun fish gelatin nanofiber containing fucoxanthin

Food Science and Biotechnology - This study aims to prepare fish gelatin nanofibers extracted from fish waste by using electrospinning method and its encapsulation with fucoxanthin extracted from...     

Enhanced electron collection efficiency of nanostructured dye‐sensitized solar cells by incorporating TiO2 cubes

Herein, enhancement of dye‐sensitized solar cell (DSC) performance is reported by combining the merits of the dye loading of TiO₂ nanoparticles and light scattering, straight carrier transport path, and efficient electron collection efficiency of TiO₂ cubes. We fabricate DSC devices with various arrangement styles and compositions of the electrodes in the forms of monolayer and double layer films. For this purpose, the solvothermal synthesized TiO₂ cubic particles (100‐600 nm) are employed as the scattering layer, whereas TiO₂ nanoparticles (15‐30 nm) synthesized via a combination of solvothermal and sol‐gel routes are used as the active layer of devices. We improve the photovoltaic characteristics of DSCs by two mechanisms. First, the light harvesting of DSC devices made of nanoparticles is improved by controlling the thickness of monolayer films, reaching the highest efficiency of 7.0%. Second, the light scattering and electron collection efficiency are enhanced by controlling the composition of double layer films composed of mixtures of TiO₂ nanoparticles and cubes, obtaining the maximum efficiency of 8.21%. The enhancements are attributed to balance between charge transfer resistance and charge recombination of photo‐generated electrons as well as dye loading and light scattering.     

Effect of Silica Nanoparticles on the Performance of Polysulfone Membranes for Olefin‐Paraffin Separation

The separations of ethylene/ethane and propylene/propane using polysulfone‐silica nanocomposite membranes were studied. Silica nanoparticles were prepared via sol‐gel method and the membranes by phase inversion. Characterization by Fourier transform spectroscopy and scanning electron microscopy indicated a good distribution of silica nanoparticles in the polymer matrix and also a good compatibility between the two phases. The performances of the prepared membranes in ethylene‐ethane and propylene‐propane separation were evaluated. The results showed the increments in gas permeability and selectivity by silica. Higher silica contents increased the solubility coefficient and reduced the diffusion coefficient of gases. The plasticization pressure of polysulfone was increased by incorporating the silica nanoparticles in polymer.     

A novel route to prepare hydrophobic and durable antireflective hybrid silica coating by sol-gel method

The application of antireflective coatings to the glass covers of solar thermal collectors, allows increasing the efficiency of the whole system. The work presented here describes the room temperature synthesis of highly transparent and hydrophobic silica coatings using tetraethylorthosilicate (TEOS) as a precursor and polydimethylsiloxane (PDMS) as a hydrophobic modifying agent via a simple dip coating technique. Then, the films were characterized by measuring contact angle and optical transparency, Fourier transform infrared (FTIR) spectroscopy, Field emission scanning electron microscopy (FESEM) and atomic force microscopy (AFM) measurements. A new two-step method by double layer of acidic and basic sols was employed. Results showed the maximum transmittance of the antireflective solar glass with double layer coating is about 95.02% at 565 nm wavelength, which is about 4% higher than the substrate glass. The durability of the materials used in solar systems is a key point since they should keep their initial properties during the operational lifetime. In this work, the stability of the optical properties of the films after one year at room temperature in an environment has been achieved, thanks to the application of a hydrophobic treatment and two step-catalyzed sols to the coating. The addition of PDMS to the silica sols improved the hydrophobicity of the coating, and prevented to some extent the coating from cracking which occurred in a pure inorganic thick antireflective coating. It was observed that the obtained silica films become hydrophobic with the introduction of the hydrophobic organic group and static water contact angle (97°) was obtained for the silica film prepared with double layer of acidic/basic coating.