An online data access prediction and optimization approach for distributed systems

Current scientific applications have been producing large amounts of data. The processing, handling and analysis of such data require large-scale computing infrastructures such as clusters and grids. In this area, studies aim at improving the performance of data-intensive applications by optimizing data accesses. In order to achieve this goal, distributed storage systems have been considering techniques of data replication, migration, distribution, and access parallelism. However, the main drawback of those studies is that they do not take into account application behavior to perform data access optimization. This limitation motivated this paper which applies strategies to support the online prediction of application behavior in order to optimize data access operations on distributed systems, without requiring any information on past executions. In order to accomplish such a goal, this approach organizes application behaviors as time series and, then, analyzes and classifies those series according to their properties. By knowing properties, the approach selects modeling techniques to represent series and perform predictions, which are, later on, used to optimize data access operations. This new approach was implemented and evaluated using the OptorSim simulator, sponsored by the LHC-CERN project and widely employed by the scientific community. Experiments confirm this new approach reduces application execution time in about 50 percent, specially when handling large amounts of data.

Metal biosorption onto dry biomass of Arthrospira (Spirulina) platensis and Chlorella vulgaris: Multi-metal systems

Binary and ternary systems of Ni2+, Zn2+, and Pb2+ were investigated at initial metal concentrations of 0.5, 1.0 and 2.0 mM as competitive adsorbates using Arthrospira platensis and Chlorella vulgaris as biosorbents. The experimental results were evaluated in terms of equilibrium sorption capacity and metal removal efficiency and fitted to the multi-component Langmuir and Freundlich isotherms. The pseudo second order model of Ho and McKay described well the adsorption kinetics, and the FT-IR spectroscopy confirmed metal binding to both biomasses. Ni2+ and Zn2+ interference on Pb2+ sorption was lower than the contrary, likely due to biosorbent preference to Pb. In general, the higher the total initial metal concentration, the lower the adsorption capacity. The results of this study demonstrated that dry biomass of C. vulgaris behaved as better biosorbent than A. platensis and suggest its use as an effective alternative sorbent for metal removal from wastewater. (C) 2012 Elsevier B.V. All rights reserved.

Effects of early compost application on no-till organic soybean

Weed control has always been an important issue in agriculture. With the advent of no-till systems, soil erosion was reduced but herbicide use was increased. Organic no-till systems try to adjust reduced erosion to the no use of herbicides. Nevertheless, this adjustment is limited by the cost of mechanical weed control. This cost may be reduced by improved cultural weed control with cover crops mulches. In this paper we report a study on the application of compost manure on an oats winter cover crop, preceding soybean, instead of on the soybean summer crop. Treatments comprised a control without compost manure, and compost manure doses of 4 and 8 Mg ha-1 applied either on oats in winter or soybean in summer, organized in a randomized block design, with five replications. In summer, plots were split into weed-controlled or not controlled subplots. The timing of application and the manure doses did not affect the oats biomass or the soybean performance. However, in summer, without water stress, the application of manure at 8 Mg ha-1 directly on soybean has reduced weed biomass in this crop.