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Computational Grid (CG) provides a wide distributed platform for high end compute intensive applications. Inter Process Communication (IPC) affects the performance of a scheduling algorithm drastically. Genetic Algorithms (GA), a search procedure based on the evolutionary computation, is able to solve a class of complex optimization problems. This paper proposes a GA based scheduling model observing the effect of IPC on the performance of scheduling in computational grid. The proposed model studies the effects of Inter Process Communication (IPC), processing rate () and arrival rate (). Simulation experiment, to evaluate the performance of the proposed algorithm is conducted and results reveal the effectiveness of the model.

We use a simple nonlinear scaler displacement model to calculate the distribution of effect created by a shear stress on a double stranded DNA (dsDNA) molecule and the value of shear force F c which is required to separate the two strands of a molecule at a given temperature. It is shown that for molecules of base pairs less than 21, the entire single strand moves in the direction of applied force whereas for molecules having base pairs more than 21, part of the strand moves in (2008)).

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The caveolin-1 (cav-1) protein is an integral component of caveolae and has been reported to colocalize with cholesterol and sphingomyelin-rich curved membrane domains. Here, we analyze the molecular interactions between cav-1 and sphingomyelin containing bilayers using a series of coarse-grain simulations, focusing on lipid clustering and membrane curvature. We considered a palmitoylated-cav-1 construct interacting with phospholipid/cholesterol membranes with asymmetrically distributed sphingomyelin, varying between 5 and 15% in total. We observe that cav-1 binds to the intracellular leaflet and induces a small positive curvature in the leaflet to which it is bound and an opposing negative curvature in the extracellular leaflet. Both cholesterol and sphingomyelin are observed to cluster in cav-1 bound membranes, mainly in the extracellular leaflet. Due to their negative spontaneous curvature, clustering of cholesterol and sphingomyelin facilitates membrane curvature such that the extent of either cholesterol or sphingomyelin clustering is dependent on the curvature induced. Our results suggest that cav-1 binding induces concentration-dependent curvature effects in sphingomyelin-rich membranes. Overall, our work is an important step in understanding the molecular basis of curvature and lipid clustering in cav-1 bound cellular membranes.

Computational grid is an aggregation of geographically distributed network of computing nodes specially designed for compute intensive applications. The diversity of computational grid helps in resource utilization in order to support execution of all types of jobs; fine grain as well as coarse grain. It is observed that, over the period of time in the course of job execution, grid becomes highly imbalance resulting in performance degradation. It warrants balancing the load amongst the grid nodes. In absence of centralized information in a system such as grid, load balancing becomes a complex problem. Genetic Algorithm, a search procedure based on evolutionary computation, is able to solve a class of complex optimization problems. A model based on genetic algorithm is proposed, in this work, to achieve better load balancing in computational grid. To study the performance of the proposed model, experiments have been conducted by simulating the model. Experimental results reveal the effectiveness of the proposed model.