Efficient Hybrid Genetic Based Multi Dimensional Host Load Aware Algorithm for Scheduling and Optimization of Virtual Machines

Mapping the virtual machines to the physical machines cluster is called the VM placement. Placing the VM in the appropriate host is necessary for ensuring the effective resource utilization and minimizing the datacenter cost as well as power. Here we present an efficient hybrid genetic based host load aware algorithm for scheduling and optimization of virtual machines in a cluster of Physical hosts. We developed the algorithm based on two different methods, first initial VM packing is done by checking the load of the physical host and the user constraints of the VMs. Second optimization of placed VMs is done by using a hybrid genetic algorithm based on fitness function. Our simulation results show that the proposed algorithm outperforms existing methods and enhances the rate of resource utilization through accommodating more number of virtual machines in a physical hos

Efficient Hybrid Genetic Based Multi Dimensional Host Load Aware Algorithm for Scheduling and Optimization of Virtual Machines

Mapping the virtual machines to the physical machines cluster is called the VM placement. Placing the VM in the appropriate host is necessary for ensuring the effective resource utilization and minimizing the datacenter cost as well as power. Here we present an efficient hybrid genetic based host load aware algorithm for scheduling and optimization of virtual machines in a cluster of Physical hosts. We developed the algorithm based on two different methods, first initial VM packing is done by checking the load of the physical host and the user constraints of the VMs. Second optimization of placed VMs is done by using a hybrid genetic algorithm based on fitness function. Our simulation results show that the proposed algorithm outperforms existing methods and enhances the rate of resource utilization through accommodating more number of virtual machines in a physical hos

A BERT based Ensemble Approach for Sentiment Classification of Customer Reviews and its Application to Nudge Marketing in e-Commerce

According to the literature, Product reviews are an important source of information for customers to support their buying decision. Product reviews improve customer trust and loyalty. Reviews help customers in understanding what other customers think about a particular product and helps in driving purchase decisions. Therefore, for an e-commerce platform it is important to understand the sentiments in customer reviews to understand their products and services, and it also allows them to potentially create positive consumer interaction as well as long lasting relationships. Reviews also provide innovative ways to market the products for an ecommerce company. One such approach is Nudge Marketing. Nudge marketing is a subtle way for an ecommerce company to help their customers make better decisions without hesitation.Comment: Submitted to a Journal for revie

One-step microalgal biodiesel production from Chlorella pyrenoidosa using subcritical methanol extraction (SCM) technology

In this work, we propose a one-step subcritical methanol extraction (SCM) process for biodiesel production from Chlorella pyrenoidosa. Therefore, the present study attempts to establish and determine the optimum operating conditions for maximum biodiesel yield from SCM of C. pyrenoidosa. A statistical approach, i.e. response surface methodology is employed in this study. The effects of three operational factors: reaction temperature (140–220 °C), reaction time (1–15 min) and methanol to algae ratio (1–9 wt.%) were investigated using a central composite design. A maximum yield of crude biodiesel of 7.1 wt.% was obtained at 160 °C, 3 min reaction time and 7 wt.% methanol to algae ratio. The analysis of variance revealed that methanol to algae ratio is the most significant factor for maximizing biodiesel yield. Regression analysis showed a good fit of the experimental data to the second-order polynomial model. With no requirement of catalyst nor any pretreatment step, SCM process is economically feasible to scale up the commercial biodiesel production from algae

The Existence and Localization of Nuclear snoRNAs in Arabidopsis thaliana Revisited

Ribosome biogenesis is one cell function-defining process. It depends on efficient transcription of rDNAs in the nucleolus as well as on the cytosolic synthesis of ribosomal proteins. For newly transcribed rRNA modification and ribosomal protein assembly, so-called small nucleolar RNAs (snoRNAs) and ribosome biogenesis factors (RBFs) are required. For both, an inventory was established for model systems like yeast and humans. For plants, many assignments are based on predictions. Here, RNA deep sequencing after nuclei enrichment was combined with single molecule species detection by northern blot and in vivo fluorescence in situ hybridization (FISH)-based localization studies. In addition, the occurrence and abundance of selected snoRNAs in different tissues were determined. These approaches confirm the presence of most of the database-deposited snoRNAs in cell cultures, but some of them are localized in the cytosol rather than in the nucleus. Further, for the explored snoRNA examples, differences in their abundance in different tissues were observed, suggesting a tissue-specific function of some snoRNAs. Thus, based on prediction and experimental confirmation, many plant snoRNAs can be proposed, while it cannot be excluded that some of the proposed snoRNAs perform alternative functions than are involved in rRNA modificatio

Enhancement of Power Quality of Single Stage Grid Connected PV System by Using Takagi-Sugeno-Kang Fuzzy Controllers

Grid connected solar power plants are widely established in many places worldwide. Photovoltaic (PV) based grid connected solar plants are attracting recently due to improved in controlling of power converters. Single stage grid connected systems can reduce number of converters connected in power plants which resultant in reduce cost of the system. However, DC to DC converters are generally used in PV systems to enhance the operation of maximum power point for best utilization. The inverters also can be using to extract maximum power from PV systems through new controlling techniques in power electronics devises. Therefore an extraDC to DC converter is not required to make PV at its maximum power point condition. However, this technology can be used for small scale solar power plants since all PV arrays in solar power plant cannot be received same irradiance. Takagi-Sugeno-Kang (TSK) fuzzy controlleris having significant priority than proportional plus integral controllers when rapid changes are having in input. Hence, TSK based single stage controller is developed in this paper for grid connected 1MW solar plant. Generally distribution system is connected with unbalanced loads, hence these unbalanced loads will create forcefully unbalanced currents in electrical grid. Unbalanced grid currents further create many problems to other loads. Therefore, the proposed controller is designed to help making grid currents balanced during unbalanced local loads. Further, the inverter can compensate reactive power demanded by local loads to minimize reactive power supplied by grid. Extensive results are presented and evaluated through hardware-in-loop on the platform of OPAL-RT to enhance the performance of proposed controller for 1MW grid connected solar plant

Statistical Model of Shape Moments with Active Contour Evolution for Shape Detection and Segmentation

This paper describes a novel method for shape representation and robust image segmentation. The proposed method combines two well known methodologies, namely, statistical shape models and active contours implemented in level set framework. The shape detection is achieved by maximizing a posterior function that consists of a prior shape probability model and image likelihood function conditioned on shapes. The statistical shape model is built as a result of a learning process based on nonparametric probability estimation in a PCA reduced feature space formed by the Legendre moments of training silhouette images. A greedy strategy is applied to optimize the proposed cost function by iteratively evolving an implicit active contour in the image space and subsequent constrained optimization of the evolved shape in the reduced shape feature space. Experimental results presented in the paper demonstrate that the proposed method, contrary to many other active contour segmentation methods, is highly resilient to severe random and structural noise that could be present in the data