Cloud computing uses the internet to supply dynamic services including memory, data, bandwidth and applications. Work schedules have an influence on cloud service reliability and performance. A proper provisioning method is required for a systematic resource allocation, which comprises of large virtual resources. Depending on the present state of the system, load balancing solutions can be distinguished as dynamic or static. Dynamic or static load balancing solutions can be employed to increase server response time or to raise load balancing factors for quicker and more efficient resource utilization. To decrease the load across resources and maximize CPU usage, a hybrid load balancing technique is developed. In the cloud, we have a finite quantity of resources that must be efficiently managed in order to fulfill tasks. Requests are transmitted to a cloud server, which assigns work via quadratic probing. During load balancing, the load is shifted from heavy-weighted servers to lighter-weighted servers, enhancing CPU usage. The suggested methodology's performance was assessed using average mean response time, make-span, average make-span, and average resource utilization. The Load Balancing Algorithm (LBA) is created with the primary purpose of reducing job completion time and increasing the average resource utilization ratio.
This article addresses the simulation and experiments performed on a Gorlov Helical Turbine (GHT) by altering the index of revolution of its helical blades. Gorlov Helical Turbine is a hydrokinetic turbine that generates energy from the perennial/tidal source. The paper serves a two-fold purpose: parametric optimisation of Gorlov Helical Turbine with respect to the index of revolution and viability of installing the turbines in river creeks. Nine models of turbines with a diameter of 0.600 m and a height of 0.600 m were generated with different indices of revolution and then subjected to simulation studies. A significant rise in the output torque of the turbine was not observed with the various indices of revolution, even as the probability of finding a section at every azimuthal position is likely to rise. Gavasheli's solidity ratio formula was used to formulate an expression for the output power. The output power as per analytical formulation is 1.11 W, which is of the order of output power obtained through simulation (0.951 W). The studies suggest that 0.25 remains the optimum value for the index of revolution of the helical blades. A model with 0.25 as the index of revolution was fabricated and tested at a river creek. The results were found to agree with the simulations accounting for the losses. The study results could encourage setting up hydrokinetic turbines in river creeks, thereby increasing the grid capacity of SHPs in India.
In recent years, airborne broadcasting has grown more prevalent in cities. Air quality degradation is a severe air pollution issue that exists daily. To forecast the amount of pollutants, Artificial Neural Network (ANN) and Linear Vector Quantization (LVQ) techniques were utilized. The data set dimensions are defined by the pre-processing procedure and the feature extraction mechanism. The ANN model predicts categorization concentration, allowing the LVQ model to classify direct situations with greater accuracy using explanatory factors. The ANN+LVQ model outperformed other technologies in terms of classification accuracy. The raw data was cleaned to improve the accuracy of the prediction algorithms. The pollutants discovered in the collection are NO2, NOx, O3, Benzene, Xylene, NH3, CO, SO2, PM10, NO, and Toluene. The performance of the recommendation and forecast models were tested in this study using two datasets in two distinct experiments. In urban, rural, and industrial settings, the proposed ANN model is successful in detecting air quality and predicting pollution levels. The ANN-LVQ model obtained 90% percent sensitivity, 97.59% accuracy, and 99.46% specificity with 2.43% error rate. The suggested model's accuracy is much greater than that of other current research models.
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