Cloud computing and virtualisation are recent approaches to develop minimum energy usage in virtualised cloud data centre (DC) for resource management. One of the major problems faced by cloud DCs is energy consumption which increases the cost of cloud user and environmental influence. Therefore, virtual machine (VM) consolidation is properly proposed in many approaches which reallocate the VMs by VM migration with the objective of minimum energy consumption. Here, VM consolidation based on the Fruit fly Hybridised Cuckoo Search (FHCS) algorithm is proposed to obtain the optimal solution with the help of two objective functions in cloud DC. This FHCS approach efficiently minimises the energy usage and resource depletion in cloud DC. The proposed work comparison is done with Ant Colony System (ACS), Particle Swarm Optimisation (PSO) algorithm and Genetic Algorithm (GA). The simulation conclusion reveals the advantage of the FHCS and VM migration method over existing procedures such as GA, PSO and ACS in terms of energy consumption and resource utilisation. The proposed method achieves 68 Kwh less energy and 72% less resources than existing methods. Simulation results have shown that energy consumption of the proposed method is reduced with less number of active PMs than other conventional approaches.

Scouring around a bridge pier involves removing sediment from the riverbed and banks due to water flow. This paper employs eXtreme Gradient Boosting (XGBoost) and support vector machine with particle swarm optimization (SVM-PSO) machine learning (ML) approaches to model the temporal local scour depth around bridge piers under clear water scouring (CWS) conditions. CWS datasets, incorporating bridge pier geometry, flow characteristics, and sediment properties, are collected from existing literature. Five non-dimensional influencing parameters, such as ratio of pier width to flow depth (b/y), ratio of approach mean velocity to critical velocity (V/Vc), ratio of mean sediment size to pier width (d50/b), Froude number (Fr), and standard deviation of sediment (σg), are chosen as input parameters. XGBoost and SVM-PSO ML models demonstrate superior predictive capabilities, achieving coefficient of determination (R2) values exceeding 0.90 and mean absolute percentage error (MAPE) and root mean square error (RMSE) values less than 17.07% and 0.0341, respectively. Comparison with the previous four empirical models based on statistical indices reveals that the proposed XGBoost model outperforms SVM-PSO and empirical models in predicting scour depth, so it is recommended for estimating clear water scour depth under varying temporal conditions within the specified dataset range.