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In cloud computing, virtual machine (VM) consolidation and migration are significant schemes to enhance the energy efficiency and costs of the cloud environment. The process of VM consolidation is to execute a more number of tasks with less power consumption. However, due to unreliable physical resources, energy consumption is increased. So, to solve this issue, improved beetle swarm optimization (IBSO) algorithm based on energy-aware VM consolidation is presented in this article. IBSO algorithm joins together beetle swarm optimization (BSO) and particle swarm optimization (PSO). The proposed algorithm is defined with the effective representation of solution and multi-objective functions such as power consumption, resource wastage, and SLA violation. Results of the work are depicted that the proposed approach outperformed different previous methods in terms of different evaluation measures.

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In this paper, we present a hybrid model to perform the training and testing of prediction model with online streaming data. Prediction of online streaming data is a time critical task. Huge volume of data that is being generated online need to be ingested to a prediction model and to be used to train and test the prediction model dynamically which improves the learning rate. The existing approaches for dynamic training and testing use the local infrastructure or virtual machines from the cloud infrastructure to increase the learning rate of the prediction model with streaming data. Recently many applications prefer serverless cloud infrastructure than virtual machines. However, using the serverless infrastructure for the entire prediction process will have time and space tradeoffs due to its autonomic feature. Hence in this paper we propose a hybrid approach that uses the three different environments such as the local infrastructure, virtual machine and serverless cloud for different stages. A novel approach to select the suitable environment to train and test the LSTM based air quality prediction model with stream data is proposed with increased learning rate and reduced resource utilization.