Workload Prediction Based Virtual Machine Migration and Optimal Switching Strategy for Cloud Power Management

Virtual Machine (VM) Migration has been popular nowadays, as it helps to balance the load effectively. Various VM migration-based approaches are modeled for better VM placement but remain the challenge because of inappropriate load balancing. Thus, workload prediction-based VM migration is introduced to improve the energy efficiency of the system. Importantly, load prediction is very important to enhance resource allocation and utilization. Chaotic Fruitfly Rider Neural Network is devised by combining Rider neural network and chaotic Fruitfly optimization algorithm to predict load. Moreover, the fitness for predicting the load is based on old-time load, resource constraint, and network parameters. Once the load is predicted, the power optimization is performed using VM migration and optimal switching strategy. When the load is found overloaded, the VM migration is performed using the proposed Harris Hawks spider monkey optimization (HHSMO). Thus, the optimal finding of VM for executing the removed task is found out using the proposed HHSMO. The fitness function utilized for the VM migration is based on power, load, and resource parameter. If the load predicted is underloaded, the optimal switch ON/OFF is done optimally by switch ON/OFF the servers using the proposed HHSMO algorithm. Through the migration and switching strategy, the power consumption is optimized. The performance of the proposed model is evaluated in terms of power consumption, load, and resource utilization. The proposed HHSMO achieves the minimal power consumption of 0.0181, the minimal load of 0.002, and the minimal resource utilization of 0.0376.