Exploring Virtual Machine Estimation Models for Workload Management in Cloud Computing

Increased workload prediction accuracy in a cloud environment may result in processor and resource usage optimization, lowering the overall cost of using cloud services. Seasonal fluctuations in data are present in the broad and dynamic cloud ecosystem. It is possible to hybridize mathematical and machine learning models to optimize prediction accuracy. Cloud workload prediction can be improved at any moment by more accurate virtual machine estimation, which takes into account the dynamic requirements of the cloud's infrastructure. The various methods for VMs estimation are examined in this work. Every strategy will be ranked for each aspect that is most important and has to be optimized. In order to gain a better understanding of the benefits and drawbacks of each strategy. This will help to provide suggestions for the selection of the best approach for a certain cloud environment scenario. The methods that may be hybridized to improve workload prediction for the stable cloud environment are going to be highlighted in this paper. It will outline potential areas for future research in this area of computing.