Virtual Machine Allocation Using Optimal Resource Management Approach

Resources are offered to customers on demand in the modern era of computing, communication, and technology. User demand for the resources depends on the service provider and consumer. The optimal assignment of the cloud resources depends on fitness function and resource management technique. In this manuscript, the key focus is to propose a model based on a meta-heuristic evaluation technique. The meta-heuristic evaluation technique provides optimal placement of the virtual machines to the user requests across the globe. The presented framework, elephant heard optimization with neural network (EHO-ANN) outperforms the existing static, dynamic, and nature-inspired techniques. The EHO-ANN is evaluated and analyzed against the Harmony Search Approach, Elephant Heard Optimizer, BAT, and GA cost-aware approach. The evaluation and analysis include the performance metrics, average Execution Time (ms), average Start Time (ms), average utilization, and average Finish Time (ms). The presented model EHO-ANN is validated using two configuration scenarios with 10 virtual machines and 5 virtual machines. The results are generated by fifteen times repeated experimentation which assures the accuracy of the model.