Determining Energy Consumption in Heterogenous Cloud Computing by Usage of RMRECFS Workflow with Cost Confinement

For large-scale workflow applications, the cloud is a promising platform. The pay-per-use model is used. In cloud data centers, reducing the energy utilization of priority-limited cost-cutting workflows limits became a hot topic. The majority of existing scheduling algorithms focus on the completion time as well as cost of a particular workflow application while working within cost limitations; These algorithms, however, fail to account for energy savings. The critical task remapping and frequency scaling RMRECFS (Re-Mapping based Reducing Energy Consumption with Frequency Scaling) method is used in this work to provide an approach for reducing energy consumption. Energy utilization reduction and key task remap are two steps of this technique. The cost budget, critical work path, and expandable budget factor are all included to determine the adjustable cost budget and additional costs in the first stage. To execute a preliminary task and task mapping virtual machines when adhering to configurable budget restrictions, most workflow tasks are then distributed to VMs that spend some least amount of power. By reallocating key jobs and applying frequency scaling to virtual machines based on spare costs, the second phase minimizes power consumption owing to task relocation. Experiment with two different types of workflow applications at various ranges show that the provided RMRECFS algorithm efficiently saves energy usage while staying within budget limitations when compared to the conventional RMREC algorithm