Efficient algorithm for error optimization and resource prediction to mitigate cost and energy consumption in a cloud environment

As cloud computing continues to grow, the energy consumption of cloud-edge resources has become a concern, particularly in terms of cost of energy and environmental effects. Therefore, reducing energy consumption in cloud-edge environments is an important issue that needs to be addressed to ensure sustainable and cost-effective cloud services. The existing approaches face challenges in achieving optimized energy consumption and workflow execution delay while maintaining reliability. Therefore, there is a need for a novel approach that can address these challenges and provide an effective solution for managing scientific workflows in a hybrid cloud environment. This paper introduces Resource Prediction and Scheduling Error Optimization (RPSEO), a novel approach for optimizing energy consumption and workflow execution delay in cloud-edge environments. The proposed method leverages a task-ordering web server management system and a soft-computing-based searching algorithm. Evaluation of Epigenomics and SIPHT workflows demonstrates significant improvements, surpassing existing methods Reliability-Aware Cost-Efficient Scientific (RACES), Delay Aware and Performance Efficient Energy Optimization (DAPPEO), and Reliable and Efficient Webserver Management (REWM) with better average energy consumption performance (up to 43.92% and 35.93% for Epigenomics and SIPHT) and cost efficiency (up to 44.53% and 73.50% for Epigenomics and SIPHT). RPSEO emerges as a promising solution for reliable and efficient scientific workflow management in hybrid cloud settings.