A Real Time Optimizing Mobile Operating System: An Interactive Context: Context-Aware Intelligence for Adaptive and Real-Time Systems

Predicting the execution time of workflow tasks in a cloud environment is vital for optimizing resource utilization and meeting service level agreements (SLAs). In this study, we propose a Two-Stage Machine Learning Approach for Workflow Task Execution Time Prediction. In the first stage, we employ a task classifica-tion model to categorize tasks into groups based on their expected execution times (e.g., short, medium, long). The proposed two-stage approach aims to enhance prediction accuracy and interpretability by leveraging both classification and re-gression techniques. We evaluate the performance of the model using historical data, considering metrics such as accuracy, Mean Absolute Error, and Root Mean Squared Error. Finally, we discuss the deployment and monitoring of the model in a cloud environment, emphasizing its adaptability to evolving infrastructure and workload patterns. Many methods, including resource provisioning and scheduling, depend on predict-ing the workflow activities' performance for changing input data. On the other hand, producing such estimates in the cloud is challenging.