Statistical Analysis Based Performance Evaluation of Secured Workflow Allocation Model in Cloud Environment

Abstract

In modern enterprise systems, the effectiveness of secured workflow allocation (SWA) models is crucial for balancing task management and data security. This paper comprehensively evaluates secured workflow allocation models using statistical analysis that leverages descriptive and inferential statistics to assess model performance. Our approach is demonstrated through a case study, where various scenarios are analyzed to derive insights into the trade-offs between security measures and workflow efficiency. The proposed model is evaluated through a comprehensive statistical analysis, focusing on key performance metrics including failure probability and number of task failure. Advanced statistical techniques, including descriptive analysis and hypothesis testing, are employed to analyze the trade-offs and interdependencies between security mechanisms and system performance. The results provide actionable recommendations for optimizing workflow allocation models in secure environments. This study enhances the cloud computing domain by offering a performance evaluation framework that integrates secure workflow management with system efficiency, facilitating the advancement of more robust and efficient cloud-based systems.