Published Paper Details:

Task scheduling is the pillar of successful cloud computing systems. With increasing system complexity and the reliance on data, intelligent, adaptive task management becomes ever more in demand. This project proposes an error-level analysis-based, automated task rescheduling system incorporating a sequential model, and integrating it with cloud databases through MongoDB. The system seeks to effectively reallocate tasks due to delays in execution, failure, or abnormalities so that optimal use of resources and staff productivity is maintained. An intuitive interface based on Streamlit facilitates real-time task allocation, tracking, and handling. The solution solves static scheduling challenges by dynamically adjusting according to task condition variations and employee working hours. The project further incorporates a gamified experience point system that motivates timely task completion and monitors employee progress. The system's architecture, implementation, and implications are presented in this paper, which demonstrates its relevance to organizations pursuing operational resilience and efficiency.