The objective of this project is to design and develop a cloud-based distributed data processing system using parallel computing techniques. The system enables efficient processing of large datasets across multiple nodes, helping students understand distributed computing, task scheduling, and performance optimization in cloud environments.
Study cloud computing architecture, distributed systems, and parallel computing fundamentals.
Analyze data processing challenges in large-scale cloud environments.
Prepare Software Requirement Specification (SRS) and system architecture documentation.
Design system architecture including distributed nodes, task scheduler, and data aggregation module.
Create database schema or data storage structure for datasets, node information, job logs, and processing results.
Implement secure user authentication and role-based access control for submitting tasks.
Develop data partitioning and task distribution modules to assign workloads to multiple nodes.
Implement parallel processing logic using threads, processes, or distributed computing frameworks (e.g., Hadoop MapReduce or Spark simulation for MCA).
Monitor node performance and task completion status.
Aggregate processed results from all nodes for final output.
Implement fault-tolerance mechanisms to handle node failures and retries.
Maintain audit logs for job submissions, node activity, and processing results.
Develop a dashboard to visualize processing progress, node status, and task metrics.
Perform unit testing, integration testing, and performance evaluation of parallel processing.
Simulate large datasets to analyze system efficiency and scalability.
Prepare documentation including ER diagrams, architecture diagrams, processing workflow, and test cases.
Deploy system on local cloud simulation or multiple virtual machines for demonstration.
