Distributed processing is a computing technique in which multiple computers (or nodes) work together to process tasks simultaneously. This approach enhances performance, fault tolerance, and scalability by distributing workloads across multiple machines instead of relying on a single system.
Key Concepts in Distributed Processing:
- Parallel Execution: Tasks are divided into smaller subtasks and processed concurrently across multiple systems.
- Resource Sharing: Utilizes a network of computers to share CPU, memory, and storage resources.
- Scalability: Easily handles increased workloads by adding more nodes to the system.
- Fault Tolerance: Ensures system reliability by redistributing tasks if a node fails.
- Data Distribution: Spreads data across multiple systems to optimize access and processing speed.
Examples of Distributed Processing:
- Cloud Computing Services (AWS, Google Cloud, Azure)
- Big Data Processing (Hadoop, Apache Spark)
- Blockchain Networks
- Scientific Computing
- Teacher: Shesh Mani Tiwari