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At its core, distributed computing is about breaking down complex computational tasks into smaller pieces. These smaller pieces can be processed simultaneously across different machines, often in different physical locations. These machines communicate and coordinate with each other over a network. The main idea is to solve problems that might be too large or complex for a single computer to handle efficiently.
Key concepts of distributed computing include:
- Parallelization — Tasks are split into smaller subtasks. These subtasks can be processed simultaneously, significantly reducing overall processing time. For example, when rendering a CGI movie, different frames can be rendered on different machines.
- Fault Tolerance — The system continues functioning even if some components fail. If one machine goes down, others can take over its workload. Companies like Google and Amazon use this principle to maintain high availability of their services.
- Resource Sharing — Multiple computers can share resources like processing power, memory, and storage. This is fundamental to cloud computing services, where users can access vast computational resources on demand.
- Scalability — The system can grow by adding more machines to handle increased workload. This is particularly important for services that need to handle varying amounts of traffic, like social media platforms.
- Communication — Machines in a distributed system need robust protocols to coordinate their work. This involves challenges like maintaining consistency across all nodes and handling network delays or failures.
