Distributed computing

Definition

Distributed computing is a model in which computing tasks are divided among multiple interconnected computers that work together to accomplish a common goal. This approach allows for increased processing power and efficiency by leveraging the combined resources of several machines, making it particularly useful for solving complex problems that require significant computational effort.

5 Must Know Facts For Your Next Test

1. Distributed computing can improve fault tolerance, as the failure of one machine does not necessarily compromise the entire system.
2. This model supports scalability, enabling systems to handle larger workloads by adding more machines without major changes to the architecture.
3. Distributed computing is often used in applications such as scientific simulations, data analysis, and large-scale web services.
4. Communication between machines in a distributed system is critical and typically relies on protocols to ensure data consistency and integrity.
5. Security challenges are significant in distributed computing, as data must be protected during transmission and while stored across multiple nodes.

Review Questions

• How does distributed computing enhance efficiency in processing large-scale problems?
  • Distributed computing enhances efficiency by breaking down large-scale problems into smaller tasks that can be processed simultaneously by multiple interconnected computers. This parallel processing allows for faster computation and the ability to handle more complex problems than a single machine could manage alone. By utilizing the combined resources of several machines, the overall workload is distributed, significantly reducing execution time.
• Discuss the importance of communication protocols in ensuring data integrity within a distributed computing system.
  • Communication protocols are vital in distributed computing systems because they govern how data is transmitted between machines. These protocols ensure that data is delivered accurately and consistently across the network, maintaining integrity during transfer. Without robust communication protocols, data could become corrupted or lost, leading to errors in computation and undermining the reliability of the distributed system.
• Evaluate the implications of scalability and fault tolerance in distributed computing on real-world applications.
  • Scalability and fault tolerance have significant implications for real-world applications using distributed computing. Scalability allows systems to grow seamlessly by adding additional resources as needed, accommodating increasing workloads without major redesigns. Fault tolerance ensures that even if one or more components fail, the system can continue to operate effectively. Together, these features enhance the reliability and flexibility of applications like cloud services or scientific research platforms, making them capable of handling dynamic demands while minimizing downtime.

Related terms

Parallel Computing: A type of computation where many calculations are carried out simultaneously, often utilizing multiple processors to solve a problem more quickly.

Cloud Computing: The delivery of various computing services over the internet, allowing users to access resources like storage and processing power without owning physical infrastructure.

Task Scheduling: The process of assigning tasks to different machines in a distributed system to optimize resource utilization and minimize overall execution time.