Distributed Computing
In computer science, distributed computing is a method of making multiple computers work together to solve a common problem.It makes a computer network appear like a single powerful computer, providing massive resources to tackle complex challenges.This research field mainly studies how distributed systems perform calculations.A distributed system is a group of computers that are connected to each other through a network to pass messages and communicate and coordinate their behavior. Components interact with each other to achieve a common goal. It is a science that divides engineering data that requires a lot of calculations into small pieces, calculates them separately by multiple computers, and then uploads the calculation results, unifies and merges the results to draw data conclusions.
Advantages of distributed computing
Distributed systems have many advantages over single-system computing. Here are some examples:
- Scalability:Distributed systems can grow with workloads and demands, and new nodes, or more computing devices, can be added to the distributed computing network as needed.
- Availability:A distributed computing system will not crash because one of the computers fails. The design exhibits fault tolerance because it can continue to operate even if individual computers fail.
- consistency:Computers in a distributed system share information and replicate data between them, but the system automatically manages data consistency between all different computers. Therefore, you can enjoy the advantages of fault tolerance without affecting data consistency.
- transparency:Distributed computing systems provide a logical separation between users and physical devices. You can interact with the system as if it were a single computer without having to worry about the setup and configuration of individual machines. You can have different hardware, middleware, software, and operating systems that work together to make the system run smoothly.
- efficiency:Distributed systems provide faster performance through optimal resource usage of the underlying hardware. As a result, you can manage any workload without worrying about system failures due to capacity surges or underutilization of expensive hardware.
Distributed computing use cases
Distributed computing is very common today. Mobile and web applications are examples of distributed computing, where multiple machines work together in the backend to provide the correct information to the application. When distributed systems scale vertically, they can handle more complex challenges. Here are some use cases for distributed computing:
1. Healthcare and Life Sciences
Healthcare and life sciences use distributed computing to model and simulate complex life science data. Image analysis, medical drug research, and gene structure analysis are all accelerated through distributed systems. Here are some examples:
- Accelerate structure-based drug design by visualizing molecular models in three dimensions;
- reducing genomic data processing time to enable early detection of cancer, cystic fibrosis, and Alzheimer’s disease;
- Develop intelligent systems to help doctors diagnose patients by processing large volumes of complex images such as MRIs, X-rays, and CT scans.
2. Engineering research
Engineers can simulate complex physics and mechanics concepts on distributed systems. They use this research to improve product designs, build complex structures, and design faster vehicles. Here are some examples:
- Computational fluid dynamics studies the behavior of liquids and implements these concepts in aircraft design and racing cars.
- Computer-aided engineering requires computationally intensive simulation tools to test new plant engineering, electronics, and consumer products.
3. Financial services
Financial services companies use distributed systems to perform high-speed economic simulations to assess portfolio risk, predict market movements, and support financial decision making. They can create web applications that use the power of distributed systems to do the following:
- Providing low-cost, personalized premiums
- Securely support high-volume financial transactions using a distributed database.
- Authenticate users and protect customers from fraud
4. Energy and Environment
Energy companies need to analyze large amounts of data to improve operations and transition to sustainable and climate-friendly solutions. They use distributed systems to analyze large streams of data from vast networks of sensors and other smart devices. Here are some of the tasks they might perform:
- Streaming and integrating seismic data for structural design of power plants
- Real-time well monitoring for proactive risk management