What is Distributed Computing ??

Distributed computing is a field of computer science that studies distributed systems. A distributed system consists of multiple autonomous computers that communicate through a computer network. The computers interact with each other in order to achieve a common goal. A computer program that runs in a distributed system is called a distributed program, and distributed programming is the process of writing such programs.^[1]
Distributed computing also refers to the use of distributed systems to solve computational problems. In distributed computing, a problem is divided into many tasks, each of which is solved by one or more computers.^[2]

Parallel and distributed computing

Distributed systems are groups of networked computers, which have the same goal for their work. The terms "concurrent computing", "parallel computing", and "distributed computing" have a lot of overlap, and no clear distinction exists between them.^[13] The same system may be characterised both as "parallel" and "distributed"; the processors in a typical distributed system run concurrently in parallel.^[14] Parallel computing may be seen as a particular tightly-coupled form of distributed computing,^[15] and distributed computing may be seen as a loosely-coupled form of parallel computing.^[5] Nevertheless, it is possible to roughly classify concurrent systems as "parallel" or "distributed" using the following criteria:

• In parallel computing, all processors have access to a shared memory. Shared memory can be used to exchange information between processors.^[16]
• In distributed computing, each processor has its own private memory (distributed memory). Information is exchanged by passing messages between the processors.^[17]

The figure on the right illustrates the difference between distributed and parallel systems. Figure (a) is a schematic view of a typical distributed system; as usual, the system is represented as a network topology in which each node is a computer and each line connecting the nodes is a communication link. Figure (b) shows the same distributed system in more detail: each computer has its own local memory, and information can be exchanged only by passing messages from one node to another by using the available communication links. Figure (c) shows a parallel system in which each processor has a direct access to a shared memory.
The situation is further complicated by the traditional uses of the terms parallel and distributed algorithm that do not quite match the above definitions of parallel and distributed systems; see the section Theoretical foundations below for more detailed discussion. Nevertheless, as a rule of thumb, high-performance parallel computation in a shared-memory multiprocessor uses parallel algorithms while the coordination of a large-scale distributed system uses distributed algorithms.

Applications

There are two main reasons for using distributed systems and distributed computing. First, the very nature of the application may require the use of a communication network that connects several computers. For example, data is produced in one physical location and it is needed in another location.
Second, there are many cases in which the use of a single computer would be possible in principle, but the use of a distributed system is beneficial for practical reasons. For example, it may be more cost-efficient to obtain the desired level of performance by using a cluster of several low-end computers, in comparison with a single high-end computer. A distributed system can be more reliable than a non-distributed system, as there is no single point of failure. Moreover, a distributed system may be easier to expand and manage than a monolithic uniprocessor system.^[21]
Examples of distributed systems and applications of distributed computing include the following:^[22]

• Telecommunication networks:
  • Telephone networks and cellular networks.
  • Computer networks such as the Internet.
  • Wireless sensor networks.
  • Routing algorithms.
• Network applications:
  • World wide web and peer-to-peer networks.
  • Massively multiplayer online games and virtual reality communities.
  • Distributed databases and distributed database management systems.
  • Network file systems.
  • Distributed information processing systems such as banking systems and airline reservation systems.
• Real-time process control:
  • Aircraft control systems.
  • Industrial control systems.
• Parallel computation:
  • Scientific computing, including cluster computing and grid computing and various volunteer computing projects; see the list of distributed computing projects.
  • Distributed rendering in computer graphics.
  
  
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