(Post 09/12/2005) There are many different types of distributed computing systems and many challenges to overcome in successfully designing one. The main goal of a distributed computing system is to connect users and resources in a transparent, open, and scalable way. Ideally this arrangement is drastically more fault tolerant and more powerful than many combinations of stand-alone computer systems.

In computer science, distributed computing studies the coordinated use of physically distributed computers.

As stated by Andrew S. Tanenbaum, "Distributed systems need radically different software than centralized systems do."

Goal

There are many different types of distributed computing systems and many challenges to overcome in successfully designing one. The main goal of a distributed computing system is to connect users and resources in a transparent, open, and scalable way. Ideally this arrangement is drastically more fault tolerant and more powerful than many combinations of stand-alone computer systems.

Today Web Services provide the standard protocols for connecting distributed systems.

Examples

An example of a distributed system is the World Wide Web. As you are reading a web page, you are actually using the distributed system that comprises the site. As you are browsing the web, your web browser running on your own computer communicates with different web servers that provide web pages. Possibly, your browser uses a proxy server to access the web contents stored on web servers faster and more securely. To find these servers, it also uses the distributed domain name system. Your web browser communicates with all of these servers over the Internet, via a system of routers which are themselves part of a large distributed system.

Openness

Openness is the property of distributed systems such that each subsystem is continually open to interaction with other systems (see references). Web Services protocols are standards which enable distributed systems to be extended and scaled. In general, an open system that scales has an advantage over a perfectly closed and self-contained system.

Consequently, open distributed systems are required to meet the following challenges:

Scalability

A scalable system is one that can easily be altered to accommodate changes in the number of users, resources and computing entities affected to it. Scalability can be measured in three different dimensions:

Some loss of performance may occur in a system that allows itself to scale in one or more of these dimensions.

Multiprocessor systems

A multiprocessor system is simply a computer that has more than one CPU on its motherboard. If the operating system is built to take advantage of this, it can run different processes on different CPUs, or different threads belonging to the same process.

Over the years, many different multiprocessing options have been explored for use in distributed computing. OS' such as Linux already have built-in support for this. Intel CPUs employ a technology called Hyperthreading that allows more than one thread (usually two) to run on the same CPU. The most recent Sun UltraSPARC T1, Athlon 64 X2 and Intel Pentium D processors feature multiple processor cores to also increase the number of concurrent threads they can run.

Multicomputer systems

A multicomputer system is a system made up of several independent computers interconnected by a telecommunications network.

Multicomputer systems can be homogeneous or heterogeneous: A homogeneous distributed system is one where all CPUs are similar and are connected by a single type of network. They are often used for parallel computing which is a kind of distributed computing where every computer is working on different parts of a single problem.

In contrast a heterogeneous distributed system is one that can be made up of all sorts of different computers, eventually with vastly differing memory sizes, processing power and even basic underlying architecture. They are in widespread use today, with many companies adopting this architecture due to the speed with which hardware goes obsolete and the cost of upgrading a whole system simultaneously.

Architecture

Various hardware and software architectures exist that are usually used for distributed computing. At a lower level, it is necessary to interconnect multiple CPUs with some sort of network, regardless of that network being printed onto a circuit board or made up of several loosely-coupled devices and cables. At a higher level, it is necessary to interconnect processes running on those CPUs with some sort of communication system.

Concurrency

Distributed computing implements a kind of concurrency.

Computing taxonomies

The types of distributed computers are based on Flynn's taxonomy of systems; single instruction, single data (SISD), multiple instruction, single data (MISD), single instruction, multiple data (SIMD) and multiple instruction, multiple data (MIMD). Other taxonomies and architectures available at Computer architecture and in Category:Computer architecture.

Computer clusters

A cluster is multiple stand-alone machines acting in parallel across a local high speed network. Distributed computing differs from cluster computing in that computers in a distributed computing environment are typically not exclusively running "group" tasks, whereas clustered computers are usually much more tightly coupled. The difference makes distributed computing attractive because, when properly configured, it can use computational resources that would otherwise be unused. It can also make available computing resources which would otherwise be impossible.

The Second Life grid is a heterogeneous multicomputer and so are most Beowulf clusters.

Grid computing

A grid uses the resources of many separate computers connected by a network (usually the internet) to solve large-scale computation problems. Most use idle time on many thousands of computers throughout the world. Such arrangements permit handling of data that would otherwise require the power of expensive supercomputers or would have been impossible to analyze otherwise.

Distributed computing projects also often involve competition with other distributed systems. This competition may be for prestige, or it may be a means of enticing users to donate processing power to a specific project. For example, stat races are a measure of what the most distributed work a project has been able to compute over the past day or week. This has been found to be so important in practice that virtually all distributed computing projects offer online statistical analyses of their performances, updated at least daily if not in real-time.

See List of distributed computing projects for more information on specific projects.

See also

References

Distributed computing infrastructure

Distributed computing conferences and journals

Proprietary infrastructure

People who have contributed to the distributed computing resear

Foundations and Principles

Gul Agha, Henry Baker, James Aspnes, Hagit Attiya, Will Clinger, Danny Dolev, Shlomi Dolev, Michael J. Fischer, Vassos Hadzilacos, Carl Hewitt, Leslie Lamport, Nancy Lynch, Michael Merritt, Paul Spirakis, Michel Raynal, Sam Toueg, Aki Yonezawa

Systems

Ken Birman, Frans Kaashoek, Barbara Liskov, Andrew Tanenbaum,

External links

(theo Wikipedia)