Distributed computing

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Distributed computing deals with hardware and software systems containing more than one processing element or storage element, concurrent processes, or multiple programs, running under a loosely or tightly controlled regime.

In distributed computing a program is split up into parts that run simultaneously on multiple computers communicating over a network. Distributed computing is a form of parallel computing, but parallel computing is most commonly used to describe program parts running simultaneously on multiple processors in the same computer. Both types of processing require dividing a program into parts that can run simultaneously, but distributed programs often must deal with heterogeneous environments, network links of varying latencies, and unpredictable failures in the network or the computers.

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[edit] Organization

Organizing the interaction between the computers that execute distributed computations is of prime importance. In order to be able to use the widest possible variety of computers, the protocol or communication channel should not contain or use any information that may not be understood by certain machines. Special care must also be taken that messages are indeed delivered correctly and that invalid messages, which would otherwise bring down the system and perhaps the rest of the network, are rejected.

Another important factor is the ability to send software to another computer in a portable way so that it may execute and interact with the existing network. This may not always be practical when using differing hardware and resources, in which case other methods, such as cross-compiling or manually porting this software, must be used.

[edit] Goals and advantages

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.

[edit] 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. Openness cannot be achieved unless the specification and documentation of the key software interface of the component of a system are made available to the software developer.

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

Monotonicity
Once something is published in an open system, it cannot be taken back.
Pluralism
Different subsystems of an open distributed system include heterogeneous, overlapping and possibly conflicting information. There is no central arbiter of truth in open distributed systems.
Unbounded Nondeterminism
Asynchronously, different subsystems can come up and go down and communication links can come in and go out between subsystems of an open distributed system. Therefore the time that it will take to complete an operation cannot be bounded in advance.

[edit] Drawbacks and disadvantages

See also: Fallacies of Distributed Computing

[edit] Technical issues

If not planned properly, a distributed system can decrease the overall reliability of computations if the unavailability of a node can cause disruption of the other nodes. Leslie Lamport famously quipped that: "A distributed system is one in which the failure of a computer you didn't even know existed can render your own computer unusable."[1]

Troubleshooting and diagnosing problems in a distributed system can also become more difficult, because the analysis may require connecting to remote nodes or inspecting communication between nodes.

Many types of computation are not well suited for distributed environments, typically owing to the amount of network communication or synchronization that would be required between nodes. If bandwidth, latency, or communication requirements are too significant, then the benefits of distributed computing may be negated and the performance may be worse than a non-distributed environment.

[edit] Architecture

Various hardware and software architectures are used for distributed computing. At a lower level, it is necessary to interconnect multiple CPUs with some sort of network, regardless of whether that network is printed onto a circuit board or made up of 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.

Distributed programming typically falls into one of several basic architectures or categories: Client-server, 3-tier architecture, N-tier architecture, Distributed objects, loose coupling, or tight coupling.

  • Client-server — Smart client code contacts the server for data, then formats and displays it to the user. Input at the client is committed back to the server when it represents a permanent change.
  • 3-tier architecture — Three tier systems move the client intelligence to a middle tier so that stateless clients can be used. This simplifies application deployment. Most web applications are 3-Tier.
  • N-tier architecture — N-Tier refers typically to web applications which further forward their requests to other enterprise services. This type of application is the one most responsible for the success of application servers.
  • Tightly coupled (clustered) — refers typically to a cluster of machines that closely work together, running a shared process in parallel. The task is subdivided in parts that are made individually by each one and then put back together to make the final result.
  • Peer-to-peer — an architecture where there is no special machine or machines that provide a service or manage the network resources. Instead all responsibilities are uniformly divided among all machines, known as peers. Peers can serve both as clients and servers.
  • Space based — refers to an infrastructure that creates the illusion (virtualization) of one single address-space. Data are transparently replicated according to application needs. Decoupling in time, space and reference is achieved.

Another basic aspect of distributed computing architecture is the method of communicating and coordinating work among concurrent processes. Through various message passing protocols, processes may communicate directly with one another, typically in a master/slave relationship. Alternatively, a "database-centric" architecture can enable distributed computing to be done without any form of direct inter-process communication, by utilizing a shared database.[2]

[edit] Concurrency

Distributed computing implements a kind of concurrency. It interrelates tightly with concurrent programming so much that they are sometimes not taught as distinct subjects.[3]

[edit] 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 (or different threads belonging to the same process) on different CPUs.

[edit] Multicore systems

Intel CPUs from the late Pentium 4 era (Northwood and Prescott cores) employed a technology called Hyper-threading that allowed more than one thread (usually two) to run on the same CPU. The more recent Sun UltraSPARC T1, AMD Athlon 64 X2, AMD Athlon FX, AMD Opteron, AMD Phenom, Intel Pentium D, Intel Core, Intel Core 2, Intel Core 2 Quad, and Intel Xeon processors feature multiple processor cores to also increase the number of concurrent threads they can run.

[edit] Multicomputer systems

A multicomputer may be considered to be either a loosely coupled NUMA computer or a tightly coupled cluster. Multicomputers are commonly used when strong computer power is required in an environment with restricted physical space or electrical power.

Common suppliers include Mercury Computer Systems, CSPI, and SKY Computers.

Common uses include 3D medical imaging devices and mobile radar.

[edit] Computing taxonomies

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

[edit] Computer clusters

Main article: Cluster computing

A cluster consists of 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. Distributed computing also often consists of machines which are widely separated geographically.

[edit] Grid computing

Main article: Grid computing

A grid uses the resources of many separate computers, loosely connected by a network (usually the Internet), to solve large-scale computation problems. Public grids may 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.

[edit] Languages

Nearly any programming language that has access to the full hardware of the system could handle distributed programming given enough time and code. Remote procedure calls distribute operating system commands over a network connection. Systems like CORBA, Microsoft DCOM, Java RMI and others, try to map object oriented design to the network. Loosely coupled systems communicate through intermediate documents that are typically human readable (e.g. XML, HTML, SGML, X.500, and EDI).

[edit] Examples

[edit] Projects

Main article: List of distributed computing projects
Berkeley Open Infrastructure for Network Computing (BOINC), became useful as a platform for several distributed applications in areas as diverse as mathematics, medicine, molecular biology, climatology, and astrophysics.[4]

A variety of distributed computing projects have grown up in recent years. Many are run on a volunteer basis, and involve users donating their unused computational power to work on interesting computational problems. Examples of such projects include the Stanford University Chemistry Department Folding@home project, which is focused on simulations of protein folding to find disease cures and to understand biophysical systems; World Community Grid, an effort to create the world's largest public computing grid to tackle scientific research projects that benefit humanity, run and funded by IBM; SETI@home, which is focused on analyzing radio-telescope data to find evidence of intelligent signals from space, hosted by the Space Sciences Laboratory at the University of California, Berkeley (the Berkeley Open Infrastructure for Network Computing (BOINC), was originally developed to support this project); OurGrid, which is a free-to-join peer-to-peer grid provided by the idle resources of all participants; LHC@home, which is used to help design and tune the Large Hadron Collider, hosted by CERN in Geneva; and distributed.net, which is focused on finding optimal Golomb rulers and breaking various cryptographic ciphers.[5]

Distributed computing projects also often involve competition with other distributed systems. This competition may be for prestige, or it may be a matter of enticing users to donate processing power to a specific project. For example, stat races are a measure of the work a distributed computing 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.

[edit] See also

Sister project Wikibooks has a book on the topic of

[edit] References

  1. ^ Leslie Lamport. "Subject: distribution (Email message sent to a DEC SRC bulletin board at 12:23:29 PDT on 28 May 1987)". http://research.microsoft.com/users/lamport/pubs/distributed-system.txt. Retrieved on 2007-04-28. 
  2. ^ A database-centric virtual chemistry system, J Chem Inf Model. 2006 May-Jun;46(3):1034-9
  3. ^ CS236370 Concurrent and Distributed Programming 2002
  4. ^ BOINC - Berkeley Open Infrastructure for Network Computing, Dr. David Anderson describes SETI@home, BOINC and Distributed Computing, youtube.com
  5. ^ David P. Anderson (2005-05-23) (PDF). A Million Years of Computing. http://www.ngp.org.sg/seminars/Slides/DavidAnderson-Seminar@NLB.pdf. Retrieved on 2006-08-11. 

[edit] Further reading

[edit] External links

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Parallel computing topics
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High-performance computing  · Cluster computing  · Distributed computing  · Grid computing
Parallelism (levels)
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Theory
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Programming
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