Information about Grid Computing
Grid computing is a phrase in distributed computing which can have several meanings:
Functionally, one can also speak of several types of grids:
The primary advantage of distributed computing is that each node can be purchased as commodity hardware, which when combined can produce similar computing resources to a many-CPU supercomputer, but at lower cost. This is due to the economies of scale of producing commodity hardware, compared to the lower efficiency of designing and constructing a small number of custom supercomputers. The primary performance disadvantage is that the various CPUs and local storage areas do not have high-speed connections. This arrangement is thus well-suited to applications where multiple parallel computations can take place independently, without the need to communicate intermediate results between CPUs.
The high-end scalability of geographically dispersed grids is generally favorable, due to the low need for connectivity between nodes relative to the capacity of the public Internet. Conventional supercomputers also create physical challenges in supplying sufficient electricity and cooling capacity in a single location. Both supercomputers and grids can be used to run multiple parallel computations at the same time, which might be different simulations for the same project, or computations for completely different applications. The infrastructure and programming considerations needed to do this on each type of platform are different, however.
There are also differences in programming and deployment. It can be costly and difficult to write programs so that they can be run in the environment of a supercomputer, which may have a custom operating system, or require the program to address concurrency issues. If a problem can be adequately parallelized, a "thin" layer of "grid" infrastructure can cause conventional, standalone programs to run on multiple machines (but each given a different part of the same problem). This makes it possible to write and debug programs on a single conventional machine, and eliminates complications due to multiple instances of the same program running in the same shared memory and storage space at the same time.
One disadvantage of this feature is that the computers which are actually performing the calculations might not be entirely trustworthy. The designers of the system must thus introduce measures to prevent malfunctions or malicious participants from producing false, misleading, or erroneous results, and from using the system as an attack vector. This often involves assigning work randomly to different nodes (presumably with different owners) and checking that at least two different nodes report the same answer for a given work unit. Discrepancies would identify malfunctioning and malicious nodes.
Due to the lack of central control over the hardware, there is no way to guarantee that nodes will not drop out of the network at random times. Some nodes (like laptops or dialup Internet customers) may also be available for computation but not network communications for unpredictable periods. These variations can be accommodated by assigning large work units (thus reducing the need for continuous network connectivity) and reassigning work units when a given node fails to report its results as expected.
The impacts of trust and availability on performance and development difficulty can influence the choice of whether to deploy onto a dedicated computer cluster, to idle machines internal to the developing organization, or to an open external network of volunteers or contractors.
In many cases, the participating nodes must trust the central system not to abuse the access that is being granted, by interfering with the operation of other programs, mangling stored information, transmitting private data, or creating new security holes. Other systems employ measures to reduce the amount of trust "client" nodes must place in the central system such as placing applications in virtual machines.
Public systems or those crossing administrative domains (including different departments in the same organization) often result in the need to run on heterogeneous systems, using different operating systems and hardware architectures. With many languages, there is a tradeoff between investment in software development and the number of platforms that can be supported (and thus the size of the resulting network). Cross-platform languages can reduce the need to make this tradeoff, though potentially at the expense of high performance on any given node (due to run-time interpretation or lack of optimization for the particular platform).
Various middleware projects have created generic infrastructure, to allow diverse scientific and commercial projects to harness a particular associated grid, or for the purpose of setting up new grids. BOINC is a common one for academic projects seeking public volunteers; more are listed at the end of the article
Volunteer computing projects use the CPU scavenging model almost exclusively.
In practice, participating computers also donate some supporting amount of disk storage space, RAM, and network bandwidth, in addition to raw CPU power. Since nodes are apt to go "offline" from time to time, as their owners use their resources for their primary purpose, this model must be designed to handle such contingencies.
CPU scavenging and volunteer computing were popularized beginning in 1997 by distributed.net and later in 1999 by SETI@home to harness the power of networked PCs worldwide, in order to solve CPU-intensive research problems.
The ideas of the grid (including those from distributed computing, object oriented programming, cluster computing, web services and others) were brought together by Ian Foster, Carl Kesselman and Steve Tuecke, widely regarded as the "fathers of the grid[1]." They led the effort to create the Globus Toolkit incorporating not just computation management but also storage management, security provisioning, data movement, monitoring and a toolkit for developing additional services based on the same infrastructure including agreement negotiation, notification mechanisms, trigger services and information aggregation. While the Globus Toolkit remains the defacto standard for building grid solutions, a number of other tools have been built that answer some subset of services needed to create an enterprise or global grid.
Grids offer a way to solve Grand Challenge problems like protein folding, financial modeling, earthquake simulation, and climate/weather modeling. Grids offer a way of using the information technology resources optimally inside an organization. They also provide a means for offering information technology as a utility for commercial and non-commercial clients, with those clients paying only for what they use, as with electricity or water.
Grid computing is presently being applied successfully by the National Science Foundation's National Technology Grid, NASA's Information Power Grid, Pratt & Whitney, Bristol-Myers Squibb, Co., and American Express.
One of the most famous cycle-scavenging networks is SETI@home, which was using more than 3 million computers to achieve 23.37 sustained teraflops (979 lifetime teraflops) as of September 2001 [1].
As of May 2005, Folding@home had achieved peaks of 186 teraflops on over 160,000 machines.
Another well-known project is distributed.net, which was started in 1997 and has run a number of successful projects in its history.
The NASA Advanced Supercomputing facility (NAS) has run genetic algorithms using the Condor cycle scavenger running on about 350 Sun and SGI workstations.
Until April 27, 2007, United Devices operated the United Devices Cancer Research Project based on its Grid MP product, which cycle scavenges on volunteer PCs connected to the Internet. As of June 2005, the Grid MP ran on about 3,100,000 machines [2].
The Enabling Grids for E-sciencE project, which is based in the European Union and includes sites in Asia and the United States, is a follow up project to the European DataGrid (EDG) and is arguably the largest computing grid on the planet. This, along with the LHC Computing Grid [4] (LCG) have been developed to support the experiments using the CERN Large Hadron Collider. The LCG project is driven by CERN's need to handle huge amounts of data, where storage rates of several gigabytes per second (10 petabytes per year) are required. A list of active sites participating within LCG can be found online[5] as can real time monitoring of the EGEE infrastructure.[6] The relevant software and documentation is also publicly accessible.[7]
- A local computer cluster which is like a "grid" because it is composed of multiple nodes.
- Offering online computation or storage as a metered commercial service, known as utility computing, computing on demand, or cloud computing.
- The creation of a "virtual supercomputer" by using spare computing resources within an organization.
- The creation of a "virtual supercomputer" by using a network of geographically dispersed computers. Volunteer computing, which generally focuses on scientific, mathematical, and academic problems, is the most common application of this technology.
Functionally, one can also speak of several types of grids:
- Computational grids (including CPU Scavenging grids) which focuses primarily on computationally-intensive operations.
- Data grids or the controlled sharing and management of large amounts of distributed data.
- Equipment grids which have a primary piece of equipment e.g. a telescope, and where the surrounding Grid is used to control the equipment remotely and to analyze the data produced.
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Virtual Organizations accessing different and overlapping sets of resources
Grids versus conventional supercomputers
"Distributed" or "grid computing" in general is a special type of parallel computing which relies on complete computers (with onboard CPU, storage, power supply, network interface, etc.) connected to a network (private, public or the Internet) by a conventional network interface, such as Ethernet. This is in contrast to the traditional notion of a supercomputer, which has many CPUs connected by a local high-speed computer bus.The primary advantage of distributed computing is that each node can be purchased as commodity hardware, which when combined can produce similar computing resources to a many-CPU supercomputer, but at lower cost. This is due to the economies of scale of producing commodity hardware, compared to the lower efficiency of designing and constructing a small number of custom supercomputers. The primary performance disadvantage is that the various CPUs and local storage areas do not have high-speed connections. This arrangement is thus well-suited to applications where multiple parallel computations can take place independently, without the need to communicate intermediate results between CPUs.
The high-end scalability of geographically dispersed grids is generally favorable, due to the low need for connectivity between nodes relative to the capacity of the public Internet. Conventional supercomputers also create physical challenges in supplying sufficient electricity and cooling capacity in a single location. Both supercomputers and grids can be used to run multiple parallel computations at the same time, which might be different simulations for the same project, or computations for completely different applications. The infrastructure and programming considerations needed to do this on each type of platform are different, however.
There are also differences in programming and deployment. It can be costly and difficult to write programs so that they can be run in the environment of a supercomputer, which may have a custom operating system, or require the program to address concurrency issues. If a problem can be adequately parallelized, a "thin" layer of "grid" infrastructure can cause conventional, standalone programs to run on multiple machines (but each given a different part of the same problem). This makes it possible to write and debug programs on a single conventional machine, and eliminates complications due to multiple instances of the same program running in the same shared memory and storage space at the same time.
Design considerations and variations
One feature of distributed grids is that they can be formed from computing resources belonging to multiple individuals or organizations (known as multiple administrative domains). This can facilitate commercial transactions, as in utility computing, or make it easier to assemble volunteer computing networks.One disadvantage of this feature is that the computers which are actually performing the calculations might not be entirely trustworthy. The designers of the system must thus introduce measures to prevent malfunctions or malicious participants from producing false, misleading, or erroneous results, and from using the system as an attack vector. This often involves assigning work randomly to different nodes (presumably with different owners) and checking that at least two different nodes report the same answer for a given work unit. Discrepancies would identify malfunctioning and malicious nodes.
Due to the lack of central control over the hardware, there is no way to guarantee that nodes will not drop out of the network at random times. Some nodes (like laptops or dialup Internet customers) may also be available for computation but not network communications for unpredictable periods. These variations can be accommodated by assigning large work units (thus reducing the need for continuous network connectivity) and reassigning work units when a given node fails to report its results as expected.
The impacts of trust and availability on performance and development difficulty can influence the choice of whether to deploy onto a dedicated computer cluster, to idle machines internal to the developing organization, or to an open external network of volunteers or contractors.
In many cases, the participating nodes must trust the central system not to abuse the access that is being granted, by interfering with the operation of other programs, mangling stored information, transmitting private data, or creating new security holes. Other systems employ measures to reduce the amount of trust "client" nodes must place in the central system such as placing applications in virtual machines.
Public systems or those crossing administrative domains (including different departments in the same organization) often result in the need to run on heterogeneous systems, using different operating systems and hardware architectures. With many languages, there is a tradeoff between investment in software development and the number of platforms that can be supported (and thus the size of the resulting network). Cross-platform languages can reduce the need to make this tradeoff, though potentially at the expense of high performance on any given node (due to run-time interpretation or lack of optimization for the particular platform).
Various middleware projects have created generic infrastructure, to allow diverse scientific and commercial projects to harness a particular associated grid, or for the purpose of setting up new grids. BOINC is a common one for academic projects seeking public volunteers; more are listed at the end of the article
CPU scavenging
CPU-scavenging, cycle-scavenging, cycle stealing, or shared computing creates a "grid" from the unused resources in a network of participants (whether worldwide or internal to an organization). Typically this technique uses desktop computer instruction cycles that would otherwise be wasted at night, during lunch, or even in the scattered seconds throughout the day when the computer is waiting for user input or slow devices.Volunteer computing projects use the CPU scavenging model almost exclusively.
In practice, participating computers also donate some supporting amount of disk storage space, RAM, and network bandwidth, in addition to raw CPU power. Since nodes are apt to go "offline" from time to time, as their owners use their resources for their primary purpose, this model must be designed to handle such contingencies.
History
The term Grid computing originated in the early 1990s as a metaphor for making computer power as easy to access as an electric power grid in Ian Foster and Carl Kesselmans seminal work, "The Grid: Blueprint for a new computing infrastructure".CPU scavenging and volunteer computing were popularized beginning in 1997 by distributed.net and later in 1999 by SETI@home to harness the power of networked PCs worldwide, in order to solve CPU-intensive research problems.
The ideas of the grid (including those from distributed computing, object oriented programming, cluster computing, web services and others) were brought together by Ian Foster, Carl Kesselman and Steve Tuecke, widely regarded as the "fathers of the grid[1]." They led the effort to create the Globus Toolkit incorporating not just computation management but also storage management, security provisioning, data movement, monitoring and a toolkit for developing additional services based on the same infrastructure including agreement negotiation, notification mechanisms, trigger services and information aggregation. While the Globus Toolkit remains the defacto standard for building grid solutions, a number of other tools have been built that answer some subset of services needed to create an enterprise or global grid.
Fastest virtual supercomputers
Current projects and applications
Grids offer a way to solve Grand Challenge problems like protein folding, financial modeling, earthquake simulation, and climate/weather modeling. Grids offer a way of using the information technology resources optimally inside an organization. They also provide a means for offering information technology as a utility for commercial and non-commercial clients, with those clients paying only for what they use, as with electricity or water.
Grid computing is presently being applied successfully by the National Science Foundation's National Technology Grid, NASA's Information Power Grid, Pratt & Whitney, Bristol-Myers Squibb, Co., and American Express.
One of the most famous cycle-scavenging networks is SETI@home, which was using more than 3 million computers to achieve 23.37 sustained teraflops (979 lifetime teraflops) as of September 2001 [1].
As of May 2005, Folding@home had achieved peaks of 186 teraflops on over 160,000 machines.
Another well-known project is distributed.net, which was started in 1997 and has run a number of successful projects in its history.
The NASA Advanced Supercomputing facility (NAS) has run genetic algorithms using the Condor cycle scavenger running on about 350 Sun and SGI workstations.
Until April 27, 2007, United Devices operated the United Devices Cancer Research Project based on its Grid MP product, which cycle scavenges on volunteer PCs connected to the Internet. As of June 2005, the Grid MP ran on about 3,100,000 machines [2].
The Enabling Grids for E-sciencE project, which is based in the European Union and includes sites in Asia and the United States, is a follow up project to the European DataGrid (EDG) and is arguably the largest computing grid on the planet. This, along with the LHC Computing Grid [4] (LCG) have been developed to support the experiments using the CERN Large Hadron Collider. The LCG project is driven by CERN's need to handle huge amounts of data, where storage rates of several gigabytes per second (10 petabytes per year) are required. A list of active sites participating within LCG can be found online[5] as can real time monitoring of the EGEE infrastructure.[6] The relevant software and documentation is also publicly accessible.[7]
Definitions
Today there are many definitions of Grid computing:- In his article "What is the Grid? A Three Point Checklist"[8], Ian Foster lists these primary attributes:
- Computing resources are not administered centrally.
- Open standards are used.
- Non-trivial quality of service is achieved.
- Plaszczak/Wellner[9] define grid technology as "the technology that enables resource virtualization, on-demand provisioning, and service (resource) sharing between organizations."
- IBM defines grid computing as "the ability, using a set of open standards and protocols, to gain access to applications and data, processing power, storage capacity and a vast array of other computing resources over the Internet. A grid is a type of parallel and distributed system that enables the sharing, selection, and aggregation of resources distributed across 'multiple' administrative domains based on their (resources) availability, capacity, performance, cost and users' quality-of-service requirements" [10]
- An earlier example of the notion of computing as utility was in 1965 by MIT's Fernando Corbató. Fernando and the other designers of the Multics operating system envisioned a computer facility operating "like a power company or water company". http://www.multicians.org/fjcc3.html
- Buyya defines a grid as "a type of parallel and distributed system that enables the sharing, selection, and aggregation of geographically distributed autonomous resources dynamically at runtime depending on their availability, capability, performance, cost, and users' quality-of-service requirements".[11]
- CERN, one of the largest users of grid technology, talk of The Grid: "a service for sharing computer power and data storage capacity over the Internet." [12]
- Pragmatically, grid computing is attractive to geographically-distributed non-profit collaborative research efforts like the NCSA Bioinformatics Grids such as BIRN: external grids.
- Grid computing is also attractive to large commercial enterprises with complex computation problems who aim to fully exploit their internal computing power: internal grids.
- A recent survey (done by Heinz Stockinger in spring 2006; to be published in the Journal of Supercomputing in early 2007) presents a snapshot on the view in 2006.
- Another survey (done by Miguel L. Bote-Lorenzo et al. in autumn 2002; published in the LNCS series of Springer-Verlag) presents a snapshot on the view in 2002.
See also
Concepts and related technology
- Distributed computing
- List of distributed computing projects
- High-performance computing
- Network Agility
- Render farm
- Semantic grid
- Supercomputer
- Computer cluster
- Computon
- Grid FileSystem
- Edge computing
- Metacomputing
Alliances and organizations
Production grids
- Distributed European Infrastructure for Supercomputing Applications DEISA
- Enabling Grids for E-sciencE
- FusionGrid
- INFN Production Grid
- NorduGrid
- Open Science Grid
- OurGrid
- Sun Grid
- UC Grid
- Xgrid
International Grid Projects
- [http://datatag.web.cern.ch/datatag/ DataTAG] - January 2001 -> January 2003
- [http://eu-datagrid.web.cern.ch/eu%2Ddatagrid/ European DataGrid] (EDG) - March 2001 -> March 2003
- Enabling Grids for E-sciencE (EGEE) - March 2004 -> March 2006
- Enabling Grids for E-sciencE II (EGEE II) - April 2006 -> April 2008
- Open Middleware Infrastructure Institute Europe (OMII-Europe) - May 2006 -> May 2008
National Grid Projects
- China Grid Project
- D-Grid (German)
- GARUDA (Indian)
- grid computing project at VECC (Calcutta, India)
- INFN Grid (Italian)
- Malaysia National Grid Computing
- NAREGI Project
- Singapore National Grid Project
- Thai National Grid Project
Standards and APIs
- A Simple API for Grid Applications (SAGA)
- Common Object Request Broker Architecture (CORBA)
- Distributed Resource Management Application API (DRMAA)
- Grid Security Infrastructure (GSI)
- Open Grid Services Architecture (OGSA)
- Open Grid Services Infrastructure (OGSI)
- Web Services Resource Framework (WSRF)
Software implementations and middleware
- Advanced Resource Connector (NorduGrid's ARC)
- Amazon EC2 http://www.amazon.com/gp/browse.html?node=201590011
- Alchemi
- Berkeley Open Infrastructure for Network Computing (BOINC)
- gLite (EGEE)
- Globus Toolkit
- GridGain http://www.gridgain.com
- Gridbus Middleware
- Message Passing Interface (MPI)
- NInf GridRPC
- IceGrid
- Invisionix Roaming System Remote (IRSR)
- Java CoG Kit
- Java Parallel Processing Framework (JPPF)
- Parallel Virtual Machine (PVM)
- SDSC Storage resource broker (data grid)
- Simple Grid Protocol
- Sun Grid Engine
- ProActive
- Vishwa http://dos.iitm.ac.in/Vishwa
- UGP
- UNICORE
References
Notes
1. ^ Father of the Grid.
2. ^ [3], accessed 4 Jun 2007
3. ^ [4], accessed 23 Sept 2007
4. ^ Large Hadron Collider Computing Grid offical homepage
5. ^ [5]
6. ^ [6]
7. ^ [7]
8. ^ What is the Grid? A Three Point Checklist (pdf).
9. ^ P Plaszczak, R Wellner, Grid computing, 2005, Elsevier/Morgan Kaufmann, San Francisco
10. ^ IBM Solutions Grid for Business Partners: Helping IBM Business Partners to Grid-enable applications for the next phase of e-business on demand.
11. ^ A Gentle Introduction to Grid Computing and Technologies (pdf). Retrieved on 2005-05-06.
12. ^ The Grid Café - What is Grid?. CERN. Retrieved on 2005-02-04.
2. ^ [3], accessed 4 Jun 2007
3. ^ [4], accessed 23 Sept 2007
4. ^ Large Hadron Collider Computing Grid offical homepage
5. ^ [5]
6. ^ [6]
7. ^ [7]
8. ^ What is the Grid? A Three Point Checklist (pdf).
9. ^ P Plaszczak, R Wellner, Grid computing, 2005, Elsevier/Morgan Kaufmann, San Francisco
10. ^ IBM Solutions Grid for Business Partners: Helping IBM Business Partners to Grid-enable applications for the next phase of e-business on demand.
11. ^ A Gentle Introduction to Grid Computing and Technologies (pdf). Retrieved on 2005-05-06.
12. ^ The Grid Café - What is Grid?. CERN. Retrieved on 2005-02-04.
Bibliography
- Davies, Antony (June 2004). "Computational Intermediation and the Evolution of Computation as a Commodity" (pdf). Applied Economics.
- Foster, Ian; Carl Kesselman. The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann Publishers. ISBN 1-55860-475-8.
- Plaszczak, Pawel; Rich Wellner, Jr. Grid Computing "The Savvy Manager's Guide". Morgan Kaufmann Publishers. ISBN 0-12-742503-9.
- Berman, Fran; Anthony J. G. Hey, Geoffrey C. Fox. Grid Computing: Making The Global Infrastructure a Reality. Wiley. ISBN 0-470-85319-0.
- Li, Maozhen; Mark A. Baker. The Grid: Core Technologies. Wiley. ISBN 0-470-09417-6.
- Catlett, Charlie; Larry Smarr (June 1992). "Metacomputing". Communications of the ACM 35 (6).
- Smith, Roger (2005). Grid Computing: A Brief Technology Analysis. CTO Network Library.
- Buyya, Rajkumar (July 2005). "Grid Computing: Making the Global Cyberinfrastructure for eScience a Reality". CSI Communications 29 (1). ISSN 0970-647X.
- Berstis, Viktors. Fundamentals of Grid Computing. IBM.
- Ferreira, Luis; et.al.. Grid Computing Products and Services. IBM.
- Ferreira, Luis; et.al.. Introduction to Grid Computing with Globus. IBM.
- Jacob, Bart; et.al.. Enabling Applications for Grid Computing. IBM.
- Ferreira, Luis; et.al.. Grid Services Programming and Application Enablement. IBM.
- Jacob, Bart; et.al.. Introduction to Grid Computing. IBM.
- Ferreira, Luis; et.al.. Grid Computing in Research and Education. IBM.
- Ferreira, Luis; et.al.. Globus Toolkit 3.0 Quick Start. IBM.
- Surridge, Mike; et.al.. Experiences with GRIA – Industrial applications on a Web Services Grid. IEEE.
- Stockinger, Heinz; et al. (to be published in 2007). "Defining the Grid: A Snapshot on the Current View" (pdf). Supercomputing.
- Global Grids and Software Toolkits: A Study of Four Grid Middleware Technologies
External links
News & info
- Grid Computing Info Center
- GridToday
- GridsWatch
- IEEE Distributed Systems Online, Grid Computing Section
- International Science Grid This Week
- Primeur magazine - HPC and Grid computing news
- UtilityComputing.com
Portals and Grid Projects
- World Community Grid: Focuses on advancing scientific projects to benefit humanity, such as researching possible cures for cancer and muscular dystrophy, sequencing human genomes, finding better drug molecular structures to combat AIDS, etc. Open to anyone who wants to contribute idle PC processing time.
- Wikipedia article on the World Community Grid: Contains additional links for each project being conducted on the World Community Grid.
- DataSynapse
- GridGain Systems
- EnterTheGrid directory on Grid computing
- EELA: E-Infrastructure shared between Europe and Latin America
- Enabling Grids for E-sciencE
- Fura GPL Ready to use grid
- IBM Grid Computing website
- Java Parallel Processing Framework
- GridSphere Portal Framework (JSR-168 compliant)
- GridSummit.com
- Gridalogy
- BigBlueRiver
- NICE EnginFrame: Grid computing portals for research and industry
- Nivio: Virtual Desktop Based on Grid Computing
- Rechenkraft.net (German)
- gridGistics: service virtualization and grid computing.
- myGrid: bioinformatics and eScience research project built by several UK universities and EMBL-EBI.
- Consortsium SIRENE (Sharing Infrastructure and REsources iN Europe)
- Vendor-independent documentation on Grid-compliant open source portals
Articles
- O'Reilly article about grid computing software
- Grid Café, the place for everyone to learn about the Grid
Associations and conferences
- Open Grid Forum
- Israeli Association of Grid Technologies (IGT)
- International Conference on Grid Computing (Grid XY)
- International Symposium on Cluster Computing and the Grid (CCGrid XY)
Past events
- GridWorld Washington 2006
- IEEE Richmond Section Blog. Meeting — 5 October 2006:
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Distributed computing is a method of computer processing in which different parts of a program run simultaneously on two or more computers that are communicating with each other over a network.
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A computer cluster is a group of tightly coupled computers that work together closely so that in many respects they can be viewed as though they are a single computer. The components of a cluster are commonly, but not always, connected to each other through fast local area
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A node is a device that is connected as part of a computer network. For example, a node may be a computer, personal digital assistant, cell phone, router, switch, or hub.
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Utility computing (also known as on demand computing) is the packaging of computing resources, such as computation and storage, as a metered service similar to a physical public utility (such as electricity, water, natural gas, or telephone network).
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Cloud computing is a popular phrase that is shorthand for applications that were developed to be rich Internet applications that run on the Internet (or "cloud"). In the cloud computing paradigm, software that is traditionally installed on personal computers is shifted or extended
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Volunteer computing is a type of distributed computing in which computer owners donate their computing resources (such as processing power and storage) to one or more "projects".
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A computer cluster is a group of tightly coupled computers that work together closely so that in many respects they can be viewed as though they are a single computer. The components of a cluster are commonly, but not always, connected to each other through fast local area
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The introduction to this article is vague. To comply with Wikipedia's guidelines, it should be improved.
Please discuss this issue on the talk page and read the to make sure the introduction summarizes the article.
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Please discuss this issue on the talk page and read the to make sure the introduction summarizes the article.
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remote control is an electronic device used for the remote operation of a machine.
The term remote control can be also referred to as "remote" or "controller" when abbreviated.
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The term remote control can be also referred to as "remote" or "controller" when abbreviated.
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Parallel computing is the simultaneous execution of some combination of multiple instances of programmed instructions and data on multiple processors in order to obtain results faster.
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Internet is a worldwide, publicly accessible series of interconnected computer networks that transmit data by packet switching using the standard Internet Protocol (IP). It is a "network of networks" that consists of millions of smaller domestic, academic, business, and government
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Ethernet is a family of frame-based computer networking technologies for local area networks (LANs). The name comes from the physical concept of the ether. It defines a number of wiring and signaling standards for the physical layer, through means of network access at the Media
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For other uses, see Supercomputer (disambiguation).
A supercomputer is a computer that led the world (or was close to doing so) in terms of processing capacity, particularly speed of calculation, at the time of its introduction...... Click the link for more information.
bus (bidirectional universal switch) is a subsystem that transfers data or power between computer components inside a computer or between computers, and a bus typically is controlled by device driver software.
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Commodity computing is computing done on commodity computers as opposed to supermicrocomputers or boutique computers. Commodity computers are computer systems manufactured by multiple vendors, incorporated components based on open standards.
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C to C1.]] Economies of scale characterizes a production process in which an increase in the scale of the firm causes a decrease in the long run average cost of each unit.
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In telecommunications and software engineering, scalability is a desirable property of a system, a network, or a process, which indicates its ability to either handle growing amounts of work in a graceful manner, or to be readily enlarged.
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Concurrency, concurrent, or concurrence may refer to:
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- Concurrence, a legal term referring to the need to prove both actus reus and mens rea
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An administrative domain is a collection of hosts and routers, and the interconnecting network(s), managed by a single administrative authority.
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References
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Utility computing (also known as on demand computing) is the packaging of computing resources, such as computation and storage, as a metered service similar to a physical public utility (such as electricity, water, natural gas, or telephone network).
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Volunteer computing is a type of distributed computing in which computer owners donate their computing resources (such as processing power and storage) to one or more "projects".
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hot Dial-up access is a form of Internet access via telephone line. The client uses a modem connected to a computer and a telephone line to dial into an Internet service provider's (ISP) node to establish a modem-to-modem link, which is then routed to the Internet.
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A computer cluster is a group of tightly coupled computers that work together closely so that in many respects they can be viewed as though they are a single computer. The components of a cluster are commonly, but not always, connected to each other through fast local area
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Heterogeneous (American English)) means that something (an object or system) consists of a diverse range of different items. It is the antonym of , which means that an object or system consists of many identical items.
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An operating system (OS) is the software that manages the sharing of the resources of a computer. An operating system processes system data and user input, and responds by allocating and managing tasks and internal system resources as a service to users and programs of the
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computer architecture is the conceptual design and fundamental operational structure of a computer system. It is a blueprint and functional description of requirements (especially speeds and interconnections) and design implementations for the various parts of a computer —
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Cross-platform is a term which can refer to computer programs, operating systems, computer languages, programming languages, or other computer software and their implementations which can be made to work on multiple computer platforms.
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'Middleware is computer software that sits 'in the middle' between application software (e.g. a word processing program) and the operating system (Unix, Windows, z/OS etc.) It is similar to operating system software in that it provides functions to multiple applications, and it is
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The Berkeley Open Infrastructure for Network Computing (BOINC) is a non-commercial middleware system for volunteer computing, originally developed to support the SETI@home project, but intended to be useful for other applications in areas as diverse as mathematics, medicine,
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The instruction cycle (also called fetch-and-execute cycle, fetch-decode-execute cycle (FDX) can refer to either the time period during which one instruction is fetched from memory and executed when a computer receives a machine language instruction; or
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