2. GRID COMPUTING
Assinment
Submitted to: Ma’am Iqra Ilyas
Submitted by: Huma Tariq
Roll No: 14020204-050
Department: Computer Science
GC Women University
Sialkot
3. What is grid computing?
Overview:
Grid computing combines computers from multiple administrative domains to reach a
common goal, to solve a single task, and may then disappear just as quickly.
Defination:
At its most basic level, grid computing is a computer network in which each computer's
resources are shared with every other computer in the system. Processing power, memory
and data storage are all community resources that authorized users can tap into and
leverage for specific tasks.
4. Concept:The grid computing concept isn't a new one. It's a special kind of
distributed computing. In distributed computing, different computers within the same
network share one or more resources. In the ideal grid computing system, every resource is
shared, turning a computer network into a powerful supercomputer. With the right user
interface, accessing a grid computing systemwould look no different than accessing a local
machine's resources. Every authorized computer would have access to enormous
processing power and storage capacity.
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. The power grid metaphor for accessible
computing quickly became canonical when Ian Foster and Carl Kesselman published their
seminal work, "The Grid: Blueprint for a new computing infrastructure" (1999). This was
preceded by decades by the metaphor of utility computing (1961): computing as a public
utility, analogous to the phone system.
The ideas of the grid (including those from distributed computing, object-oriented
programming, and Web services) were brought together by Ian Foster, Carl Kesselman,
and Steve Tuecke, widely regarded as the "fathers of the grid".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 de facto standard for building grid solutions, a number of
other tools have been built that answer some subset of services neededto create an
enterprise or global grid.
5. How does it work?
Grids are a form of distributed computing whereby a “super virtual computer” is
composed of many networked loosely coupled computers acting together to perform very
large tasks. This technology has been applied to computationally intensive scientific,
mathematical, and academic problems through volunteer computing, and it is used in
commercial enterprises for such diverse applications as drug discovery, economic
forecasting, seismic analysis, and back office data processing in support for e-commerce
and Web services.
Coordinating applications on Grids can be a complex task, especially when coordinating
the flow of information across distributed computing resources. Grid workflow systems
have been developed as a specialized form of a workflow management systemdesigned
specifically to compose and execute a series of computational or data manipulation steps, or
a workflow, in the Grid context.
Grid middleware:
Grid middleware is a specific software product, which enables the sharing of heterogeneous
resources, and Virtual Organizations. It is installed and integrated into the existing
infrastructure of the involved company or companies, and provides a special layer placed
among the heterogeneous infrastructure and the specific user applications. Major grid
middlewares are Globus Toolkit, gLite, and UNICORE.
Grid-enabled applications are specific software applications that can utilize grid
infrastructure. This is made possible by the use of grid middleware, as pointed out above.
6. Examples:
Projects and applications:
Grid computing offers a way to solve Grand Challenge problems such as 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
noncommercial clients, with those clients paying only for what they use, as with electricity
or water.
E-Science project:
The Enabling Grids for E-sciencE project, based in the European Union and included sites
in Asia and the United States, was a follow-up project to the European DataGrid (EDG)
and evolvedinto the European Grid Infrastructure. This, along with the LHC Computing
Grid (LCG), was developed to support experiments using the CERN Large Hadron
Collider. A list of active sites participating within LCG can be found online as can real time
monitoring of the EGEE infrastructure.The relevant software and documentation is also
publicly accessible.There is speculation that dedicated fiber optic links, such as those
installed by CERN to address the LCG's data-intensive needs, may one day be available to
home users thereby providing internet services at speeds up to 10,000 times faster than a
traditional broadband connection. The European Grid Infrastructure has been also used
for other research activities and experiments such as the simulation of oncological clinical
trials.
SETI@home Project:
One of the more famous examples of grid computing projects is run by SETI, also known
as Search for Extra-Terrestrial Intelligence. The application looks for radio signals or
other forms of communication in space, in an effort to prove the existence of extra-
terrestrial intelligence. SETI developed a grid computing middleware where the program
could be executed over multiple computers, since the application requires huge computing
resources to scan the skies effectively. The infrastructure was designed in such a way that a
layman using the Internet could choose to donate their unused computing power to the
project. The middleware in known as BOINC (Berkeley Open Infrastructure for Network
Computing) and was distributed under a GNU public licence.
7. LHC Computing Grid:
Known as by the moniker ‘Screensaver Lifesaver’, the computational grid at Oxford
University’ Centre for Computational Drug Discovery is built along the same principles as
the BOINC infrastructure. In this project, volunteers donate a few of the computing cycles
when their screensavers are running. When screensavers are running, the computer is
essentially idle, apart from a few background tasks. The project aims to find a cure for
cancer using computational methods to screensmall molecule structures, otherwise
shortening a very lengthy process.
NFCR Centre for Computational Drug Discovery:
Known as by the moniker ‘Screensaver Lifesaver’, the computational grid at Oxford
University’ Centre for Computational Drug Discovery is built along the same principles as
the BOINC infrastructure.
In this project, volunteers donate a few of the computing cycles when their screensavers are
running. When screensavers are running, the computer is essentially idle, apart from a few
background tasks. The project aims to find a cure for cancer using computational methods
to screen small molecule structures, otherwise shortening a very lengthy process.
As of August 2009 Folding@home achieves more than 4 petaflops on over 350,000
machines.
Components of grid computing:
The three primary types of grids are summarized below. Of course, there are no hard
boundaries between these grid types and often grids may be a combination of two or more
of these.However, as you consider developing applications that may run in a grid
environment, remember that the type of grid environment that you will be using will affect
many of your decisions.
8. Computational grid:
A computational grid is focused on setting aside resources specifically for computing
power. In this type of grid, most of the machines are high-performance servers.
Computational grids are sometimes mischaracterized as useful only for
calculation/computational tasks, but in fact they are able to handle many research projects
that require a lot CPU time, a lot of memory or the ability to communicate in real time. In
some of these cases, supercomputers do not have the capacity to solve these needs. A
computational grid offers a convenient way to use many devices in combination.
Scavenging grid:
A scavenging grid is most commonly used with large numbers of desktop machines.
Machines are scavengedfor available CPU cycles and other resources. Owners of the
desktop machines are usually given control over when their resources are available to
participate in the grid.
CPU scavenging:
CPU-scavenging, cycle-scavenging, 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 evenin the scattered seconds throughout the day when
the computer is waiting for user input on relatively fast devices. In practice, participating
computers also donate some supporting amount of disk storage space, RAM, and network
bandwidth, in addition to raw CPU power.
Data grid:
A data grid is responsible for housing and providing access to data across multiple
organizations. Users are not concerned with where this data is located as long as they have
access to the data. For example, you may have two universities doing life science research,
each with unique data. A data grid would allow them to share their data, manage the data,
and manage security issues such as who has access to what data.A data grid is an
architecture or set of services that gives individuals or groups of users the ability to access,
modify and transfer extremely large amounts of geographically distributed data for
research purposes.
9. Functionalityof data grid:
Resource management system (RMS):
The resource management system represents the core functionality of the data grid. It is
the heart of the system that manages all actions related to storage resources. In some data
grids it may be necessary to create a federated RMS architecture because of different
administrative policies and a diversity of possibilities found within the data grid in place of
using a single RMS. In such a case the RMSs in the federation will employ an architecture
that allows for interoperability based on an agreed upon set of protocols for actions related
to storage resources.
