3. the grid new infrastructure

GRID COMPUTING
Sandeep Kumar Poonia
Head Of Dept. CS/IT
B.E., M.Tech., UGC-NET
LM-IAENG, LM-IACSIT,LM-CSTA, LM-AIRCC, LM-SCIEI, AM-UACEE

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The Grid: A new Infrastructure
for 21st century science
As computer networks become cheaper and more powerful, a new computing
paradigm is poised to transform the practice of science and engineering.
A pc in 2001 is as fast as a supercomputer of 1990.
But 10 years ago, biologists were happy to compute a single
molecular structure. Now, they want to calculate the structures
of complex assemblies of macromolecules and screen
thousands of drug candidates.
Personal computers now ship with up to 100 gigabytes (GB)
of storage – as much as an entire 1990 supercomputer center.
Some wide area networks now operate at 155 Mbps, three
orders of magnitude faster than the state-of-the art 56 Kbps that
connected U.S. supercomputer centers in 1985.

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TECHNOLOGY TRENDS
Storage, Networks, and Computing power, doubles or,
more or less equivalently, halves in price in around 12, 9,
and 18 months, respectively

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INFRASTRUCTURE AND TOOLS
The road system enables us to travel by car; the international banking
system allows us to transfer funds across borders; and the Internet
allows us to communicate with virtually any electronic device
A Grid infrastructure needs to provide more functionality than the
Internet on which it rests, but it must also remain simple. And of
course, the need remains for supporting the resources that power
the Grid, such as high-speed data movement, caching of large
datasets, and on-demand access to computing.
Tools make use of infrastructure services. Internet and Web
tools include browsers for accessing remote Web sites, e-mail
programs for handling electronic messages, and search engines
for locating Web pages.
Grid tools are concerned with resource discovery, data
management, scheduling of computation, security, and so forth.

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Science portals:
Science portals make advanced problem-solving
methods easier to use by invoking sophisticated
packages remotely from Web browsers or other
simple, easily downloaded ‘thin clients.’
The packages themselves can also run remotely on
suitable computers within a Grid.
Such portals are currently being developed in
biology, fusion, computational chemistry, and other
disciplines.

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Distributed computing:
High-speed workstations and networks can yoke together an
organization’s PCs to form a substantial computational resource.
Entropia Inc’s Fight-AIDSAtHome system harnesses more than
30 000 computers to analyze AIDS drug candidates.
And in 2001, mathematicians across the U.S. and Italy pooled
their computational resources to solve a particular instance,
dubbed ‘Nug30,’ of an optimization problem.
For a week, the collaboration brought an average of 630 – and a
maximum of 1006 – computers to bear on Nug30, delivering a
total of 42 000 CPU-days.
Future improvements in network performance and Grid
technologies will increase the range of problems that aggregated
computing resources can tackle.

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Large-scale data analysis:
Many interesting scientific problems require the analysis of large
amounts of data.
For such problems, harnessing distributed computing and
storage resources is clearly of great value.
Furthermore, the natural parallelism inherent in many data
analysis procedures makes it feasible to use distributed resources
efficiently.
For various technical and political reasons, assembling these
resources at a single location appears impractical. Yet the
collective institutional and national resources of the hundreds of
institutions participating in those experiments can provide these
resources. These communities can, furthermore, share more than
just computers and storage. They can also share analysis
procedures and computational results.

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Computer-in-the-loop instrumentation:
Scientific instruments such as telescopes, synchrotrons, and
electron microscopes generate raw data streams that are archived
for subsequent batch processing.
But quasi-real-time analysis can greatly enhance an instrument’s
capabilities.
For example, consider an astronomer studying solar flares with a
radio telescope array. The deconvolution and analysis algorithms
used to process the data and detect flares are computationally
demanding. Running the algorithms continuously would be
inefficient for studying flares that are brief and sporadic.
But if the astronomer could call on substantial computing
resources (and sophisticated software) in an on-demand fashion, he
or she could use automated detection techniques to zoom in on
solar flares as they occurred.

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Collaborative work:
Researchers often want to aggregate not only
data and computing power but also human
expertise.
Collaborative problem formulation, data analysis,
and the like are important Grid applications.
For example, an astrophysicist who has
performed a large, multiterabyte simulation might
want colleagues around the world to visualize the
results in the same way and at the same time so
that the group can discuss the results in real time.

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GRID ARCHITECTURE

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GRID ARCHITECTURE ……….
At the lowest level, the fabric, we have the physical
devices or resources that Grid users want to share
and access, including computers, storage systems,
catalogs, networks, and various forms of sensors.

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The resource layer contains protocols that exploit communication
and authentication protocols to enable the secure initiation,
monitoring, and control of resource-sharing operations.
Running the same program on different computer systems
depends on resource layer protocols.
The Globus Toolkit is a commonly used source of connectivity
and resource protocols and APIs.

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The collective layer contains protocols, services, and APIs that
implement interactions across collections of resources.
Because they combine and exploit components from the
relatively narrower resource and connectivity layers, the
components of the collective layer can implement a wide variety
of tasks without requiring new resource-layer components.
Examples of collective services include
directory and brokering services for resource discovery and
allocation;
monitoring and diagnostic services;
data replication services; and
membership and policy services for keeping track of who in
a community is allowed to access resources.

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At the top of any Grid system are the user applications, which are
constructed in terms of, and call on, the components in any other
layer.
For example, a high-energy physics analysis application that needs
to execute several thousands of independent tasks, each taking as
input some set of files containing events, might proceed by
obtaining necessary authentication credentials ;
querying an information system and replica catalog to determine
availability of services;
submitting requests to appropriate computers, storage systems, and
networks to initiate computations, move data, and so forth (resource
protocols); and
monitoring the progress of the various computations and data
transfers, notifying the user when all are completed, and detecting and
responding to failure conditions (resource protocols).

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AUTHENTICATION, AUTHORIZATION
AND POLICY
In Grid environments, the situation is more complex. The distinction
between client and server tends to disappear, because an individual
resource can act as a server one moment (as it receives a request)
and as a client at another (as it issues requests to other resources).
Managing that kind of transaction turns out to have a number of interesting
requirements, such as:
Single sign-on
Mapping to local security mechanisms
Delegation
Community authorization and policy

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AND POLICY

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AND POLICY
Single sign-on:
A single computation may entail access to many resources, but
requiring a user to re-authenticate on each occasion (by, e.g., typing
in a password) is impractical and generally unacceptable.
Instead, a user should be able to authenticate once and then
assign to the computation the right to operate on his or her behalf,
typically for a specified period.
This capability is achieved through the creation of a proxy
credential.

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In Figure, the program run by the user (the user proxy)
uses a proxy credential to authenticate at two different
sites.
These services handle requests to create new processes.

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AND POLICY
Mapping to local security mechanisms:
Different sites may use different local security solutions, such as
Kerberos and Unix.
A Grid security infrastructure needs to map to these local solutions at
each site, so that local operations can proceed with appropriate
privileges.
In Figure, processes execute under a local ID and, at site A, are assigned a
Kerberos ‘ticket,’ a credential used by the Kerberos authentication system to
keep track of requests.

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AND POLICY
Delegation:
The creation of a proxy credential is a form of delegation, an
operation of fundamental importance in Grid environments.
A computation that spans many resources creates sub-computations
(subsidiary computations) that may themselves generate requests to
other resources and services, perhaps creating additional sub-
computations, and so on.

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AND POLICY
In Figure, the two sub-computations created at sites A and B both
communicate with each other and access files at site C.
Authentication operations – and hence further delegated credentials
– are involved at each stage, as resources determine whether to grant
requests and computations determine whether resources are
trustworthy.
The further these delegated credentials are disseminated, the
greater the risk that they will be acquired and misused by an
adversary. These delegation operations and the credentials that enable
them must be carefully managed.

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Community authorization and policy:
In a large community, the policies that govern who can use which
resources for what purpose cannot be based directly on individual
identity.
It is infeasible for each resource to keep track of community
membership and privileges.
Instead, resources (and users) need to be able to express policies in
terms of other criteria, such as group membership, which can be
identified with a cryptographic credential issued by a trusted third
party.
AND POLICY

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In the scenario depicted in Figure, the file server at site C must know
explicitly whether the user is allowed to access a particular file. A
community authorization system allows this policy decision to be
delegated to a community representative.

3. the grid new infrastructure

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