Grid computing 12

GRID COMPUTING
-AN APPROACH FOR ENGINEERING APPLICATIONS
With a case study on management issues
And
Weather forecasting
Authors
Haripriya.R.S
Pranavi.S
haripriya271094@gmail.com
susilapranavi@gmail.com
Velalar College of Engineering and Technology
Erode.

GRID COMPUTING
--An approach for engineering applications
ABSTRACT
“Grid” computing has emerged as an important new field, distinguished from conventional
distributed computing by its focus on large-scale resource sharing, innovative applications, and
in some cases, high-performance orientation. Grid Computing has the design goal of solving
problems too big for any single supercomputer, whilst retaining the flexibility to work on
multiple smaller problems. We provide a motivation for Grid Computing based on a vision to
enable a collaborative research environment. Our vision goes beyond the connection of
hardware resources. We argue that with an infrastructure such as the Grid, new modalities for
collaborative research are enabled. We provide an overview showing why Grid research is
difficult, and we present a number of management related issues that must be addressed to
make Grids a reality. We encounter unique authentication, authorization, and resource access,
resource discovery, and other challenges. It is this class of problem that is addressed by Grid
technologies. We have also provided extended view over the Grid-like scenario over the
weather prediction.

CONTENTS
 INTRODUCTION
 PROBING INTO GRID COMPUTING
 WHY DO WE NEED GRID COMPUTING
 ASPECTS OF GRID COMPUTING
 TYPES OF GRID
 HOW GRID COMPUTING WORKS
 GRID ARCHITECTURE
 GRID APPLICATION AREAS
 ADVANTAGES OF GRID COMPUTING
 LIMITATIONS OF GRID COMPUTING
 A CASE STUDY
 GRID MANAGEMENT FACETS
 CONCLUSION
 APPENDIX

INTRODUCTION
Grid computing is an emerging computing model that provides the ability to perform
higher throughput computing by taking advantage of many networked computers to model a
virtual computer architecture that is able to distribute process execution across a parallel
infrastructure. Grids provide the ability to perform computations on large data sets, by breaking
them down into many smaller ones.
Grid Computing is a technique that uses the resources of many separate computers
connected by a network (usually the internet) to solve large-scale computation problems. Grid
Computing is 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 user’s quality of service
requirements.
The term “GRID” is an analogy to the electric power grid that allows pervasive access to
electric power. In a similar fashion, computing Grid provide access to pervasive collections of
compute resources in a distributed fashion. Besides supercomputers and compute pools, Grids
include access to information resources (such as large scale database) and access to knowledge
resources (such as collaboration between colleagues).
Probing into Grid Computing
Grid Computing is a hardware and software infrastructure that clusters and integrates high-
end computers, networks, databases and scientific instruments from multiple sources to form a
virtual supercomputer, which users can work collaboratively.
A Grid Computer is nothing else than a multiple number of the same class of computers
clustered together. Often the connecting technology is called clustering. Internally a grid
computer is connected through a super fast network and shares other devices such as disk
drives, printers, mass storage and mass memory. A sophisticated operating system takes care
for the sharing in computing and processing.
Machines in Grid Computing cost a fraction of what a super computer cost. If we use
microcomputers to combine into a grid, we will have a little less power, but by adding more
members we can generate computing power way beyond the limits of the individual machines.

WHY DO WE NEED GRID COMPUTING
Grid Computing makes large scale computing a commodity that anyone can buy and
anyone can use. It enables “coordinated resource and problem solving in dynamic, multi-
institutional virtual organizations”. Grid Computing also enables new classes of application,
new services and new products. It makes access to remote resources as straight forward as
access to local resources like Data, Computation, Services and Devices.
Grid computing offers a model for solving massive computational problems by making
use of the unused resources (CPU cycles and/or disk storage) of large number of disparate,
often desktop computers treated as a virtual cluster embedded in a distributed
telecommunications infrastructure.
Grids offer a way to solve Grand Challenge problems like protein folding, financial
modeling, earthquake simulation, climate/weather modeling etc. Grids offer a way of using the
information technology as a utility bureau for commercial and non-commercial clients, with
those clients paying only for what they use, as with electricity or water.
Thus grid computing provides a multi-user environment. Its secondary aims are: better
exploitation of available computing power, and catering for the intermittent demands of large
computational exercises. This implies the use of secure authorization technique to allow
remote users to control computing resources.
Grid Computing involves sharing heterogeneous resources (based on different platforms,
hardware/software architectures, and computer languages), located in different places
belonging to different administrative domains over a network using open standards. In short, it
involves virtualizing computing resources.

ASPECTS OF GRID COMPUTING
Data:
The data aspects of any grid computing must be able to effectively manage all
aspects of data, including date location, data transfer, data access, and critical aspects of
security. The core functional data requirements or Grid Computing applications are:
 The ability to integrate multiple distributed, heterogeneous, and independently managed
data sources.
 The ability to provide efficient date transfer mechanisms and to provide data where the
computation will take place for better scalability and efficiency.
 The ability to provide date caching and/or replication mechanisms to minimize network
traffic.
 The ability to provide necessary date discovery mechanisms, which allows the user to
find data based on the characteristics of the data.
 The capability to implement data encryption and integrity checks to ensure that data is
transported across the network in a secure fashion.
 The ability to provide the backup/restore mechanisms and policies necessary to prevent
data loss and minimize unplanned downtime across grid.
Computation:
The core functional computational requirements for grid applications are:
 The ability to allow for independent management of computing resources.
 The ability to provide mechanisms that can intelligently and transparently select
computing resources capable of running a user’s job.
 The understanding of current and predicted loads on grid resources, resources
availability, dynamic resource configuration and provisioning.
 Failure detection and fail-over mechanisms.

 Ensure appropriate security mechanisms for secure resource management, access and
integrity.
TYPES OF GRIDS:
Grid Computing can be used in variety of ways to address various kinds of application
requirements. Often grids are categorized by the type of solutions that they best address. 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.
Computational Grid:
A Computation Grid is focused on setting aside resources specifically for computing
power. In this type of grid most of the machines are high-performance servers.
Scavenging Grid:
A Scavenging the Grid is most commonly used with large number of desktop
machines. Machines are scavenged for 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.
Data Grid:
A Data Grid is responsible for housing and providing access to data across
multiple organizations. Users are not connected with where this data is located as long as they
have access to data. For example, we may have two universities doing life science research,
each with unique data. A data grid would allow them to share the data, manage the data, and
manage security issues such as who has access to what data.
HOW GRID COMPUTING WORKS:
A Grid Computer can be looked as if a million people can calculate faster
together than doing the calculations all on them individually. The image below is a very
simplified example of this principle.

[Fig1: Principle of Grid Computing. (Refer Appendix-I)]
Each red dot represents a computer. Each computer has one single task: Adding two
numbers. The trick is while adding two numbers and passing that to the next row (2+2) the
first row can do a new calculation again while the other is busy.
And as you see the final answer does not have to be computed by one single computer. In
principles this is how supers and all other parallel computers work too.
In the global computing scenario, unused processing power on local clusters of
computers scattered across the Internet would be harnessed to address a single, complex
application.
[Fig2: Working of Grid Computing (Refer Appendix-I)]
GRID ARCHITECTURE:
The architecture of grid is often described in terms of “LAYERS” each
providing a specific function. In general, the higher layers are focused on the user (user-
centric) whereas the lower layers are more focused on computers and networks (hardware-
centric).
[Fig3: GRID ARCHITECTURE (Refer Appendix-II)]
NETWORK AND RESOURCE LAYER:
At the base of everything, the bottom layer is the network, which assures the
connectivity for the resources in the Grid. On top of it lies the resource layer, made up of the
actual resources that are part of the Grid, such as computers, storage systems, electronic data
catalogues, and even sensors such as telescopes or other instruments, which can be connected
directly to the network.
MIDDLE LAYER:
The Middle layer provides the tools that enable the various elements (servers,
storage, networks etc.) to participate in a unified Grid environment. The middleware layer can
be thought of as the intelligence that brings the various elements together – the “brain” of the
Grid.

APPLICATION LAYER:
The highest layer of the structure is the application layer, which includes all
different user applications (science, engineering and business) portals and development toolkits
supporting the applications. This is layer that users of the grid will “see”.
In most common Grid architectures, the application layer also provides the so-called
service ware, the sort of general management functions such as measuring the amount a
particular user employs the Grid, billing for this use (assuming a commercial model) and
generally keeping accounts of who is providing resources and who is using them – an
important activity when sharing the resources of a variety of institutions amongst large number
of different users. The service ware is the top layer, because it is something the user actually
interacts with, whereas the middleware is a “hidden” layer that the user should not have to
worry about.
The term “Fabric” is used to describe all the physical infrastructure of the Grid,
including computers and communication protocols, and a higher layer of collective services.
Resource and connectivity protocols handle all “Grid specific” network transactions
between different computers and other resources on the Grid. Remember that the network used
by the grid is the Internet. A myriad of transactions is going on at any instant on the Internet,
and computers that are actively contributing to the Grid have to be able to recognize those
messages that are relevant to them, and filter out the rest. This is done with Communication
protocols, which let the resources speak to each other, enabling exchange of data, and
authentication protocols, which provide secure mechanisms for verifying the identity of both
users and resources.
The collective services are also based on the information protocols, which obtain information
about the structure and state of the resources on the Grid, and management protocols, which
negotiate access to resources in a uniform way.
The Services include:
 Keeping directions of available resources updated at all times.
 Brokering resources (which like stock brokering, is about negotiating
 Between those who want to “buy” resources and those who wants to
sell)

 Monitoring and diagnosing problems on the Grid.
 Replicating key data so that multiple copies are available at different
locations for ease of use.
 Provides membership/policy services for keeping track on the Grid of
who is allowed to do what.
In all schemes, the topmost layer is the application layer. Applications rely on
all the other layers below them in order to run on the Grid. To take a fairly concrete example:
consider a user application that needs to analyze data contained in several independent files.
IT WILL HAVE TO:
• Obtain the necessary authentication credentials to open the files (resource and
connectivity protocols)
• Query on information system and replica catalogue to determine where copies of the
files in question can currently be found on the Grid, as well as where computational
resources to do the data analysis are most conveniently located (collective services).
• Submit requests to the fabric – the appropriate computers, storage systems, and
networks – to extract the data, initiate computations, and provide the results (resources
and connectivity protocols)
• Monitor the progress of the various computations and data transfers, notifying the user
when the analysis is complete, and detecting and responding to failure conditions
(collective services).
In order to do all the above, it is clear that an application that a user may have written
to run on a stand-alone PC will have to be adapted in order to invoke all the right services and
use all the right protocols. The grid will require the users to invest some effort into
“gridifying” their application. Once gridified, thousands of people will be able to use the same
application and run it trouble-free .
GRID APPLICATION AREAS:
Many organizations have started identifying the major business areas for Grid
Computing business applications. Some examples of major business areas include:

 Life Sciences, for analyzing and decoding strings of biological and chemical
information
 Financial service, for running long, complex financial models and arriving at more
accurate decisions.
 Higher education for enabling advanced, data and computation-intensive research.
 Engineering services, including automotive and aerospace, for collaborative design and
data-intensive testing.
 Government, for enabling seamless collaboration and agility on both civil and military
departments and other agencies.
 Collaborative games for replacing the existing single-server games with more highly
parallel, massively multiplayer online games.
ADVANTAGES OF GRID COMPUTING:
• Cheap (compared to super computers)
• CPU – intensive tasks can be processed (e.g. better weather forecast)
• Can solve larger more complex problems in shorter time
• Easier to collaborate with other organizations
• Makes better use of existing hardware.
LIMITATIONS OF GRID COMPUTING:
o Complex software needed for administration of the grid, distribution of
tasks to the computers attached to the net.
o Not all tasks are suitable for grids.
o Grid software and standards are still evolving.
VISION FOR OPEN INTERNATIONAL SCIENTIFIC
COLLABORATORY-A CASE STUDY.
First, we identify what motivates us to develop a Grid approach. We simplify our
presentations by providing an example for a particular scientific domain, meteorology. The

ingredients for an accurate weather prediction are a model allowing calculations based on
observations for the upcoming weather. L.F.Richard expressed the first modern vision for
numerical weather predictions in 1922. Within two decades, the first prototype of a predictive
system was implemented by Von Neumann, Charney, and others on the first generation of
computers. With the increased power of computers numerical weather prediction became a
reality in the 1960s and initiated a revolution in the field that we are still experiencing today.
But what vision promotes us a Grid-like scenario for weather prediction?
In contrast to these early weather prediction models, today the scientific communities
understand that complex chemical processes and their interactions with land and sea have to be
considered. The information based on observations is still incomplete and international efforts
are under way to improve this situation. Thus, we see that one of the ingredients for a
successful weather forecast is a sophisticated sensor network.
An other important ingredient is an accurate model. A group of interdisciplinary
scientists is necessary to derive such models while sharing the intellectual property of their
contributions with the community. A third ingredient is a high-end distributed computer. We
believe that although today’s supercomputers offer enormous power, predictive climate and
weather modeling will require distributed computing, exploiting diverse computational
resources at dispersed locations. The result is delivered appropriately to consumers. Thus, we
have identified the need for an infrastructure that allows us to create a dynamic, dispersed set
of sensor, data, compute, collaboration and community to formulate forecasts as a collaborative
and interdisciplinary effort while providing proper delivery to consumers. In summary, the
Grid approach promotes a vision for sophisticated international scientific and business oriented
co laboratories.
Historical Perspective: Making the Vision a Reality
When we look at why it is now possible to develop very sophisticated forecast models,
we see an increase in understanding, capacity, capability and accuracy in all levels of our
infrastructure. Clearly, technology has advanced dramatically. Sensor
Infrastructure measure data for the input in prediction. Models has expanded from temperature
measurements on the surface to Doppler radar, weather balloons, and

Weather satellites and many more improvements are underway to improve further coverage
and accuracy.
Communications satellites and the Internet enable remote access to regional and
international databases collecting the weather measurements. Collaborative infrastructures as
the access Grid have moved exchange of information beyond the desktop. These advances
have profoundly affected the way scientists work with each other. Compute power also has
steadily increased. Indeed, as for more than three decades, computer speed has doubled every
18 months and this trend is expected to last at least for the next decade. Further more, over the
past five years, network bandwidth has increased at a much larger rate, leading experts to
believe that the network speed doubles every nine months. At the same time, the cost of
production for network and computer hardware is decreasing.
Besides the increasing capability, we also observe a change in modality of computer
operations. The first generation of super computing enterprise was concerned mostly with the
development of high-end mainframes, vector processors and parallel computers. Access to this
expensive infrastructure was provided and controlled as part of a single institution within a
single administrative domain. With the advent of the network technologies, promoting
connectivity between computers, and the creation of the Internet, promoting connectivity
between different organizations, we observe a trend leading away from the centralized
computing center to a decentralized environment.
As part of this trend, it was natural to collect geographically dispersed and possibly
heterogeneous computer resources, typically as networks of workstations or supercomputers.
The first connections between high-end computers to solve a problem in parallel on these
machines were termed a Meta Computer. This Meta Computing is the ancestor of Grid
Computing.
[Fig4: Ingredients for a successful Weather Forecasting (Refer Appendix-II)]
[Fig5: Weather forecasting is a complex process that requires a complex
infrastructure (Refer Appendix-II)]
GRID MANAGEMENTS FACETS:
Reviewing FIG5: we observe that the envisioned Grid infrastructure raises significant
control issues. For example, we have to deal with the control of communities, information,

data, tasks, hardware, services and application or software. Within each of these management
challenges, we find a number of issues that must be addressed such as security, heterogeneity,
quality, distribution, disparity, dynamicity, unpredictability and compatibility. In each case we
must consider the dynamic, unpredictable properties of the Grid, while at the same time try to
provide a reliable and persistent infrastructure.
Additionally, we want to enable open collaborations, while at the same time protect the
collaboration with appropriate security restrictions. In order for grids to become a reality, we
must develop infrastructures, frameworks, and tools that address these challenges. Several
state-of-the-art projects try to provide solutions to a subset of these issues.
Security Management:
Grid approach deals with heterogeneous and dispersed resources and services. Security
services available today enable the interaction between two peers.
Authentication:
Deals with the verification of the identity of an entity within the grid.
Authorization:
Deals with the verification of an action an entity can perform after authentication was
successfully performed.
Non-repudiation:
Deals with issues that provide data or message integrity, such as verifying that data was not
changed accidentally or maliciously during message trans-mission.
Grid Software Management:
Must generate interoperability between versions of software and libraries on already
installed and operational software and services.

Grid Hardware Management:
Resource provides are responsible for hardware management. Notifications about
downtimes and maintenance upgrades must be available through the information service in
order to simplify finding suitable resources with service guarantees to the user.
Grid Data Management:
A reliable file transfer service must be provided to move the data between
source and destination on behalf of the issuing client. To reduce the amount of data during a
transfer, appropriate filters may be needed.
CONCLUSION:
We are in the infancy of Grid Computing. We have identified many management
related aspects of Grids and have posed some of the problems emerging Grids will face.
Solutions do exist for a subset of these problems, but much remains in developing Grids and
making the vision a reality. In particular, in addition of the development of Grid middleware,
interfaces are needed that can be used by the scientists to access Grids. With grid computing,
an organization can transform its distributed and difficult-to-manage systems into a large
virtual computer that can be set loose on problems and processes too complex for a single
computer to handle efficiently. The problems to be solved can involve data processing,
network bandwidth, or data storage. One of the most important issues grid computing addresses
for businesses is utilization of existing resources. Companies have made significant
investments in computing capacity, but much of it sits idle up to 90 percent of the time. Grid
computing can help these businesses connect those under-utilized assets, harness their
collective power, and manage them like a single large computer. Portals also must be
developed to hide the complex infrastructure of Grids and allow non-expert scientists using this
powerful infrastructure. Besides the technical problems, we also must address the sociological
aspects. We believe that the development of open standards, open source, and open
communities is essential if we wish to make the Grid a reality.

APPENDIX - I
Fig1: Principle of Grid Computing.
Fig2: Working of Grid Computing

APPENDIX – I I
Fig3: GRID ARCHITECTURE
Fig4: Ingredients for a successful Weather Forecasting
Fig5: Weather forecasting is a complex process that requires a complex infrastructure
A
SOPHISTICAT
ED NETWORK
GRID COMPUTING
ACCURATE
MODELS
HIGH-END
DISTRIBUTED
COMPUTERS

Grid computing 12

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