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TREND ON ADVANCED GRID COMPUTING
1. ABSTRACT
Grid computing is a method of harnessing the power of many computers in a network to solve problems requiring a large number of processing cyc1es and involving huge amounts of data. The grid computing helps in exploiting underutilized resources, achieving parallel CPU capacity; provide virtual resources for collaboration and reliability. Although commercial and research organizations might have collaborative or monetary reasons to share resources, they are unlikely to adopt such a distributed infrastructure until they can rely on the confidentiality of the communication, the integrity of their data and resources, and the privacy of the user information. In other words, large-scale deployment of grids will occur when users can count on their security.
Most organizations today deploy firewalls around their computer networks to protect their sensitive proprietary data. But the central idea of grid computing-to enable resource sharing makes mechanisms such as firewalls difficult to use. On the grid, participants form virtual organizations dynamically, and the trust established prior to such collaborations often takes place at the organizational rather than the individual level. Thus, expressing restrictive policies on a user-by-user basis often proves difficult. Also, frequently a single transaction takes place across many grid nodes that are dynamic and unpredictable. Finally, unlike the Internet, a grid gives outsiders complete access to a resource, thus increasing the security risk. Grid security is a multidimensional problem. Organizations participating in grids must use appropriate policies, such as firewalls, to harden their Infrastructures while enabling interaction with outside resources.
In this paper, we briefly describe the reasons for using grid computing and analyze the unique security requirements of large-scale grid computing. We propose a security policy for grid systems that addresses requirements for single sign-on, interoperability with local policies, and dynamically varying resource requirements. This policy focuses on authentication of users, resources, and processes and supports user-to resource, resource to user, process-to-resource, and Process to process authentication. We also describe security architecture and associated protocols that implement this policy.
2. INTRODUCTION
Grid Computing is a method of harnessing the power of many computers in a network to solve problems requiring a large number of processing cycles and involving huge amounts of data. Grid applications are distinguished from traditional client server applications by their simultaneous use of large numbers of resources, dynamic resource requirements, use of resources from multiple administrative domains, complex communication structures and stringent performance requirements, among others.
While scalability, performance and heterogeneity are desirable goals for any distributed system, the characteristics of computational grids lead to security problems that are not addressed by existing security technologies for distributed systems. For example parallel computations that acquire multiple computational resources introduce the need to establish security relationships not simply between a client and a server, but among potentially hundreds of processes that collectively span many administrative domains. Further more, the dynamic nature of grid can make it impossible to establish trust relationships between sites prior to application execution. Finally, by inter domain security solutions used for grids must be able to inter operate with, rather than replace, the diverse intra domain access control technologies inevitable encountered in individual domains.
In this paper, we describe new techniques that overcome many of the cited difficulties. We propose a security policy for grid systems that addresses requirements for single sign-on, inter operability with local policies, and dynamically varying resource requirements. This policy focuses on authentication of users, resources, and processes and supports user-to-resource, resource-to-user, process-to-resource, and process-to-process authentication.
2. Reasons for using Grid Computing
When you deploy a grid, it will be to meet a set of customer requirements. To better match grid computing capabilities to those requirements, it is useful to keep in mind the reasons for using grid computing.
Exploiting underutilized resources
The easiest use of grid computing is to run an existing application on a different machine. The machine on which the application is normally run might be unusually busy due to an unusual peak in activity. The job in question could be run on an idle machine elsewhere on the grid. There are at least two prerequisites for this scenario. First, the application must be executable remotely and without undue overhead. Second, the remote machine must meet any special hardware, software, or resource requirements imposed by the application.
For example, a batch job that spends a significant amount of time processing a set of input data to produce an output set is perhaps the most ideal and simple use for a grid. If the quantities of input and output are large, more thought and planning might be required to efficiently use the grid for such a job. It would usually not make sense to use a word processor remotely on a grid because there would probably be greater delays and more potential points of failure.
In most organizations, there are large amounts of underutilized computing resources. Most desktop machines are busy less than 5 percent of the time. In some organizations, even the server machines can often be relatively idle. Grid computing provides a framework for exploiting these underutilized resources and thus has the possibility of substantially increasing the efficiency of resource usage.
The processing resources are not the only ones that may be underutilized. Often, machines may have enormous unused disk drive capacity. Grid Computing, more specifically, a “data grid”, can be used to aggregate this unused storage into a much larger virtual data store, possibly configured to achieve improved performance and reliability over that of any single machine.
If a batch job needs to read a large amount of data, this data could be automatically replicated at various strategic points in the grid. Thus, if the job must be executed on a remote machine in the grid, the data is already there and does not need to be moved to that remote point. This offers clear performance benefits. Also, such copies of data can be used as backups when the primary copies are damaged or unavailable.
Parallel CPU capacity
The potential for massive parallel CPU capacity is one of the most attractive features of a grid. In addition to pure scientific needs, such computing power is driving a new evolution in industries such as the bio-medical field, financial modeling, oil exploration, motion picture animation, and many others.
The common attribute among such uses is that the applications have been written to use algorithms that can be partitioned into independently running parts. A CPU intensive grid application can be thought of as many smaller “sub jobs,” each executing on a different machine in the grid. To the extent that these sub jobs do not need to communicate with each other, the more “scalable” the application becomes. A perfectly scalable application will, for example, finish 10 times faster if it uses 10 times the number of processors.
Barriers often exist to perfect scalability. The first barrier depends on the algorithms used for splitting the application among many CPUs. If the algorithm can only be split into a limited number of independently running parts, then that forms a scalability barrier. The second barrier appears if the parts are not completely independent; this can cause contention, which can limit scalability.
For example, if all of the sub jobs need to read and write from one common file or database, the access limits of that file or database will become the limiting factor in the application’s scalability. Other sources of inter-job contention in a parallel grid application include message communications latencies among the jobs, network communication capacities, synchronization protocols, input-output bandwidth to devices and storage devices, and latencies interfering with real-time requirements. Other sources of inter-job content in parallel grid application include message communications latencies among the jobs.
Virtual resources and Virtual Organizations for Collaborations
Another important grid computing contribution is to enable and simplify collaboration among a wider audience. In the past, distributed computing promised this collaboration and achieved it to some extent. Grid computing takes these capabilities to an even wider audience, while offering important standards that enable very heterogeneous systems to work together to form the image of a large virtual computing system offering a variety of virtual resources. The users of the grid can be organized dynamically into a number of virtual organizations, each with different policy requirements. These virtual organizations can share their resources collectively as a larger grid.
Sharing starts with data in the form of files or databases. A “data grid” can expand data capabilities in several ways. First, files or databases can seamlessly span many systems and thus have larger capacities than on any single system. Such spanning can improve data transfer rates through the use of striping techniques. Data can be duplicated throughout the grid to serve as a backup and can be hosted on or near the machines most likely to need the data, in conjunction with advanced scheduling techniques.
Sharing is not limited to files, but also includes many other resources, such as equipment, software, services, licenses, and others. These resources are “virtualized” to give them a more uniform interoperability among heterogeneous grid participants.
Reliability
High-end conventional computing systems use expensive hardware to increase reliability. They are built using chips with redundant circuits that vote on results, and contain much logic to achieve graceful recovery from an assortment of hardware failures. The machines also use duplicate processors with hot plug ability so that when they fail, one can be replaced without turning the other off. Power supplies and cooling systems are duplicated. The systems are operated on special power sources that can start generators if utility power is interrupted. All of this builds a reliable system, but at a great cost, due to the duplication of high-reliability components.
In the future, we will see a complementary approach to reliability that relies on software and hardware. A grid is just the beginning of such technology. The systems in a grid can be relatively
Inexpensive and geographically dispersed. Thus, if there is a power or other kind of failure at one location, the other parts of the grid are not likely to be affected. Grid management software can automatically resubmit jobs to other machines on the grid when a failure is detected. In critical, real-time situations, multiple copies of the important jobs can be run on different machines throughout the grid. Their results can be checked for any kind of inconsistency, such as computer failures, data corruption, or tampering. Such grid systems will utilize “autonomic computing.” This is a type of software that automatically heals problems in the grid, perhaps even before an operator or manager is aware of them. In principle, most of the reliability attributes achieved using hardware in today’s high availability systems can be achieved using software in a grid setting in the future.
Resource balancing
A grid federates a large number of resources contributed by individual machines into a greater total virtual resource. For applications that are grid-enabled, the grid can offer a resource balancing effect by scheduling grid jobs on machines with low utilization. This feature can prove invaluable for handling occasional peak loads of activity in parts of a larger organization. This can happen in two ways: An unexpected peak can be routed to relatively idle machines in the grid and if the grid is already fully utilized, the lowest priority work being performed on the grid can be temporarily suspended or even cancelled and performed again later to make room for the higher priority work.
Without a grid infrastructure, such balancing decisions are difficult to prioritize and execute. Occasionally, a project may suddenly rise in importance with a specific deadline. A grid cannot perform a miracle and achieve a deadline when it is already too close. However, if the size of the job is known, if it is a kind of job that can be sufficiently split into sub jobs, and if enough resources are available after preempting lower priority work, a grid can bring a very large amount of processing power to solve the problem. In such situations, a grid can, with some planning, succeed in meeting a surprise deadline.
3. Security in Grid Computing
a. The Grid Security Problem
We introduce of grid security problem with and example illustrated in figure1. We imagine a scientist, a member of a multi-institutional scientific collaboration, who receives e-mail from a colleague regarding a new data set. He starts an analysis program, which dispatches code to the remote location where the data is stored (site C). Once started, the analysis program determines that it needs to run a simulation in order to compare the experimental results with predictions. Hence, it contacts a resource broker service maintained by the collaboration (at site D), in order to locate the resources that can be used for the simulation. The resource broker in turn initiates
Computation on computers at two sites (E and G).These computers access parameter values store on a File system at another site (F) and also communicate among themselves and with broker, the original site, And the user.
We imagine a scientist, a member of a multi-institutional scientific collaboration, who receives e-mail from a colleague regarding a new data set. He starts an analysis program, which dispatches code to the remote location where the data is stored (site C). Once started, the analysis program determines that it needs to run a simulation in order to compare the experimental results with predictions. Hence, it contacts a resource broker service maintained by the collaboration (at site D), in order to locate idle resources that can be used for the simulation. The resource broker in turn initiates computation on computers at two sites (E and G). These computers access parameter values stored on a file system at yet another site (F) and also communicate among themselves (perhaps using specified protocols, such as multicast) and with the broker, the original site, and the user.
This example illustrates many of the distinctive characteristics of the grid computing environment:
1. The user population is large and dynamic. Participants in such virtual organizations as this scientific collaboration will include members of many institutions and will change frequently.
2. The resource pool is large and dynamic. Because individual institutions and users decide whether and when to contribute resources, the quantity and location of available resources can change rapidly.
3. A computation may require, start processes on, and release resources dynamically during its execution. Even in our simple example, the computation required resources at five sites. In other words, throughout its lifetime, a computation is composed of a dynamic group of processes running on different resources and sites.
4. The processes constituting a computation may communicate by using a variety of mechanisms, including unicast and multicast. While these processes form a single logical entity, low-level communication connection may be created and destroyed dynamically during program execution.
5. Resources may require different authentication and authorization mechanisms and policies, which we will have limited ability to change. In figure 1, we indicate this situation by showing the local access control policies that apply at the different sites.
6. An individual user will be associated with different local name spaces, credentials, or accounts, at different sites, for the purposes of accounting and access control.
7. Resources and users may be located in different countries.

presented by:
Faisal N. Abu-Khzam
&
Michael A. Langston

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GRID COMPUTING
What is Grid Computing?
 Computational Grids
– Homogeneous (e.g., Clusters)
– Heterogeneous (e.g., with one-of-a-kind instruments)
 Cousins of Grid Computing
Methods of Grid Computing
Computational Grids
 A network of geographically distributed resources including computers, peripherals, switches, instruments, and data.
 Each user should have a single login account to access all resources.
 Resources may be owned by diverse organizations.
Computational Grids
 Grids are typically managed by gridware.
 Gridware can be viewed as a special type of middleware that enable sharing and manage grid components based on user requirements and resource attributes (e.g., capacity, performance, availability…)
Cousins of Grid Computing
 Parallel Computing
 Distributed Computing
 Peer-to-Peer Computing
 Many others: Cluster Computing, Network Computing, Client/Server Computing, Internet Computing, etc...
Distributed Computing
 People often ask: Is Grid Computing a fancy new name for the concept of distributed computing?
 In general, the answer is “no.” Distributed Computing is most often concerned with distributing the load of a program across two or more processes.
PEER2PEER Computing
 Sharing of computer resources and services by direct exchange between systems.
 Computers can act as clients or servers depending on what role is most efficient for the network.
Methods of Grid Computing
 Distributed Supercomputing
 High-Throughput Computing
 On-Demand Computing
 Data-Intensive Computing
 Collaborative Computing
 Logistical Networking
 Distributed Supercomputing
 Combining multiple high-capacity resources on a computational grid into a single, virtual distributed supercomputer.
 Tackle problems that cannot be solved on a single system.
High-Throughput Computing
 Uses the grid to schedule large numbers of loosely coupled or independent tasks, with the goal of putting unused processor cycles to work.
On-Demand Computing
 Uses grid capabilities to meet short-term requirements for resources that are not locally accessible.
 Models real-time computing demands.
Data-Intensive Computing
 The focus is on synthesizing new information from data that is maintained in geographically distributed repositories, digital libraries, and databases.
 Particularly useful for distributed data mining.
Collaborative Computing
 Concerned primarily with enabling and enhancing human-to-human interactions.
 Applications are often structured in terms of a virtual shared space.
Logistical Networking
 Global scheduling and optimization of data movement.
 Contrasts with traditional networking, which does not explicitly model storage resources in the network.
 Called "logistical" because of the analogy it bears with the systems of warehouses, depots, and distribution channels.
Who Needs Grid Computing?
 A chemist may utilize hundreds of processors to screen thousands of compounds per hour.
 Teams of engineers worldwide pool resources to analyze terabytes of structural data.
 Meteorologists seek to visualize and analyze petabytes of climate data with enormous computational demands.
An Illustrative Example
 Tiffany Moisan, a NASA research scientist, collected microbiological samples in the tidewaters around Wallops Island, Virginia.
 She needed the high-performance microscope located at the National Center for Microscopy and Imaging Research (NCMIR), University of California, San Diego.
 She sent the samples to San Diego and used NPACI’s Telescience Grid and NASA’s Information Power Grid (IPG) to view and control the output of the microscope from her desk on Wallops Island. Thus, in addition to viewing the samples, she could move the platform holding them and make adjustments to the microscope.
 The microscope produced a huge dataset of images.
 This dataset was stored using a storage resource broker on NASA’s IPG.
 Moisan was able to run algorithms on this very dataset while watching the results in real time.
 Grid Users
 Grid developers
 Tool developers
 Application developers
 End Users
 System Administrators
 Grid Developers
 Very small group.
 Implementers of a grid “protocol” who provides the basic services required to construct a grid.
Tool Developers
 Implement the programming models used by application developers.
 Implement basic services similar to conventional computing services:
– User authentication/authorization
– Process management
– Data access and communication
Tool Developers
 Also implement new (grid) services such as:
– Resource locations
– Fault detection
– Security
– Electronic payment
Application Developers
 Construct grid-enabled applications for end-users who should be able to use these applications without concern for the underlying grid.
 Provide programming models that are appropriate for grid environments and services that programmers can rely on when developing (higher-level) applications.
System Administrators
 Balance local and global concerns.
 Manage grid components and infrastructure.
 Some tasks still not well delineated due to the high degree of sharing required.
Some Highly-Visible Grids
 The NSF PACI/NCSA Alliance Grid.
 The NSF PACI/SDSC NPACI Grid.
 The NASA Information Power Grid (IPG).
 The Distributed Terascale Facility (DTF) Project.
DTF
 Currently being built by NSF’s Partnerships for Advanced Computational Infrastructure (PACI)
 A collaboration: NCSA, SDSC, Argonne, and Caltech will work in conjunction with IBM, Intel, Quest Communications, Myricom, Sun Microsystems, and Oracle.
DTF Expectations
 A 40-billion-bits-per-second optical network (Called TeraGrid) is to page link computers, visualization systems, and data at four sites.
 Performs 11.6 trillion calculations per second.
 Stores more than 450 trillion bytes of data.

Presented by:
E.Himaja
Y.N.Sowjanya

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ABSTRACT
Today we are in the Internet world and everyone prefers to enjoy fast access to the Internet. But due to multiple downloading, there is a chance that the system hangs up or slows down the performance that leads to the restarting of the entire process from the beginning. This is one of the serious problems that need the attention of the researchers.
So we have taken this problem for our research and in this paper we are providing a layout for implementing our proposed Grid Model that can access the Internet very fast. By using our Grid we can easily download any number of files very fast depending on the number of systems employed in the Grid. We have used the concept of Grid Computing for this purpose.
The Grid formulated by us uses the standard Globus Architecture, which is the only Grid Architecture currently used world wide for developing the Grid. And we have proposed an algorithm for laying our Grid Model that we consider as a blueprint for further implementation. When practically implemented, our Grid provides the user to experience the streak of lightening over the Internet while downloading multiple files.
Key words:
Grid Security Interface (GSI), Global Access to Secondary Storage (GASS), Monitoring and Discovery Service (MDS), Globus Resource Allocation Manager (GRAM).
INTRODUCTION :
What's Grid computing? Grid Computing is a technique in which the idle systems in the Network and their “ wasted “ CPU cycles can be efficiently used by uniting pools of servers, storage systems and networks into a single large virtual system for resource sharing dynamically at runtime. These systems can be distributed across the globe; they're heterogeneous (some PCs, some servers, maybe mainframes and supercomputers); somewhat autonomous (a Grid can potentially access resources in different organizations).
Although Grid computing is firmly ensconced in the realm of academic and research activities, more and more companies are starting to turn to it for solving hard-nosed, real-world problems.
IMPORTANCE OF GRID COMPUTING:
Grid computing is emerging as a viable technology that businesses can use to wring more profits and productivity out of IT resources -- and it's going to be up to you developers and administrators to understand Grid computing and put it to work.
It's really more about bringing a problem to the computer (or Grid) and getting a solution to that problem. Grid computing is flexible, secure, coordinated resource sharing among dynamic collections of individuals, institutions, and resources. Grid computing enables the virtualization of distributed computing resources such as processing, network bandwidth, and storage capacity to create a single system image, granting users and applications seamless access to vast IT capabilities. Just as an Internet user views a unified instance of content via the World Wide Web, a Grid user essentially sees a single, large, virtual computer.
Grid computing will give worldwide access to a network of distributed resources - CPU cycles, storage capacity, devices for input and output, services, whole applications, and more abstract elements like licenses and certificates.
For example, to solve a compute-intensive problem, the problem is split into multiple tasks that are distributed over local and remote systems, and the individual results are consolidated at the end. Viewed from another perspective, these systems are connected to one big computing Grid. The individual nodes can have different architectures, operating systems, and software versions. Some of the target systems can be clusters of nodes themselves or high performance servers.
TYPES OF GRID:
The three primary types of grids and are summarized below:
 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.
 Scavenging grid
A scavenging grid is most commonly used with large numbers 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 concerned with where this data is located as long as they have access to the data.
OUR PROPOSED GRID MODEL:
We are using the Scavenging Grid for our implementation as large numbers of desktop machines are used in our Grid and later planning to extend it by using both Scavenging and data Grid. Figure1 gives an idea about the Grid that we have proposed.
PROBLEMS DUE TO MULTIPLE DOWNLOADING:
While accessing Internet most of us might have faced the burden of multiple downloading and in particular with downloading huge files i.e., there can be a total abrupt system failure while a heavy task is assigned to the system. The system may hang up and may be rebooted while some percentage of downloading might have been completed. This rebooting of the system leads to download of the file once again from the beginning, which is one of the major problems everyone is facing today.
Let us consider N numbers of files of different sizes (in order of several MBs) are being downloaded on a single system (a PC). This will take approximately some minutes or even some hours to download it by using an Internet connection of normal speed with a single CPU. This is one of the tedious tasks for the user to download multiple files at the same time. Our Grid plays a major role here.
CONCEPT OF OUR PROPOSED GRID:
In order to avoid this problem we have formulated our own Grid for such an access to the Internet via an Intranet (LAN). By using our Grid these large numbers of files are distributed evenly to all the systems in the Network by using our Grid. For example we have taken into account of a small LAN that consists of around 20 systems out of which 10 systems are idle and 5 systems are using less amount of CPU(for our consideration) and their CPU cycles are wasted. And our work begins here, as we are going to efficiently utilize those “wasted CPU cycles” into “working cycles”.

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INTRODUCTION
Grid computing is a term referring to the combination of computer resources from multiple administrative domains to reach a common goal. The grid can be thought of as a distributed system with non-interactive workloads that involve a large number of files. What distinguishes grid computing from conventional high performance computing systems such as cluster computing is that grids tend to be more loosely coupled, heterogeneous, and geographically dispersed. Although a grid can be dedicated to a specialized application, it is more common that a single grid will be used for a variety of different purposes. Grids are often constructed with the aid of general-purpose grid software libraries known as middleware.
Grid size can vary by a considerable amount. 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. Furthermore, “distributed” or “grid” computing, in general, is a special type of parallel computing that relies on complete computers (with onboard CPUs, storage, power supplies, network interfaces, etc.) connected to a network (private, public or the Internet) by a conventional networkinterface, such as Ethernet. This is in contrast to the traditional notion of a supercomputer, which has many processors connected by a local high-speed compute bus
Grids enable the sharing, selection, and aggregation of a wide variety of resources including supercomputers, storage systems, data sources, and specialized devices that are geographically distributed and owned by different organizations for solving large-scale computational and data intensive problems in science, engineering, and commerce. Thus creating virtual organizations and enterprises as a temporary alliance of enterprises or organizations that come together to share resources and skills, core competencies, or resources in order to better respond to business opportunities or large-scale application processing requirements, and whose cooperation is supported by computer networks.
PC GRID COMPUTING
The cocept of grid derived from electric power grid and the terms refer to an environment in which various information processing resources(computer,storage,displays,experimental and observations equipment etc) distributed across the network are used as virtual computer.Grid computing aims to provide the necessary aount of processing resource to its operator,on demand. Its potential benefits as follows:
• Collection of distributed processing resources for centralized use.
• Effective utilsation of idle resource.
• Load balancing to eliminate the need to maintain the processing capacity to meet the peak load.
• Ensured fault tolerance for improved reliability
DIVISION OF GRID ACCORDING TO CONFIGURATION
1.COMPUTING GRID
A network of distributed high performance computers(e.g supercomputers) working like asingle huge computer.
2.PC GRID COMPUTING
A concept similar to the computing grid.Collecting the idle CPU power of numerous PC’s to perform large -scale processing.
3.DATA GRID COMPUTING
Making a grid of disk devices and file system that is remotely accessible through the network and works like a large external storage devices.
4.SENSOR GRID
A group of myriad of distributed and network sensors from which data can be collected for specific purpose such as global environment monitoring system.
MECHANISM OF OPERATION
A PC Grid computing work as follows:
1.All participating PC owners download special software from the web server and install it on their PC’s.
2.The special software request to the central server the application programs and the data that each PC is to process as part of the grid.
3.The central server transmits the parallel processing programs and the data to the PC’s divided into packages of appropriate size.
4.The PC’s run the received programs and data during their idle CPU time as their lowest priority task.
5.When the processing is complete ,the special software returns the results to the central server and request new data.(step 3 to 5 are pepat until the entire project is finished)
6.The central server collects and compile the results returned from participating PC’s into the final results.
CLASSIFICATION BY STRUCTURE
• OPEN STRUCTURE
This is the most common type of PC grid computing. This type of PC grid computing is connected to the internet and comprised of Pc owned by individuals who are willing to offer their Pc’s idle processing power. Such project sometimes involves a great many PC’s from a broad range of individuals. Since participation is essentially on voluntary basis, providing an incentive is the key to success. Recognizing that sending goods or real money to individual participants is not practical because of huge delivery cost, project operators are finding other cost-effective ways of rewarding the participants, such as sending electronic money, electronic mileage points or other incentive points over the network, or lottery systems.
Instead of giving such financial incentives ,other grid project operators choose to appeal to people’s volunteer spirit by emphazing the contribution to social welfare, the search for truth and contribution to human advancement. Such programs are sometimes called volunteer computing because participants offer their PCs extra power for free. A famous example is SETI@home, a project operated by university of California, Berkeley. It aims to search for extraterrestrial intelligence based on data collected with a Radio Telescope. More than five millions PC’s voluntarily participate in this project from around the world.The computing power is said to reach 100 TFlops which is almost comparable to the performance of IBM’s Blue Gene/L(140 TFlpos) the worlds largest supercomputer. There are many other open grid project as shown in table.
CLOSED STRUCTURE
PC grids in closed structure are constructed by business enterprises and other organization, based on their existing PC’s. Organisation can have high computing power at low cost, while effective using existing resources. The benefits of creating this type of PC grid include following. Once the organisation decide to launch a project, there is no need to consider incentives for participants: the state of participating PC’s can be monitored and managed with relative ease; and since the each participants ID is known, security risk are risks are better controlled than open structure. To construct a grid using PCs within a single building, an organization can purchase a software package that easily integrates PCs connected through a LAN into a grid.
SEMI-OPEN STRUCTURE
Where multiple organizations that may own many PCs,these organisation can build a single network that extends beyond their boundaries to achieve high computing power. Such a grids would have a semi-open structure and allow public organizations (municipal offices, schools etc.) and local business to jointly provide the local community with shared computing resources. This is a PC grid that makes computing with shared computing resources. This is a PC grid that makes computing resources available at low cost to local small- to- medium companies that hardly afford to use supercomputers .Even large companies can benefit from such a grid because owning expensive super-computers is not always an option. Universities and research institutes in the region also enjoy this benefit. When many regions are making a variety of efforts to enhance their information infrastructure, regionally based grid computing scheme would strengthen these efforts. This is the scheme of ‘of the region, by the region , for the region’.
An example of this type of grid project is a field experiment conducted in Gifu Prefecture in February 2005.Led by Gifu National college of Technology, universities, high schools, education boards, research institutes and other organizations in the prefecture participated in the project, offering over 1000 PCs. The experiment was designed to solve” the traveling salesman problem for 80 muncipalities in Gifu by using by using parallel by using parallel genetic algorithms. After the experiment the institutions involved expressed their expectations for the future if anundant computing resource were to become easily available, including reaserch project that would otherwise not be feasible, such as highly complex simulation .On the other hand, the experiment exposed social issues, such as whether each organisations rules permits its PCs time to be used for the purpose of other than the original intent and how to compensate for the difference in security policy among participating organizations.

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ABSTRACT
Grid computing can mean different things to different individuals. The grand vision is often presented as an analogy to power grids where users (or electrical appliances) get access to electricity through wall sockets with no care or consideration for where or how the electricity is actually generated. In this view of grid computing, computing becomes pervasive and individual users (or client applications) gain access to computing resources (processors, storage, data, applications, and so on) as needed with little or no knowledge of where those resources are located or what the underlying technologies, hardware, operating system, and so on are. Though this vision of grid computing can capture one’s imagination and may indeed someday become a reality, there are many technical, business, political, and social issues that need to be addressed. If we consider this vision as an ultimate goal, there are many smaller steps that need to be taken to achieve it. These smaller steps each have benefits of their own. Therefore, grid computing can be seen as a journey along a path of integrating various technologies and solutions that move us closer to the final goal. Its key values are in the underlying distributed computing infrastructure technologies that are evolving in support of cross-organizational application and resource sharing—in a word, virtualization—virtualization across technologies, platforms, and organizations. This kind of virtualization is only achievable through the use of open standards. Open standards help ensure that applications can transparently take advantage of whatever appropriate resources can be made available to them. An environment that provides the ability to share and transparently access resources across a distributed and heterogeneous environment not only requires the technology to virtualize certain resources, but also technologies and standards in the areas of scheduling, security, accounting, systems management, and so on.
CHAPTER 1
What grid Computing is
Grid computing can mean different things to different individuals. The grand vision is often presented as an analogy to power grids where users (or electrical appliances) get access to electricity through wall sockets with no care or consideration for where or how the electricity is actually generated. In this view of grid computing, computing becomes pervasive and individual users (or client applications) gain access to computing resources (processors, storage, data, applications, and so on) as needed with little or no knowledge of where those resources are located or what the underlying technologies, hardware, operating system, and so on are. Though this vision of grid computing can capture one’s imagination and may indeed someday become a reality, there are many technical, business, political, and social issues that need to be addressed. If we consider this vision as an ultimate goal, there are many smaller steps that need to be taken to achieve it. These smaller steps each have benefits of their own. Therefore, grid computing can be seen as a journey along a path of integrating various technologies and solutions that move us closer to the final goal. Its key values are in the underlying distributed computing infrastructure technologies that are evolving in support of cross-organizational application and resource sharing—in a word, virtualization—virtualization across technologies, platforms, and organizations. This kind of virtualization is only achievable through the use of open standards. Open standards help ensure that applications can transparently take advantage of whatever appropriate resources can be made available to them. An environment that provides the ability to share and transparently access resources across a distributed and heterogeneous environment not only requires the technology to virtualize certain resources, but also technologies and standards in the areas of scheduling, security, accounting, systems management, and so on.
Grid computing could be defined as any of a variety of levels of virtualization along a continuum. Exactly where along that continuum one might say that a particular solution is an implementation of grid computing versus a relatively simple implementation using virtual resources is a matter of opinion. But even at the simplest levels of virtualization, one could say that grid-enabling technologies are being utilized. This continuum is illustrated in Figure 1-1 on page 5. Starting in the lower left you see single system partitioning. Virtualization starts with being able to carve up a machine into virtual machines. As you move up this spectrum you start to be able to virtualize similar or homogeneous resources. Virtualization applies not only to servers and CPUs, but to storage, networks, and even applications. As you move up this spectrum you start to virtualize unlike resources. The next step is virtualizing the enterprise, not just in a data center or within a department but across a distributed organization, and then, finally, virtualizing outside the enterprise, across the Internet, where you might actually access resources from a set of OEMs and their suppliers or you might integrate information across a network of collaborators. Early implementations of grid computing have tended to be internal to a particular company or organization. However, cross-organizational grids are also being implemented and will be an important part of computing and business optimization in the future. The distinctions between interorganizational grids and interorganizational grids are not based in technological differences. Instead, they are based on configuration choices given: Security domains, degrees of isolation desired, type of policies and their scope, and contractual obligations between users and providers of the infrastructures. These issues are not fundamentally architectural in nature. It is in the industry’s best interest to ensure that there is not an artificial split of distributed computing paradigms and models across organizational boundaries and internal IT infrastructures. Grid computing involves an evolving set of open standards for Web services and interfaces that make services, or computing resources, available over the Internet. Very often grid technologies are used on homogeneous clusters, and they can add value on those clusters by assisting, for example, with scheduling or provisioning of the resources in the cluster. The term grid, and its related technologies, applies across this entire spectrum. If we focus our attention on distributed computing solutions, then we could consider one definition of grid computing to be distributed computing across virtualized resources. The goal is to create the illusion of a simple yet large and powerful virtual computer out of a collection of connected (and possibly heterogeneous) systems sharing various combinations of resources.
CHAPTER 2
Benefits of grid computing
When you deploy a grid, it will be to meet a set of business requirements. To better match grid computing capabilities to those requirements, it is useful to keep in mind some common motivations for using grid computing.
2.1 Exploiting underutilized resources:
The potential for massive parallel CPU capacity is one of the most common visions and attractive features of a grid. In addition to pure scientific needs, such computing power is driving a new evolution in industries such as the bio-medical field, financial modeling, oil exploration, motion picture animation, and many others.
The common attribute among such uses is that the applications have been written to use algorithms that can be partitioned into independently running parts. A CPU-intensive grid application can be thought of as many smaller subjobs, each executing on a different machine in the grid. To the extent that these subjobs do not need to communicate with each other, the more scalable the application becomes. A perfectly scalable application will, for example, finish in one tenth of the time if it uses ten times the number of processors. Barriers often exist to perfect scalability. The first barrier depends on the algorithms used for splitting the application among many CPUs. If the algorithm can only be split into a limited number of independently running parts, then that forms a scalability barrier. The second barrier appears if the parts are not completely independent; this can cause contention, which can limit scalability. For example, if all of the sub jobs need to read and write from one common file or database, the access limits of that file or database will become the limiting factor in the application’s scalability. Other sources of inter-job contention in a parallel grid application include message communications latencies among the jobs, network communication capacities, synchronization protocols, input-output bandwidth to stoage or other devices, and other delays interfering with real-time requirements.
There are many factors to consider in grid-enabling an application. One must understand that not all applications can be transformed to run in parallel on a grid and achieve scalability. Furthermore, there are no practical tools for transforming arbitrary applications to exploit the parallel capabilities of a grid. There are some practical tools that skilled application designers can use to write a parallel grid application. However, automatic transformation of applications is a science in its infancy. This can be a difficult job and often requires mathematics and programming talents, if it is even possible in a given situation. New computation-intensive applications written today are being designed for parallel execution, and these will be easily grid-enabled, if they do not already follow emerging grid protocols and standards.
2.2 Parallel CPU Capacity:
The potential for massive parallel CPU capacity is one of the most common visions and attractive features of a grid. In addition to pure scientific needs, such computing power is driving a new evolution in industries such as the bio-medical field, financial modeling, oil exploration, motion picture animation, and many others.
The common attribute among such uses is that the applications have been written to use algorithms that can be partitioned into independently running parts. A CPU-intensive grid application can be thought of as many smaller subjobs, each executing on a different machine in the grid. To the extent that these subjobs do not need to communicate with each other, the more scalable the application becomes. A perfectly scalable application will, for example, finish in one tenth of the time if it uses ten times the number of processors. Barriers often exist to perfect scalability. The first barrier depends on the algorithms used for splitting the application among many CPUs. If the algorithm can only be split into a limited number of independently running parts, then that forms a scalability barrier. The second barrier appears if the parts are not completely independent; this can cause contention, which can limit scalability. For example, if all of the subjobs need to read and write from one common file or database, the access limits of that file or database will become the limiting factor in the application’s scalability. Other sources of inter-job contention in a parallel grid application include message communications latencies among the jobs, network communication capacities, synchronization protocols, input-output bandwidth to storage or other devices, and other delays interfering with real-time requirements. There are many factors to consider in grid-enabling an application. One must understand that not all applications can be transformed to run in parallel on a grid and achieve scalability. Furthermore, there are no practical tools for transforming arbitrary applications to exploit the parallel capabilities of a grid. There are some practical tools that skilled application designers can use to write a parallel grid application. However, automatic transformation of applications is a science in its infancy. This can be a difficult job and often requires mathematics and programming talents, if it is even possible in a given situation. New computation-intensive applications written today are being designed for parallel execution, and these will be easily grid-enabled, if they do not already follow emerging grid protocols and standards
2.3 Virtual resources and virtual organizations for Collaboration
Another capability enabled by grid computing is to provide an environment for collaboration among a wider audience. In the past, distributed computing promised this collaboration and achieved it to some extent. Grid computing can take these capabilities to an even wider audience, while offering important standards that enable very heterogeneous systems to work together to form the image of a large virtual computing system offering a variety of resources, as illustrated in Figure 2-1 on page 11. The users of the grid can be organized dynamically into a number of virtual organizations, each with different policy requirements. These virtual organizations can share their resources collectively as a larger grid. Sharing starts with data in the form of files or databases. A data grid can expand data capabilities in several ways. First, files or databases can span many systems and thus have larger capacities than on any single system. Such spanning can improve data transfer rates through the use of striping techniques.
Data can be duplicated throughout the grid to serve as a backup and can be hosted on or near the machines most likely to need the data, in conjunction with advanced scheduling techniques.
Sharing is not limited to files, but also includes other resources, such as specialized devices, software, services, licenses, and so on. These resourcesare virtualized to give them a more uniform interoperability among heterogeneous grid participants. The participants and users of the grid can be members of several real and virtual organizations. The grid can help in enforcing security rules among them and implement policies, which can resolve priorities for both resources and users.

SUBMITTED BY
Ankit Sharma

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CHAPTER 1
OVERVIEW
What is Grid Computing
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.
One of the main strategies of grid computing is to use middleware to divide and apportion pieces of a program among several computers, sometimes up to many thousands. Grid computing involves computation in a distributed fashion, which may also involve the aggregation of large-scale cluster computing-based systems.
The size of a grid may vary from small—confined to a network of computer workstations within a corporation, for example—to large, public collaborations across many companies and networks. "The notion of a confined grid may also be known as an intra-nodes cooperation whilst the notion of a larger, wider grid may thus refer to an inter-nodes cooperation".
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.
Comparison of Grids and Conventional Supercomputers
“Distributed” or “grid” computing in general is a special type of parallel computing that relies on complete computers (with onboard CPUs, storage, power supplies, network interfaces, 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 processors 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 a similar computing resource as multiprocessor supercomputer, but at a 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 processors and local storage areas do not have high-speed connections.This arrangement is thus well-suited to applications in which multiple parallel computations can take place independently, without the need to communicate intermediate results between processors.] 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.]
There are also some differences in programming and deployment. It can be costly and difficult to write programs that can 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 allow conventional, standalone programs, given a different part of the same problem, to run on multiple machines. This makes it possible to write and debug 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.

ABSTRACT
Mankind is right in the middle of another evolutionary technological transition which once more will change the way we do things. And, you guessed right, it has to do with the Internet. It’s called "The Grid", which means the infrastructure for the Advanced Web, for computing, collaboration and communication.
Grid computing, most simply stated, is distributed computing taken to the next evolutionary level. The goal is to create the illusion of a simple yet large and powerful self managing virtual computer out of a large collection of connected heterogeneous systems sharing various combinations of resources.
This paper aims to present the state-of-the-art concepts of Grid computing. A set of general principles, services and design criteria that can be followed in the Grid construction are given. One of the Grid application project Legion is taken up. We conclude with future trends in this yet to be conquered technology.

Grid Computing

[attachment=16989]
.It is a network of geographically distributed resources including computers, peripherals, switches and data.

.It is originated in early 1990’s for making computer power easy to access.

.Different pc’s are connected with the help of grid which are managed by Gridware.

.Gridware are special type of middleware that unable the sharing and manage Grid components.


GRID - COMPONENTS

.Mainly five components are used in grid computing:-

.User-Interface

.Resource Broker

.Computing elements

.Storage elements

.Schedular


User-Interface:-The place where
users logon to.

Resources Broker:-matches the user requirements with the available resources on the grid.

Computing Element:-A batch queue on a site’s computer where the user’s job is executed.

Storage Element:-Provide large scale storage for files.

Information System:-Tell the status of computing elements and storage element

Grid Computing seminar report


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History

The ancestor of the Grid is Metacomputing. This term was coined in the early eighties by NCSA.
Director, Larry Smarr . The idea of Metacomputing was to interconnect supercomputer centers in order to achieve superior processing resources.
One of the first infrastructures in thisarea, named Information Wide Area Year, was demonstrated at Supercomputing 1995 .
This project strongly influenced the subsequent Grid computing activities. In fact one of there searchers who lead the project I-WAY was Ian Foster who along with Carl Kesselman published in 1997 a paper that clearly links the Globus Toolkit which is currently the heart of many Grid projects, to Metacomputing.
The Foster-Kesselman duo organized in 1997, at Argonne National Laboratory, a workshop entitled “Building a Computational Grid” . At this moment the term “Grid” was born. The workshop was followed in 1998 by the publication of the book “The Grid: Blueprint for a New Computing Infrastructure” by Foster and Kesselman themselves.



Introduction

Grid computing is form of networking unlike conventional network that focus on communication among devices.
It harnesses unused processing cycles of all computers in a network for solving problems too intensive for any stand alone machine.
Grid computing is a method of harnessing the power of many computers in network to solve problems requiring a large numbers of processing cycles and involving huge amount of data.



BASIC CONCEPTS OF HOW IT WORKS?

The computer is tied to network such as internet, which enables regular people with home pcs to participate in the grid project from anywhere in the world.
The pc owners have to download a simple software from the grid computing provider. And the project sites use the software that can divide and distribute the pieces of program to thousands of computers for processing.
This system on desktop of user shows a grid computing system that is distributed among the various local domains.



APPLICATION OF GRID COMPUTING

The grid computing is used to solve the problems which are beyond the scope of single processor, the problems involving the large amount of computations or the analysis of huge amount of data.
Right now there are scientific and technical projects such as CANCER,AIDS and other medical research projects that involves the analysis of the inordinate amount of data.

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