Paper Presentations

Advanced grid computing 

  Abstract
                    With the furtherance of technology, life is becoming simpler and less demanding. The conglomeration of three major disciplines, Computers, Communications and Electronics unleashes a vast amount of power in terms of technology. In an era of cost-cutting ,when companies are faced with a growing need to maximize and/or improve efficiencies of existing IT investments, organizations are turning to Grid Computing as a strategic solution.
This paper seeks to introduce this Grid Computing as an emerging computing model that provides the ability to perform higher throughput computing by taking advantage of many networked computers to model virtual computer architecture.
Further, it enumerates on how the Grids use the resources of many separate computers connected through a network to solve large-scale computation problems. It explains how the Grids provide the ability to perform computations on large data sets, by breaking them down into many smaller ones, or provide the ability to perform many more computations at once than would be possible on a single computer, by modeling a parallel division of labor between processes.
Finally the paper puts forth the Future of Grid Computing with a case-study and enlightens on how organizations can optimize computing and data resources, pool them for large capacity workloads, share them across networks and enable collaboration.

1.Introduction

                       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.
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. The systems linked in a grid might be in the same room or distributed around the world. They might be running different operating systems on many hardware platforms. They might even be owned by different organizations. Regardless of the depth of a grid's resources, the entire grid user experiences the processing resources of a very large virtual computer.

2.Definition of Grid Computing 

 
Because it is an emerging technology, grid computing can mean different things to different people.
“Grid computing allows you to unite pools of servers, storage systems, and networks into a single large system so that you can deliver the power of multiple-systems resources to a single user point for a specific purpose.” To a user, data file, or an application, the system appears to be a single enormous virtual computing system.

3.Grid Network
3.1 A Simple Grid Network:

Illusion of a Virtual Computing Environment

Grid computing is the next logical step in distributed networking. A simple Grid Network contains a Grid Software which acts as an interface between any user (normal user, administrator) and the virtual computing environment (a superset of PC’s, Workstations, Super Computers).

3.2 Components of Grid Network:
Clusters, Workstations, Desktop PC’s, Super Computers contribute to the components of a grid. Each component might be an important component of a grid, but by itself doesn’t constitute a grid.

4.Evolution of GridIn fact, grid can be seen as the latest and most complete evolution of more familiar developments — such as distributed computing, the Web, peer-to-peer computing and virtualization technologies.

4.1 The Time Is Right:Many organizations feel that this is the right time to encourage Grid Computing for the following reasons:
- Unprecedented pressure to lower costs
- Inexpensive, commodity blade servers
- Inexpensive OS optimized for1-4CPUs
- Storage no longer tied to a single server

5.Functions
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. This section describes the most important capabilities of grid computing.

5.1 Optimal usage of resources:
 Remote Applicability: The easiest use of grid computing is to run an existing application on a different machine.
 Utility of un-used disk drive: Grid computing can be used to aggregate the unused storage into a much larger virtual data store.
 Resource Utilization: Many resources such as CPU, Storage and so on, can be used with the principle of optimality.

5.2. Parallel CPU capacity:A CPU intensive grid application can be thought of as many smaller “sub jobs,” each executing on a different machine in the grid. For example, a job finishes 10 times faster if it uses 10 times the number of processors.



5.3 Virtual resources and virtual organizations for Collaboration:
In the past, distributed computing promised collaboration among a wider audience, 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.

5.4 Access to additional resources:
In addition to CPU and storage resources, a grid can provide access to other resources in additional numbers and/or capacity.
5.5 Resource balancing:
For applications that are grid-enabled, the grid can offer a resource balancing effect by scheduling grid jobs on machines with low utilization. Without a grid infrastructure, balancing decisions are difficult to prioritize and execute.
5.6 Reliability And Management:As there are various supplementary resources available for each resource, reliability is achieved to a greater extent.
The Grid software itself takes care of all the management among the various Grid components.

6.Grid Architecture

 
6.1 Layers of Grid Architecture:
• Fabric Layer: It provides access to some shared resources using Grid Protocols. Shared Resources can be computational resources, storage systems, catalogs, network resources, and sensors.
• Connectivity Layer: It defines the core communication and authentication protocols required for grid-specific network functions.
• Resources Layer: It defines protocols for secure negotiations, initiation and monitoring the control of sharing operations on individual resources. Information and management protocols define this layer.
• Collective Layer: It contains protocols and services that capture interactions among a collection of resources.
• Application Layer: User applications operate in this layer by using the services of other layers.

6.2 Grid Topology:

 
Intra grid:A typical intra grid topology exists within a single organization. The primary characteristics of an intra grid are a single security provider, bandwidth on the private network is high and always available, and there is a single environment within a single network.

Extra grid:Based on a single organization, the extra grid expands on the concept by bringing together two or more intra grids. An extra grid, typically involves more than one security provider, and the level of management complexity increases. The primary characteristics of an extra grid are dispersed security, multiple organizations, and WAN connectivity.

Inter grid:The primary characteristics of an inter grid include dispersed security, multiple organizations, and WAN connectivity. The data in an inter grid is global public data, and applications must be modified for a global audience.

6.3 Grid Software:
There are many aspects to grid computing that typically are controlled through software. These functions can be handled across a spectrum of very manual procedures to processes being handled through sophisticated software.
 Donor software
 Management Software
 Communications Software
 Schedulers Software

7.Grid Security
In a grid, the member machines are configured to execute programs rather than moving data. This makes an unsecured grid potentially fertile ground for viruses and Trojan horse programs. For this reason, it is important to understand which components of the grid must be rigorously secured. The high level grid security requirements are:
 Authorization
 Privacy
 Confidentiality
 Manageability
 Firewall

8.Case Study

Task: To Find the Annual Income of all the employees of an organization that is wide spread through out the world.
Assignment: This task is assigned to a processor of a grid.

Work-Flow:
• This processor initially checks if the task is too large for a single processor to handle.
• If so, distributes the task among various devices of the grid, here comes the aspect of Parallel CPU Capacity. The processors job is to collect the Incoming data files and this is simply done through WAN/Internet.
• The Annual Incomes of the employees starts getting accumulated in the disk drive of the processor. Once there is no more disk space left with it, it has a privilege of using the storage device that has been idle for the past few hours in the same grid network; the functional Application Exploiting under Utilized Resources is taken advantage of at this point.
• Suppose a high priority task has been assigned to this device, it has a capability to transfer the relatively low priority task to some other device of the network and can finish its high priority task as per the dead line, thereby Utilizing Resources.

9.Conclusion
It is important to know that grid is not a silver bullet that can take any application and run it a 1000 times faster without the need for buying any more machines or software. Grid computing appears to be a promising trend for three reasons:
(1) Its ability to make more cost-effective use of a given amount of computer resources.
(2) As a way to solve problems that can't be approached without an enormous amount of computing power.
(3) Because it suggests that the resources of many computers can be cooperatively and perhaps synergistically harnessed and managed as collaboration towards a common objective.

evolution in grid computing

GRID COMPUTING

CONTENTS: 

1. ABSTRACT
2. INTRODUCTION TO COMPUTING
3. TYPES OF GRID COMPUTING
4. ISSUES
5. APPLICATIONS
6. A SIMPLE HELLO WORLD EXAMPLE
7. DOWNSIDESOF GRID COMPUTING
8. CONCLUSION

1: ABSTRACT:

Grid computing is said to be the next big thing in IT. Research in grid computing is making rapid progress, owing to the increasing need for large-scale computation in the resolution of complex problems.
Clusters are, in a sense, the predecessors of grid technology. Clusters interconnect nodes through a local high-speed network, using commodity hardware, with the aim of reducing the costs of such infrastructures.
Supercomputers have been replaced by clusters of workstations in a large number of research projects.
Grids provide access to widely distributed computing and data resources, allowing data-intensive applications significantly improved data access, management and analysis. Nowadays there are a huge number of data intensive applications in several domains such as physics, climate modeling, biology/bio-informatics, addressing some of the most important current problems.
Computing grids are conceptually not unlike electrical grids. When you connect to the electrical grid, you don’t need to know where the power plant is or how the current gets to you. Grid computing uses middleware to coordinate disparate IT resources across a network, allowing them to function as a virtual whole.
Grids use a layer of middleware to communicate with and manipulate heterogeneous hardware and data sets. In some fields— astronomy, for example—hardware cannot reasonably be moved and is prohibitively expensive to replicate on other sites.

Grids address two distinct but related goals: providing remote access to IT assets, and aggregating processing power. The most obvious resource included in a grid is a processor, but grids also encompass sensors, data-storage systems, applications, and other resources.
Grids use a layer of middleware to communicate with and manipulate heterogeneous hardware and data sets. In some fields— astronomy, for example—hardware cannot reasonably be moved and is prohibitively expensive to replicate on other sites.
Many grids are appearing in the sciences, in fields such as chemistry, physics, and genetics, and cryptologists and mathematicians have also begun working with grid computing. Grid technology has the potential to significant impact other areas of study with heavy computational requirements, such as urban planning. Another important area for the technology is animation, which requires massive amounts of computational power and is a common tool in a growing number of disciplines included in a grid is a processor, but grids also encompass sensors, data-storage systems, applications, and other resources.

2: INTRODUCTION
TO COMPUTING:
Grid computing is an implementation in an enterprise computing taxonomy .It consists of family of technologies for opportunistically providing computing power from a pool of resources. Grid computing is opportunistic since it has to wait for resources to become available. The resources may include computing cycles, file and data storage, caching, network bandwidth, databases and application software. These resources can be distributed diversely on the globe .Provision of computer power means the methods and mechanisms for locating, authorizing, assembling, scheduling, releasing and accounting for resources and their usage.
For example compute grids share computational and data resources. And some grids share both and can also share network bandwidth, storage and caching resources and application software.

So the first dimension reflects the types of resources that the grid can use .Second dimension describes a grids geographic or administrative reach .Third dimension tells how the companies can get the resources. Fourth dimension reflects the partnerships between the Enterprises. The Fifth dimension reflects for what type of application a grid is used for biology, sensors or any other access.
So this proves that a Grid is multidimensional.
Coming to the Cluster, they are connected commonly connected to a high speed LANS. Clusters are usually deployed to improve speed and/or reliability over that provided by a single computer, while typically being much more cost-effective than single computers of comparable speed or reliability. They can be geographically distributed, but are often closely coupled in the same room.

Clusters can consist of heterogeneous processors and peripherals but they are homogeneous and use high performance and special purpose interconnection networks.


Fig: clusters in a university
But a CLUSTER is not a grid.
Since Computer Clusters require a much higher degree of centralized control, this point clearly distinguishes between a cluster and a grid.
Mainly the grid community focuses on issues such as Reliability, Security, Service and Quality Performance and Resource integration.
3:TYPES OF GRID COMPUTING:
1: Utility Computing:
Here, the main idea is to offer computing resources as an on-demand service to customers in much the same way that utilities offer electrical, gas, water, and telephone services to households and businesses. The utility computing service provider offers hosted computing resources.
One distinction is that the on-demand computing resources can comprise a grid in the service provider’s realm, and the grid can span several sites in the provider’s service area. Grid economies and scalability add a new dimension.





2: Autonomic computing:
Autonomic computing architectures monitor utilization and performance, tune and manage themselves, and adapt to failure. Some share resources and schedule tasks with other systems. The fundamental components of an autonomic computing system provide functions that computing grids will almost certainly need to operate effectively, such as the ability to recover lost computational subtasks.
As grids evolve, they might take on many of the characteristics of an autonomic computing system: Self-monitoring, diagnosis, and adaptability in their youth; sophisticated resource scheduling and forecasting; and perhaps a vertebrate-like involuntary autonomic nervous system at maturity. So the real issue is the extent to which a grid has adopted the characteristics of autonomic computing.




3: Peer-to-peer computing:

Peer-to-peer computing is one type of application that uses grid services to advertise, find, and share files.
The grid community tends to focus on top-down issues such as
1: resource integration,
2: performance,
3: reliability,
4: service quality, and
5: security.
The peer-to-peer community tends to focus on bottom up issues such as narrowly defined and specialized services, and support for tens of thousands of concurrent participants.

4: ISSUES:

The success of grid computing depends on fundamental issues in 2 main areas:
1: security
2: performance

Security:
Grids must deal with every security that any enterprise-owned or outsourced computing model faces. Security issues include secure authentication, access rights and privileges. Reliable and secure communications, perhaps with encryption, are also a requirement. Maintaining confidentiality and privacy will also be issues if you are transferring personal data.

Performance:
For a grid performance is the main key is to deliver nontrivial qualities of service “. Some grid services might fall short because

the scattering and gathering steps can incur significant delay. Grid computing tends to be opportunistic – it must wait for computing resources to become idle – which means that performance can be nondeterministic. Grid performances include resource availability and reliability, utilization and load, response time, delay and delay variation. Data Integrity is another consideration .
5: Applications:

1: Bio-Informatics:
Bioinformatics analysis of data produced by complete genome sequencing projects is one of the major challenges. Integrating up-to-date databanks and relevant algorithms is a clear requirement of such an analysis. Grid computing would be a viable solution to distribute algorithms and data, computing and storage resources for Genomics.
When bioinformatics grid server receives the computational requests from the client, it locates a suitable node in the grid to perform the mathematical computation according to the users’ requirement and task allocation rule, or integrates a virtual supercomputer to perform the larger computational requests from users.

2: SMALLPOX project:We intend to use grid computing to screen millions of potential anti-smallpox drugs against this target.
The Smallpox Research Grid uses a SETI-like model to analyze interactions between virus protein targets and a catalog of tens of millions of drug molecules. The Smallpox project can harness millions of computers belonging to people in over two hundred countries, all of whom will benefit from protection against smallpox.
By adding your CPU to the global grid, every time your computer is idle, you contribute your computing resources to the grid, accelerating the screening process while dramatically reducing the cost of the project.
The result is that rather than spending years to screen hundreds of thousands of molecules, it will be possible to screen hundreds of millions of molecules in just months.
This saves a large amount of system time and alters the use of Resources.

3: Grid Computing In SETI:
SETI, the search for Extraterrestrial Intelligence uses a huge number of Internet-connected computers to download the results during idle times.
As of late 2004, SETI had scavenged 1.83 million years of CPU time from 4.9 million users in 226 countries .It had used this grid to perform 4.5*10( power)21 floating-point operations Experts say that 10(power)21 -one sextillion-is the approximate number of grains of sand on all of Earth's beaches and deserts
It is just one order of magnitude shy of the estimated number of stars in the visible universe.



Some SETI observations have been conducted using the radio telescope

4: The Rice Genome Program: :
Bioinformatics research leads to a lot of information collected worldwide for which large databases should be maintained .Grid computing solves this problem. Grid computing would be a viable solution to distribute algorithms and data, computing and storage resources for Genomics.
Finding a single genome of Rice (scientific name Oryza sativa) can take a lot of time which measures up to months examining all the base pairs which are in millions These data is stored in data repositories. Hence we require a technology not only to visualize analyze DNA data but also the integration and exchange of information on a gene or coding regions from different international collaborative databases needs to be done in a careful and in a robust manner .Grid Technology Solves this problem.

Grid Technology enables sharing of bioinformatics data from different files by creating a virtual organization of data.
So the collection of databases generated during the research work on RICE is collected and maintained by a grid which helps in posing Queries on a particular type of Rice.


Here the user must provide the services they want providing constraints like family size , protein interactions ..etc .


6: A simple Hello world Example:
Let us consider some cases of web client-server applications like Video streaming Game serving, File downloading etc. So an approach to provide a scalable solution is to distribute the application even to the other servers that run the same application. A Network dispatcher is the entry point for an application but does not run the application , rather the servers which are connected onto the same LAN handle the workload and answers the queries of the client.
Let us consider an example: A network dispatcher (or) Front end server waits for client requests. When connected the client is given back a ticket and an application service IP address of where to connect.
The application answers HELLO WORLD when the client connects to it . The application is started on the application servers by the front end server.
The Executable for the client is Hello client and takes the front end server host name as parameter.


Grid computing used in Hello world Example .

7:Downsides of Grid Computing:

Being able to access distant IT assets—and have them function seamlessly with tools on different platforms—can be a boon to researchers, but it presents real security concerns to organizations responsible for those resources. An institution that makes its IT assets available to researchers or students on other campuses and in other countries must be confident that its involvement does not expose those assets to unnecessary risks. Similarly, directors of research projects will be reluctant to take advantage of the opportunities of a grid without assurances that the integrity of the project, its data, and its participants will be protected.
Another challenge facing grids is the complexity in building middleware structures that can knit together collections of resources to work as a unit across network connections that often span oceans and continents. Scheduling the availability of IT resources connected to a grid can also present new challenges to organizations that manage those resources. Increasing standardization of protocols addresses some of the difficulty in creating smoothly functioning grids, but, by their nature, grids that can provide unprecedented access to facilities and tools involve a high level of complexity.
A word of caution should be given to the overly enthusiastic. The grid is not a silver bullet that can take any application and run it a 1000 times faster without the need for buying any more machines or software. Not every application is suitable or enabled for running on a grid.


Some kinds of applications simply cannot be parallelized. For others, it can take a large amount of work to modify them to achieve faster throughput. The configuration of a grid can greatly affect the performance, reliability, and security of an organization’s computing infrastructure. For all of these reasons, it is important for us to understand how far the grid has evolved today and which features are coming tomorrow or in the distant future.

8: CONCLUSION:
A greater awareness of this area is needed so that people can make a direct contribution towards solving these problems by providing whatever spare computing resources they may have at their disposal (usually in the form of idle cycles). Grid computing is the future for Bio-Informatics which helps to create an epidemic free future for man kind and to solve long-term problems. Grids make research projects possible that formerly were impractical or unfeasible due to the physical location of vital resources.
Using a grid, researchers in Great Britain, for example, can conduct research that relies on databases across Europe, instrumentation in Japan, and computational power in the United States. Making resources available in this way exposes students to the tools of the profession, facilitating new possibilities for research and instruction, particularly at the undergraduate level.
Although speeds and capacities of processors continue to increase, resource-intensive applications are proliferating as well. At many institutions, certain campus users face ongoing shortages of computational power, even as large numbers of computers are underused. With grids, programs previously hindered by constraints on computing power become possible.

paper on grid computing

Abstract
Grid Computing delivers on the potential in the growth and abundance of network connected systems and bandwidth: computation, collaboration and communication over the Advanced Web. This paper deals with various grid concepts.In particular it is shown what advantages a utilization of grid computing may have to offer and which applications could benefit from it.Moreover the notion of grid learning object is introduced.

Introduction

Increased network bandwidth, more powerful computers, and the acceptance of the Internet have driven the on-going demand for new and better ways to compute. Commercial enterprises, academic institutions, and research organizations continue to take advantage of these advancements, and constantly seek new technologies and practices that enable them to seek new ways to conduct business. However, many challenges remain. Increasing pressure on development and research costs, faster time-to-market, greater throughput, and improved quality and innovation are always foremost in the minds of administrators - while computational needs are outpacing the ability of organizations to deploy sufficient resources to meet growing workload demands.
On top of these challenges is the need to handle dynamically changing workloads. The truth is, flexibility is key. In a world with rapidly changing markets, both research institutions and enterprises need to quickly provide compute power where it is needed most. Indeed, if systems could be dynamically created when they are needed, teams could harness these resources to increase innovation and better achieve their objectives.

1.Grid Overview

Grid Computing delivers on the potential in the growth and abundance of network connected systems and bandwidth: computation, collaboration and communication over the Advanced Web. At the heart of Grid Computing is a computing infrastructure that provides dependable, consistent, pervasive and inexpensive access to computational capabilities. By pooling federated assets into a virtual system, a grid provides a single point of access to powerful distributed resources.
Researchers working to solve many of the most difficult scientific problems have long understood the potential of such shared distributed computing systems. Development teams focused on technical products, like semiconductors, are using Grid Computing to achieve higher throughput. Likewise, the business community is beginning to recognize the importance of distributed systems in applications such as data mining and economic modeling.
With a grid, networked resources -- desktops, servers, storage, databases, and even scientific instruments -- can be combined to deploy massive computing power wherever and whenever it is needed most. Users can find resources quickly,use them efficiently and scale them seamlessly.

2.The Grid Concept

The term ‘grid’ is variously used to describe a number of different, but related, ideas, including utility computing concepts, grid technologies, and grid standards. In this paper the term ‘Grid’ is used in the widest sense to describe the ability to pool and share Information Technology (IT) resources in a global environment in a manner which achieves seamless, secure, transparent, simple access to a vast collection of many different types of hardware and software resources, (including compute nodes, software codes, data repositories, storage devices, graphics and terminal devices and instrumentation and equipment), through non-dedicated
wide area networks, to deliver customized resources to specific applications.
At the most general level Grid is independent of any specific standard or technology. Any practical grid is realized through specific distributed computing
technologies and standards that can support the necessary interoperability. Today, there are no universally agreed grid standards, but there are freely available, open source and proprietary grid technologies that implement emerging standards recommendations. Separate web services standards are also emerging which have many grid-like capabilities. Indeed grids are already being built by integrating and enhancing web standards technology.

3.Practical Realizations

Practical grids are generally described in terms of layers (see Fig 1). The lowest layers (the ‘platform’) comprise the hardware resources, including computers, networks, databases, instruments, and interface devices. These devices, which will be geographically distributed, may present their data in very different formats, are likely to have different qualities of service (e.g. communication speeds, bandwidth) and are likely to utilize different operating systems and processor architectures. A key concept is that the hardware resources can change over
time - some may be withdrawn, upgraded or replaced by newer models, others may change their performance to adapt to local conditions - for example restrictions in the available communications bandwidth.
The middle layers (sometimes referred to as ‘middleware’) provide a set of software functions that ‘buffer’ the user from administrative tasks associated
with access to the disparate resources. These functions are made available as services and some provide a ‘jacket’ around the hardware interfaces, such that the different hardware platforms present a unified interface to different applications. Other functions manage the underlying fabric, such as identification and scheduling of resources in a secure and auditable way. The middle layer also provides the ability to make frequently used patterns of functions available as a composed higher-level service using workflow techniques.
The highest layers contain the user ‘application services’. Pilot projects have already been carried out in user application areas, such as life sciences
(e.g. computational biology, genomics), engineering (e.g. simulation and modeling, just in time maintenance) and healthcare (e.g. diagnosis, telematics). These services could include horizontal functions such as workflow (the
linkage of multiple services into a single service), web portals, data visualization and the language/semantic concepts appropriate to different application sectors.



4.Grid Developments and Deployment

A key issue facing the industry is the timing and mode of deployment of Grid technology to ensure that it is sufficiently mature to deliver the expected business
benefits. There is emerging evidence that the technology can achieve significant operational benefits (e.g. in telemedicine), improvements in performance (e.g. in climate modeling and genomics) and a significant reduction in costs. Nevertheless, current grid technologies are not yet viewed as sufficiently mature for industry scale use, and remain largely unproven in terms of security, reliability, scalability, and performance.

4.1.Short term

For the short term (within the next two years), Grid is most likely to be introduced into large organizations as internal ‘Enterprise grids’, i.e. built behind firewalls and used within a limited trust domain, perhaps with controlled links to external grids. A good analogy would be the adoption into business of the Internet, where the first step was often the roll out of a secure internal company ‘Intranet’, with a gradual extension of capabilities (and hence opportunity for misuse) towards fully ubiquitous Internet access. Centralized management is expected to be the only way to guarantee qualities of service. Typically users of this early technology will be expecting to achieve IT cost reduction, increased efficiency, some innovation and flexibility in business processes. At the same time the distinction between web services and grid services is expected to disappear, with
the capabilities of one merging into the other and the interoperability between the two standards being taken for granted.

4.2.Medium Term

In the mid term (say a five year timeframe) expect to see wider adoption - largely for resource virtualization and mass access. The technology will be particularly appropriate for applications that utilize broadband and mobile/air interfaces, such as on-line gaming, ‘visualization-on-demand’ and applied industrial research. The emphasis will move from use within a single organization to use across organizational domains and within Virtual Organizations, requiring issues such as ownership, management and accounting to be handled within trusted partnerships. There will be a shift in value from provision of computer power to provision of information and knowledge. At the same time open standards based tooling for building service oriented applications are likely to emerge and Grid technology will start to be incorporated into off-the-shelf’ products. This will lead to standard consumer access to virtualized compute and data resources, enabling a whole new range of consumer services to be delivered.

4.3.Long term

In the longer term, Grid is likely to become a prerequisite for business success - central to business processes, new types of service, and a central component of product development and customer solutions. A key business change will be the establishment of trusted service providers, probably acting on a global scale and
disrupting the current supply chains and regulatory environments.

5.Conclusions 

5.1.The Grid -- the IT infrastructure of the future -- promises to transform computation, communication, and collaboration. Over time, these will be seen in the context of grids -- academic grids, enterprise grids, research grids, entertainment grids, community grids, and so on. Grids will become service-driven with lightweight clients accessing computing resources over the Internet. Datacenters will be safe, reliable, and available from anywhere in the world. Applications will be part of a wide spectrum of network-delivered services that include compute cycles, data processing tools, accounting and monitoring, and more.

5.2.Grid computing and related technologies will only be adopted by commercial users if they are confident that their data and privacy can be adequately protected and that the Grid will be at least as scaleable, robust and reliable as their own in-house IT systems. Thus, new Internet technologies and standards such as IPv6 take on even greater importance. Needless to say, users of the Grid want easy, affordable, ubiquitous, broadband access to the Internet.

5.3.Similar to the public policy issues raised by the development of electronic commerce and electronic government, Grids raise a number of public policy issues: data privacy, information and cyber security, liability, antitrust, intellectual property, access, taxes, tariffs, as well as usage for education, government, and regional development.