Emerging Grid computing

Grid computing is a vital shift in thinking about how to maximize the value of computing resources. The technology is still fairly nascent. A new study shows that the grid computing market is on the verge of major expansion. According to a recent forecast, the aggregate grid computing market is expected to exceed $12 billion in revenue by 2007 across high performance computing (HPC) technical markets and commercial viable enterprises.

Grid computing -- next-generation distributed computing

The grid market is beginning to split into 3 distinct segments: compute, data, and optimization. The majority of today's grid implementations are in the compute space with a small set of applications Emerging opportunity is primarily focused on the pooling and allocation of resources across a variety of business servic.es While the potential opportunity is both broad and significant, the challenges are also varied with end users most often citing the cultural and organizational concerns associated with resource sharing.

Grid computing is an important new field, distinguished from conventional distributed computing by its focus on large-scale resource sharing and innovative applications.

As it is an emerging technology, grid computing can mean different things to different people, but here is a simple, serviceable definition for the concept of grid computing -- grid computing allows you to unite pools of servers, storage systems, and networks into a single large system so you can deliver the power of multiple-systems resources to a single user point for a specific purpose.

Grid computing is the next logical step in distributed networking. Just as the Internet allows users to share ideas and files as the seeds of projects, grid computing lets us share the resources of disparate computer systems so people can actually start working on those projects. Grid computing takes the ability for computers (and their users) to communicate a step further -- with grid computing, you can reach out and use computational or storage resources on machines other than your own.



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 across the globe; 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, all the grid user experiences is the processing resources of a very large virtual computer.
The major purpose of a grid is to virtualize resources to solve problems; the main resources grid computing is designed to give access to include (but are not limited to):

Computing/processing power
Data storage/networked file systems
Communications and bandwidth
Application software


Grid computing has recently enjoyed an increase in popularity as a distributed computing architecture that is becoming highly suitable for corporate computing. Grid computing solutions are being employed in many areas to address critical business requirements, such as:

Financial services firms tapping into grid computing to address risk management and compliance.
Automotive manufactures using grid solutions to accelerate product development and increase collaboration.
Oil companies harnessing grid technology to hasten the discovery of oil and increase the odds of successful mining.


The origins of grid computing

Just like with the Internet, academic institutions were at the forefront when it came to developing the first-generation technologies and architectures that formed the basis of grid computing. Institutions such as the Globus Alliance, the China Grid, and the UK e-Science Grid core program were some of the first to incubate and grow grid solutions to maturity, preparing them for commercial adoption.

IGrids were borne out of the research and academic communities' very real need to collaborate. A crucial component of research is the ability to disseminate knowledge -- the more efficiently you can share not only vast amounts of information but also the computational resources that help you create this data -- the more refined and informative a level of quality in collaboration you can achieve.

A counterpart to this need to disseminate knowledge is in the commercial world. Grid computing can also address this need, because the integration of business processes and transactions, facilitated by Web services standards, continues to grow in importance. As the adoption of commercial grid computing continues, standards (such as those proposed by organizations like the Global Grid Forum, or GGF) will benefit from the real-world, hardened, and practical requirements to which commercial applications will subject them.

Currently, grid computing benefits from the early identification and development of standards-based technologies in the academic world that is matched with more practical and robust implementations that commercial businesses require. There is no reason to imagine that this synergy will not continue as grid computing matures.

How grid differs from cluster computing

Cluster computing can't truly be characterized as a distributed computing solution; however, it's useful to understand the relationship of grid computing to cluster computing. Often, people confuse grid computing with cluster-based computing, but there are important differences.

Grids consist of heterogeneous resources. Cluster computing is primarily concerned with computational resources; grid computing integrates storage, networking, and computation resources. Clusters usually contain a single type of processor and operating system; grids can contain machines from different vendors running various operating systems.

Grids are dynamic by their nature. Clusters typically contain a static number of processors and resources; resources come and go on the grid. Resources are provisioned onto and removed from the grid on an ongoing basis.

Grids are inherently distributed over a local, metropolitan, or wide-area network. Usually, clusters are physically contained in the same complex in a single location; grids can be (and are) located everywhere. Clusters interconnect technology delivers extremely low network latency, which can cause problems if clusters are not close together.

Grids offer increased scalability. Physical proximity and network latency limit the ability of clusters to scale out; due to their dynamic nature, grids offer the promise of high scalability

Cluster and grid computing are completely complementary; many grids incorporate clusters among the resources they manage. Indeed, a grid user may be unaware that his workload is in fact being executed on a remote cluster. And while there are differences between grids and clusters, these differences afford them an important relationship because there will always be a place for clusters -- certain problems will always require a tight coupling of processors

However, as networking capability and bandwidth advances, problems that were previously the exclusive domain of cluster computing will be solvable by grid computing. It is vital to comprehend the balance between the inherent scalability of grids and the performance advantages of tightly coupled interconnections that clusters offer.

GRIDS Standards And Web Services

Two forces are leading the development of grid standards: the Globus Alliance, which oversees the Globus Toolkit; and the Global Grid Forum (GGF, www.gridforum.org), which is creating a set of open standards for grid technologies and applications. The GGF includes academics, researchers, and small and big technology companies. The GGF's major efforts include the Open Grid Services Architecture (OGSA) and the Open Grid Services Infrastructure (OGSI). Globus and the GGF have cooperated such that Globus Toolkit 3.0 includes a reference implementation of the OGSA/OGSI standards.

The emerging grid infrastructure also incorporates Web services standards to facilitate communication among heterogeneous resources. Grid proponents expect that Web services mechanisms will become the interface for grid computing.



Pros & Cons

Pros:
Significant cost savings
Increased computational power
Maximized utilization of resources


Cons:
Fledgling technology
Software licensing may be problematic
Security risks can be high
Applications must be converted to new architecture

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