Grid computing
Grid computing uses middleware to process and coordinate large amounts of data from different resources across a network, allowing them to function as a virtual whole. Anderson 2004 states that ‘These resources are centrally managed by IT professionals, are powered on most of the time, and are connected by full-time, high-bandwidth network links. There is a symmetric relationship between organizations: each one can either provide or use resources.’ The concept was developed to provide users with access to resources they needed at any point in time. Grid computing has helped increase the development of information systems to become more flexible, cost and power efficient, faster performance, scalability and become more available. Grid computing has enabled groups of networked computers to be pooled and provisioned on demand to meet the changing needs of business. Instead of dedicated servers and storage for each application, grid computing enables multiple applications to share computing infrastructure.

As seen from the diagram above, the use of grid computing has improved information systems of a company by increasing the flexibility of resources used amongst each department. In every company the workloads are constantly fluctuating during the course of a day, week, or month. The resources are now spread across all the departments, so they are now able to demand for resources in real time and allowing the business to supply accordingly. The concept is also brought up in the article A Cost-based Multi-Unit Resource Auction for
Service-oriented Grid Computing ‘The application of grid infrastructure will facilitate the integration of efforts among different departments within and across large enterprises, as well as the contributions of service providers for projects of finite duration.’
As stated by Arafah, M.A. ‘Grid computing is emerging as the foundation up which organisation can be built. Such organisations are becoming of increasing importance for tackling various projects.’ Grid computing has help information systems broaden its access to more resources. Grid computing operates on three basic principles to help develop information systems; standardisation, virtualisation, and automation. It uses standardise software and hardware components to simplify configuration and deployment and reduce incompatibility, virtualised IT resources by pooling hardware and software into shared resources; and automate systems management, including monitoring and provisioning.
The grid computing architecture allows organisation to easily create a large-scale computing infrastructure from low budget, off-the-shelf components. However there is a slight downside to grid computing, ‘Grid computing deals with the usage of large scale and heterogeneous resources in geographically dispersed sites. The target applications which are executed over grid environments usually require a huge execution time and computation capacity. If there is a failure in one or more grid services used by the application, all the work that has been done can be simply lost.’

Many businesses today operate in an unpredictable and global environment. With the underlying grid infrastructure, IT now has the ability to respond quickly to these types of changing business needs. For the National Applied Research Laboratories (NARL), Taiwan, they have developed a GEO Grid Framework based on grid technology, remote sensing data, and geographic information. Grid computing has allowed the organisation to adapt quickly to change, ‘The presented approach is initiated by synergy of NARL’s core competence on environment monitoring and disaster reduction techniques which include high-resolution satellite image processing, virtual reality visualization, grid computing, and disaster mitigation technology along with the advanced cyber infrastructure environment established within NARL.’ Grid computing allows organisations to respond to dynamic workloads in real time. The application are used today are tied to specific hardware and software silos, limiting their ability to adapt to changing workloads. This expensive and inefficient use of IT resources means that IT departments must have large amounts of hardware so that each application can handle peak or worst-case workload scenarios. Grid computing lets IT professionals dynamically de-allocate and allocate IT resources as needed, resulting in an improved responsiveness to workloads that change on a global scale. This method can be seen in Wasson and Humphrey’s article as they use policy and enforcement in virtual organisations to respond to dynamic workloads in real time. Grid computing has also provided a predictable service level through the use of service-level agreements. Organisation can tie business requirements to IT architecture to proactive maintenance and monitoring. A grid-based architecture minimises single sources of failure and provide powerful, high-availability capabilities through software stacks, protection for valuable information assets, and business continuity. It reduces expensive systems administration overhead, costly integration projects, and runaway budgets. Grid computing has also improved information systems development by reducing the costs with improved efficiency and smarter capacity planning. Grid computing mainly focuses on operational efficiency and its predictability. Organisations are able to avoid overprovision to meet worst-case scenarios during peak period with the new generation of clustering and server virtualisation functions. Due to computing resources being able to applied incrementally when needed, organisation now have increased computing and storage capacity utilisation at a reduced cost. Through the use of grid computing researchers have found that projects that were previously impractical and unfeasible due to physical location of vital sources are now feasible. For example Great Britain can conduct research that relies on database across Europe, instrumentation in Japan, and computational power in the United States. Making resources available exposes end-users to tools of the profession, facilitating new possibilities for research and instruction.

Although speeds and capacities of processors continue to increase resource-intensive applications are proliferating as well. At many institutions, certain campus user-face ongoing shortages of computational power, even as large numbers of computer are under-used. With grid, programs hindered by constraints on computing power become possible.

Due to the number of functioning grids is relatively small, it may take time for the higher education community to capitalise on the opportunities that grids can provide and the feasibility of such projects, as the number and capacity of high-speed networks increase, however, particularly those catering to the research community and higher education, new opportunities will arise to combine IT assets in ways that expose end users to the tools and applications relevant to their organisation and to dramatically reduce the amount of time required to process data-intensive jobs. Further, as grids become more widespread and easier to use, increasing numbers and kinds of IT resources will be included on grids. We may also start to see advantages to solving a complex genetic problem suing grid computing, being able to harness spare computing cycles to manipulate an image in Photoshop or create a virtual worlds in a simulation may be some of the first implementations of grids.

Grid computing IT architecture and methodology include various technology and practices. It includes consolidated IT resources, agile IT operations, predictable high performance and scalability, and continuous availability. Not every IT department will adopt every grid computing technology or technique; however many groups are already seeing dramatic benefits by using gird computing technologies and practices.
Grid computing enables the sharing of IT resources and the consolidation of services, storage, application, and even data centres. Results include reduced costs and lower power, cooling and space requirements.

References * Adarsh Patil 2004, Grid Computing, digital image, viewed 1 August 2011, < http://www.adarshpatil.com/newsite/images/gridcomputing.gif> * Arafah, M.A. ‘ Grid computing: a STOPE view’ in International Journal of Network Management, v 17, n 4, p 295-305, July-Aug. 2007 * David P. Anderson, 2004, BOINC: A System for Public-Resource Computing and Storage, pp 1-6 * Javier A., Luis S., Artur A., Paulo S. and Jordi T., 2007 High-Available Grid Services through the use of Virtualised Clustering, pp.1-4 * Michael S., Oliver H., Roman B., 2008 A Cost-based Multi-Unit Resource Auction for Service-oriented Grid Computing, pp 1-8 * Guey-Shin C., Whey-Fone T.Fang-Pang L., Charlie C., Te-Lin C., 2008 A GEO Grid Implementation for 3D GIS Taiwan, pp 1-6 * Glenn W. and Marty H., 2003, Policy and Enforcement in Virtual Organizations, pp 1-8 * Min C., Martin F., Jinbo C., Pedro S., 2003, MAAN: A Multi-Attribute Addressable Network for Grid Information Services, pp 1-8 * Oracle 2009, Oracle Grid Computing, USA, viewed 1 August 2011, <http://www.oracle.com/us/technologies/grid/index.html> * Oracal 2009, Oracle Grid Computing, USA, viewed 3 Augest 2011,
<http://www.oracle.com/us/technologies/026979.pdf>
* Schwiegelshohn, U. ‘Perspectives on grid computing’ in Future Generation Computer Systems, v 26, n 8, p 1104-15, Oct. 2010