
Most industries today have become so dynamic that organisations have to consistently seek and adapt to change, in order to survive and prosper. Factors like more diversified customer preferences, technological advances, increased competitive threats and an intensified global economy are among the forces inducing change. Organisations need to become more adaptable embracing Charles Darwin’s view that “it is not the strongest of the species that survives, nor the most intelligent, but the one that is the most adaptable to change”.
A survey conducted by PricewaterhouseCoopers in March 2004 shows that 47% of the CEO’s of the US’s fastest growing companies believe that their most critical success factor is having flexible strategies to respond to accelerating business changes. However, many recently implemented Information Systems still tend to ignore this need for flexibility and at times are hard to scale and customise, thereby limiting the ability of an enterprise to react fast to its evolving business needs.
In the last two decades we constantly experienced a dramatic change in the way we store and process digital information. Every few years there has been an industry breakpoint; an important new computing concept that changed radically the way computers are used and Information Systems are implemented. Examples include graphical and more user-friendly interfaces, the clientserver concept and the Internet. Such factors have somehow aided and contributed to position computers as a necessary commodity. Additionally, with the constant drop in the cost of hardware, and better and cheaper network bandwidth, computers have become even more ubiquitous. The Internet has evolved tremendously and is today considered as probably the most effective communication medium. Whilst technology tends to evolve in a non-linear fashion, Moore’s Law has ensured that processing power has been increasing exponentially.
Though this is contributing to easier hoarding and dissemination of information, ICT professionals today still face tough challenges. ICT budgets grew rapidly in the late 90′s in anticipation of the Y2K problem. In these last years many ICT departments have been even asked to cut their budgets while they were expected to continue providing an appropriate information infrastructure so as to enable the organisations to augment their products and possibly gain a competitive edge. Hardware replacement cycles are perceived to have increased. Generally speaking, ICT budgets did not grow in these last years in line with the computational needs of the organisations; whilst workloads are still increasing, the capacities to handle them are not.
In some cases increasing a firm’s computational needs might end up in a lot of computational power which is not appropriately utilised. Why? Consider for example the utilization of a server machine. Most of the time its real processing capacity is not used at all. However maybe sometimes because a large and long process is executed or the number of connected users temporarily increase, the server might endup experiencing a processing overload. It has been estimated that on average a desktop computer uses only about 5% to 8% of its processing power (EuropeanCeo, 2005). Whilst, as Hendry (2004) reports, load balancing can aid in the distribution of processing and communication activity, similar servers that experience spikes in processor usage are barely used for the rest of the day and eventually end up with a large amount of unused computing capacity.
So the inevitable questions are, is it really feasible to increase and upgrade the firm’s single source of computational power if most of the time the existing processing power is not being used? How can we ensure that a firm’s computational resources are well balanced and allocated, so as to minimise wastage and eventually, justify any further investment in the ICT infrastructure?
The basic concept that gives insight to the answer to these questions extends back to the 70′s when the notion of distributed computing was born. Today, we are seeing increasing interest among business communities in what is termed as, Grid Computing.
Definition
World-renowned organisations are promoting the Grid in a big way and several definitions can be found. It has become a fashionable term. Dr. Ian Foster, a professor at the University of Chicago and director of the Distributed Systems Lab at Argonne National Laboratory, a pioneer in Grid Computing, provided his definition for the layman as being the “technology to enable the sharing of computing resources across institutional boundaries”. Research firm, Gartner, Inc., defines grid computing as a way to solve computing tasks using resources that are shared by more than one owner and coordinated to solve more than one problem.
The concept of Grid Computing was initially popular among academics, research and scientific communities. It was used for functions that required a substantial amount of computing power. However in these last years, an increasing number of organisations are early adopting and trying to reap benefits from this technology.
There are numerous examples where Grid computing has been applied. Among the research communities, Oxford University is using Grid technology to analyse 3.5 billion molecules to work out their cancer fighting potential. Same is being done by Stanford University in order to analyse the role protein plays in keeping people healthy. The search for Extraterrestrial Intelligence (SETI) project is another example. Here, volunteers download and install a free program so as to process and analyse massive amounts of data in search of evidence of possible radio transmissions from extraterrestrial life. When tallying up all the processing power that these PC’s provide, it’s like having one big supercomputer. Grid technologies also played a major role in identifying the world’s largest known prime number. This was part of the Marsenne project where scientists identified the 43rd Marsenne Prime 230,402,457-1. – a figure that contains 9,152,052 digits.
Business Applicability
Within business communities, the Grid concept is far more popular among large corporations. Baum, the publishing editor for Oracle Corporation, states that these corporations are initially attracted by the amount of savings that the technology can provide. Mainstay Partners conducted an ROI study to evaluate the enterprise grid technology platforms currently in use at seven participating companies. It was concluded that the adaptation of grid technology yielded an average of 43 percent savings in hardware cost. Much of the savings were credited to the shift from a large symmetric multiprocessor server to a number of lower cost servers. With the use of Grid technology the latter setup delivered similar or at times even more computational power than the larger system, however with fewer costs. Baum’s report adds that the grids within these companies were being used for a variety of applications, including enterprise resource planning (ERP), decision support, customer relationship management (CRM), and supply chain management (SCM).
Still, companies that operate in the financial services industry, drug discoveries and weather modeling are initially more prone to benefit from Grid technologies, as they are involved in complex scientific and mathematical calculations and therefore require an added amount of computational power. So are companies that tend to process large amounts of data for their business intelligence activities. However, organisations are increasingly being enticed to adopt Grid technologies even for their transactional based systems, given that Grids may further facilitate storage space Issues.
Challenges faced by Grid Computing
IDC, the market intelligence and advisory services firm, are referring to Grid computing as the fifth generation of computing, after client-server and multi-tier (Table 1).
Yet, according to IDC, the technology still needs to be ‘normalised’ and has to overcome various challenges. IDC believes that these concerns, in some cases, are more perception than reality, and as organisations gain more experience with this distributed approach, their concerns will be laid to rest.
Additionally, a research conducted by the 451 Group shows that software licensing, security and bandwidth matters are among the things that can disturb grid rollouts.
Conclusion
Whilst Grid computing still needs to find broad acceptance in the commercial space, yet, market analysts state that the technology is here to stay. As Tom Hawk, the general manager of Grid computing for IBM says, “The Web is about sharing information. The grid is about sharing resources”.
Watch the video related to computer hardware
Q. How do you remove the static electricity in your body before you touch computer hardware so you don’t destroy it? A. The best option is to purchase an anti-static wrist strap. Alternatively install your PSU and connect power cord to outlet and touch a metal non-painted part of the case. Provided of course your home or office is properly grounded.
Help answer the question about computer hardware
When you major in Computer Engineering, does it relate to both computer hardware and software engineering?I'm trying to figure out if computer software engineering and computer hardware engineering both fall into computer engineering. Or does computer software engineering fall towards the side of computer science.




http://www.worldcommunitygrid.org/
http://en.wikipedia.org/wiki/Grid_computing
I think there is already a hub.
Apparently, this service is going to be pretty low cost when it finally breaks out! I am really excited
http://www.foxnews.com/story/0,2933,347212,00.html
Normal Windows versions do not support this. You need Windows 2000 Advanced Server or Windows 2003 Enterprise Server to run either server clustering or network load balancing. Server clustering means that all the computers act as one individual computer but take over when another fails. Network load balancing is similar but instead of acting as standby, the least busiest computer responds to the request. If you were to choose, network load balancing probably is the closest one to your needs. Otherwise I'm not aware of any software or hardware additions you may use other than special Linux or Unix distributions.
SETI
Current 'real world' efforts are based on a loose Distributed Computing model where Berkeley acts as the 'hub' and everyone else act as more-or-less independant 'stand-alone' Compute Nodes.
In cluster computing, a bunch of similar (or identical) computers are hooked up locally (in the same physical location, directly connected with very high speed connections) to operate as a single computer. The computers that make up the cluster cannot be operated independently as separate computers. A cluster, as far as any software or other computer is concerned, looks like essentially one big computer.
In grid computing, the computers do not have to be in the same physical location and can be operated independently. As far as other computers are concerned each computer on the grid is a distinct computer. Computers on a network have a program on them that allows unused resources (usually processing time and memory) to be used by another computer on the network. The speed of the connections between the computers on the grid are relatively slow (Ethernet speeds) compared to the speed of connections inside each computer, so processing tasks are broken up into independent chunks and sent out to different computers on the grid. When a computer is done with a chunk, it sends the results back to the server.
Roughly on a grid, a server log in to a bunch of computers (the grid), send them data and a program to run, and runs the program on those computers, which sends the data back to the server when its done.
In sum, a cluster is one large computer made up of small, similar computers, just as R.A.I.D. is one large hard disk made up of small hard disks. Whereas a grid is a bunch of computers that make their unused resources available to select computers (often a single server) over a network.
Grid computing is taking a large problem, splitting it into many small pieces, and running the pieces on a lot of different computers. Then the answers are put back together to get the final answer.
~
That is as simple as it gets.
Of course it is a simple analogy and it breaks down quickly under scrutiny but it gets the point accross.
The extra power gained would be negated by the processing needed to distribute the processing and network overhead. Sorry.
For distributed computing to work, you would need more computers. 5-10 is a bare minimum, depending on how powerful they are. Also, day to day computing won't benefit. Something continuously processor intensive, such as renedering an image or movie is better suited to distributed grid computing.
It can bi used for complex calculations that need lots of processing and will take a lot of time if being done on one machine.
They are different views and uses of a concept rather than the same exact thing.
Grid Computing tends to refer (in academic circles) to the science, protocols and technologies that enable massively distributed computer systems to be assembled across many origanisations. Due to the massive scale of these systems they are often homogeneous (i.e. many different kinds of large computers and protocols that find some common ground through the grid protocols).
Utility Computing (among commercial circles) is most commonly used to describe the ability to purchase computing power when it is needed, as a result utility computing tends to be more heterogeneous since it is often supplied by a single vendor.
Virtualization is the odd-man-out here as it's a concept that appears in both grid computing and utility computing models, as well as on smaller machines and describes the capability for a single hardware machine to appear to run as multiple machines through software. So it's popular in Utility computing because it's a nice way of selling "a machine" that doesn't really exist – i.e. the company sells the capability of a machine but owns an entirely different kind of machine altogether.