Grid Computing – Now and future development

There are lots of problems and questions need to be solved in academy. It is time consuming for scientists to solve the problems even they use supercomputer to compute the result. However, the grid computing do a great job on solving some complicated problems that should be done by supercomputer traditionally. Although grid computing is not so popular in normal computer users compare with cloud computing and cluster computing, grid computing is quite popular on solving some professional problems such as scientific and mathematical questions.

What is Grid Computing?

Grid computing refers to the combination of computer resources from heterogeneous computing device to reach a common goal. Although grid computing is a kind of distributive computing, it is different from cluster computing-based systems. In grid computing, each device can be widely spread.  The devices need not be the same computational architecture. They need middleware to divide and allocate the job that the devices are responsible to compute through network which is typically the Internet or Ethernet. There are several big projects using the grid computing to solve the problems, for example, Genome@Home, Folding@Home, MilkyWay@Home etc. All these projects contribute a lot to human society.

Advantage of using Grid Computing

There are many advantages to apply gird computing in scientific, mathematical and academic problems through volunteer computing. This means the people, who share their computing power from their device, will not get any money from institutes. It is voluntary base. The institute does not need to pay a lot to obtain a great computing power. Also, the cost of combining multiple processors in a normal Personal Computer is far lower than cost of making a tailor-made supercomputer due to the mass production of normal processors lower the cost of normal CPU. With grid computing, multiple devices can produce similar computing resource as multi-processor supercomputers. Hence, cost is relatively low compare with setting up a tailor-made super computer to solve the problems. Besides, the institutes need not to have lots of space to distributive computing-based system or a super computer. The maintenance cost can also be saved. 

Disadvantage of using Grid Computing

Although grid computing can lower the cost of computation, there are some restrictions. Firstly, as the devices in grid computing may not have stable and instant connections with other devices, the computation should be dived in the part that is highly parallel and can be solved independently. This increases the difficulty on designing the programme. Moreover, grid computing is mainly relying on voluntary computing. This limits the maximum computing power of grid computing.

Future Development

In past few years, grid computing mainly relies on the CPU as the main processing source. More and more computing power is made by the Graphic Processing Unit (GPU) and the Cell processor in the PlayStation 3 (PS3). These processors have higher Floating Point Operations (FLOPS), which is the main operation of scientific problems, than the traditional CPU in traditional PC. As GPU is not always have heavy work, there are more idling time compare with the CPU. Also, it is impossible for the gamers to play with their PS3 all the day. That means more available computing power. Therefore, the computing power from the GPU and PS3 can be easily obtained compare with CPU. In the future, there are more PCs equipped with high computing power GPU to fulfill the needs of multimedia. With certain promotion of grid computing, it is believed that grid computing will provide a considerable computing power and recycle more idling computing power.

Secondly, more devices have high computing power, such as mobile phones, tablets and Home Theatre PCs (HTPC). Their computing power is increasing while their power consumption is decreasing. They can be the potential devices of gird computing. Plus, development of high speed connection to the Internet through traditional connection and wireless connection makes grid computing easier than before. It is believed that more computing devices can join the grid computing and share the idling computing power. At the same time, the institutes can join their supercomputers and mainframe to speed up the processing. This may form an ultimate virtual supercomputer.

In shorts, grid computing will still develop to help scientists to study difficult problems in future. With more computing devices are available to join grid computing model. The potential of developing grid computing model is unlimited. Also, it is better to promote people to join the grid computing to share their idling computing power in order to greatly boost up computing power of grid computing. Let their devices to join the grid computing is better than idling their devices and do nothing. At least, there are some contributions to society. It is no doubt that developing grid computing is beneficial to us.

Reference

1.    Grid computing – Wikipedia. Retrieved October 2011 from Wikipedia: http://en.wikipedia.org/wiki/Grid_computing

2.    Folding@home. Retrieved October 2011 from Folding@home http://folding.stanford.edu/English/Main

3.    Folding@home – Wikipedia. Retrived October 2011 from http://en.wikipedia.org/wiki/Folding@Home

Grid Computing: Future Development

Wong Lok Kwan

Grid is an infrastructure that enables heterogeneous geographically separated clusters of processors to be connected in a virtual environment (network). Grid computing is a technical means to solve problem by sharing and applying the resources of many computers in the network. There are three main reasons that make grid computing popular. The first one is making more cost-effective use of a given amount of computer resources. Besides, it is a good way to solve problems that can't be approached if not enough amount of computer power is provided. And the last is that it suggests that all the resources of the member computers can be cooperatively harnessed and managed as collaboration toward a common goal.

Due to its many advantages, some models are developing and designing in progress for grid computing. Examples are as follows. 

Corba, it is a set of facilities linked through “off-the-shelf” packages. It is a client server model using web based technology. And it wraps the existing code used in Java objects. What is more, it utilizes current public key security techniques. Furthermore, it also exploits thread level and object level parallelism.

Legion, it is a single unified virtual machine that tying a large number of objects and processors together. This is object oriented machine. Each object defines the rules for access so that the object interfaces will be well defined. In addition, there is a core set of objects which provide basic services. So, the users can create and define their own objects easily and in a comfortable way. Besides, legion works with high performance, users can first consider the load and job affinity then select the hosts. The presence of object wrapping characteristic help to support the parallel programming. And because the user autonomy exists, users can choose security arrangements as well as the scheduling polices.

Globus, it is an integrated ”bag” of basic grid services. Its middleware layered architecture structure helps to builds global services on top of core local services. Apart from that, translucent interfaces to core Globus services, well defined interfaces that can be directly accessed by applications. In addition, its system can be improved like incremental implementation or other existing tools can be enhanced or replaced if needed.

In fact, grid computing has been promoted as the global computing infrastructure of the future for several years. Some scientists even considered grid computing as one of the major sources of the impact to technological and scientific changes on the society and economy, because of the fact that the usage of large amount of data becomes crucial to many aspects.
However, there are still some challenges existing, including the latency limitations, improved protocol schemes, and additional grid-based tools are needed.

But the most important thing is the cost problem. In order to amortize the substantial costs of managing these large data volumes (the key property of computing grid), the cost will usually be shared by scientists. Besides, large IT system is required to exploit the huge amount of data volumes. However some disciplines with no experience in administrating and managing these complex systems will be affected. These users can neither afford to establish a sufficiently large local compute nor to manage suitable IT systems by their own. Although the significant investments in the grid concepts, it recently draw less attention. Instead, some new concepts like cloud computing seem to replace it.

In fact, computer users will give up if the expected benefits dwarf the transition costs. In this way, we have to enhance the both sides. Many of the publications enhance the eventual benefits, but at the same time should we focus on the transition costs for the user? Can we identify user groups who want to use grid computing so that they may help us to make the system becomes mature then to reduce transition costs? However, this approach will focus on the core services and may create barriers in building a grid with higher level services. But we should consider carefully that do most users actually need such a high level services grid?

In short, there are many advantages of grid computing which is worthwhile to use it. But the inevitable fact is that there are still many problems should be solved first. If those problems are solved, grid computing will be one of the best techniques.