Grid computing technology

Grid Computing Technology


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Keywords grid computing, stock market, prediction, artificial neural network

1. Introduction

Grid computing technology is now not limited to researching fields only, it now being getting used in different areas including education, multimedia, medical & pharmacy, businesses etc where there is a demand of high computational power or IT resources. The main advantage of grid technology is that it's dynamic in nature i-e according to the requirements of computational resources and be added or removed. Grid computing also opened a new dimension of revenue in the form of cloud computing where the resources owner sells its unused computational resources on the utility basis.

Stock Market which is a very dynamic in nature and requires a very robust and fast IT infrastructure to facilitate the handling of millions of transactions getting made by the investors in trading and also to help in predicting the future stock market share rates by providing fast processing and computational power to the artificial neural network which is commonly used in predicting stock market trends. Grid computing technology can increase the efficiency of the ANN (Artificial Neural Network) by combining scalable yet fast computational resources which is not possible by traditional machines or a computer network.

2. Background Review

Grid Computing is a dominant and proficient computational technology which "enable the sharing, selection, and aggregation of a wide variety of geographically distributed computational resources (such as supercomputers, computer clusters , storage systems, data sources) and presents them as a single, unified resource for solving large-scale computer and data intensive computing applications using a specialized software". [1] Grid computing can also be referred as an advance form of distributed networking which links databases, servers and applications to appear as a single large system using a specialized system. The components in the grid are heterogeneous i-e they can be of different type using different hardware and operating systems. Dr. Ian Foster recognized as the "father of grid computing," Foster is an IBM researcher who is currently teaches at the University of Chicago and also work as a head of the Distributed Systems Lab at Argonne National Laboratory.

The Globus Alliance comes into existence when Dr. Ian Foster worked with his fellow researchers Steven Tuecke and Carl Kesselman of the University of Southern California to establish the Globus Project. Globus Project toolkit is an open source software development endeavour focused on building computational grids. The project received recognition in the form of funding from the US government and grid projects started to be up in several US government agencies, based on Globus Project software.[3] The Globus Alliance & its partners also established the Open Grid Services Architecture (OGSA) which is a set of standards for forming computational grids. Furthermore Globus Alliance also created the Open Grid Services Infrastructure (OGSI) on the basis of OGSA which defines how to produce, control and communicate among grid services.

Grid computing technology is spreading rapidly in the commercial sector as Grid computing provides inexpensive, robust and scalable solution for the computational requirements for the businesses. Grid computing allows people working in a company to analyse the huge customer data within minutes instead of days and allows researchers to share and compute the scientific data with ease. Grid computing is now being use in researching, education, molecular modelling, army defence systems, engineering simulations and in high-energy physics fields.

3. Personal Recommendation

The implementation of grid computing technology in stock exchange market can be very vital since stock market is highly dynamic in nature where millions of transactions done by the investors in trading shares, because of the deficient of computing resources in the stock exchanges the needs of data processing has increased at such a rate that the conventional processors or systems cannot handle the number of transactions coming through. A grid computing can handle huge amount of data set by forming a networking of computers which can be as big as the whole Europe or can be the internet.

Grid computing can also be used to for the prediction of the stock market. The most important technique involves the use of artificial neural networks. Artificial neural networks can be thought of as mathematical approximation function. Grid computing provides robust computational power from the combination of different computing resources to power an artificial neural network which is used to predict the stock market prices. The grid computing based neural network not only processes several times faster than a single iterative approach but also provides increase the prediction/forecasting accuracy by analysing the real market data feed into the neural network.

The grid computing technique in the stock market can be implemented by connecting stock exchanges with a grid data-centre containing different type of computational resources including parallel, distributed and simple network configurations which can work together to compute, handle and predict stock market trends, transactions and market share prices using sophisticated simulation tools and neural network based software for predicting stock market. The data can be feed into the grid data-centre by the investors, share holders, companies CEOs etc where as the neural network can be set on the automated learning mode by analysing the data which is getting feed into the data-centre.

Since this whole implementation require very fast & massive computational resources, grid computing environment in the stock market will handle it pretty well, more over grid computing is scalable as well and don't have a single point of failure.

4. Conclusion

Even though I have discussed advantages of grid technology in certain field where massive computational processing power is required like stock exchange and how grid computing is spreading rapidly in different application areas but there are few downsides of grid computing exist as well like complexity in building middleware structures that can bind together collections of resources to work as a unit across network connections that often span continents. Despite of the downsides gird computing technology is emerging and lots of enterprises & companies like IBM investing into it. In the near future gird technology will be used commonly in various application areas.

5. References

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