One of the most lucrative markets in the world is the stock market. There are scores of investors and trading buying and selling stocks at any point of time when the market is open. This means there are countless strategies being played out in the market at any point of time. As the number of market participants increase, every trader will find that the time required for a transaction to go through also increases.
This is because of the lack of data processing or computing resources in the stock exchanges. The requirements of data processing has increased at such a rate that the conventional processors cannot handle the number of transactions coming through.
Data grid is a system that uses grid computing technique. Grid computing technique essentially utilizes the processing power of several computers that are connected to form a network. This network may be private public or also the internet. The current grid computing is done centrally where all the transactions are handled. This system crumbles when there are thousands of transactions being processed at once. The traders will experience downtime in such scenarios which is totally unexpected. If there is a grid that is the size of Europe, you need scores of computers connected in a network to process the data.
This can be expensive and cumbersome. Current developments are trying to make this process much cheaper by using the internet. At any point of time, there are millions of computers that are connected to the internet and are lying unused. If a part of the processing power can be utilized, then the load on the central administrator becomes less which reduces the risk of possible downtime. By employing this technique, exchanges can reduce the administrative costs of handling each transaction. Another advantage that this type of system has is scalability. The resources can be scaled up during peak hours of trading by utilising more computers connected to the internet. This type of system is however very complex to design and it is usually done in a phased manner.
Most of the exchanges have not implemented the latest system described above as this is still in the research phase where they are trying to validate its use. The more conventional approaches are using the computing facilities in the exchange itself. There are several systems that are idle at any point of time. The exchanges try to use these resources to reduce the operational time in settlement and clearing system. This kind of grid computing needs support at the software level that can allocate resources depending upon the various needs.
Managing data is one of the biggest challenges for the stock exchanges. Grid computing technology has addressed this challenge and has provided the ideal solution. It allows the stock exchange to share and manage distributed data with its traders much more efficiently. This efficiency translates to lesser time required for transactions between buyers and sellers and reduction of the costs involved for such transactions. The world is certainly becoming faster.