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Cloud Technology for Data Intensive Computing

Companies like Facebook and Google amongst others are facing challenges unique to themselves after the growth in internet – especially after Web 2.0. Most of the companies receive Petabytes and Terabytes of data every day, something which is of concern to the companies.

That’s why the companies are ready to pay a lot of money to hire Managed IT Support by www.resolutets.com and other such companies with an ability to secure the data; because the consequences of its breach or leak can be very devastating.

Cloud and fusion technology are the same.

Processing data from such companies require high computation resources. So, companies like Facebook and Google have massive privately owned data centers for storing and processing their data.

However, not many companies can afford such data centers to cater that kind of data-intensive computing. Small and medium-sized businesses, on the other hand, can utilize modern technology to compensate what they lose through cloud technology.

Cloud technology is the merging of various technologies like parallel, distributed and grid computing. It then incorporates all the benefits of such computing which offer an upper hand over traditional computing and medium. Main features of cloud technology include:

  • Storage space: cloud technology easily provides optimized storage system. This system can be used to store large volumes of data and other distributed storage architecture.

 

  • Elasticity: the cloud is very elastic, and this allows users to upgrade their virtual environments to suit the requirements they need for their computation. This scalability allows large volumes of structured or unstructured data to be analyzed and get processed. Also, the user is only allowed to pay for the utilized cloud resources for processing particular data.

 

  • API and framework: in cloud technology, the APIs are connected to specific storage infrastructure. This allows users to access a framework and programming APIs required to process massive volumes of data in an optimized manner.

Different architectures

Different architectures have been developed to allow the processing of such amounts of data. These architectures can be used to perform data intensive computing in the cloud, and they include:

  • Stream Processing: this architecture processes data using the concept of single program multiple data techniques. It has multiple computational resources which allow each member of input data to be processed independently. Sphere is an example of where stream processing is applied.

  • MapReduce: this is a common programming model used to process large data sets. The datasets usually have a parallel or distributed algorithm in cluster.

  • Hybrid DBMS: this architecture is designed to compress the benefits of traditional DBMS. The traditional DBMS utilizes shared parallel DBMS with MapReduce architecture. This architecture type offers superior data computing and high fault tolerance level at the same time.

  • Data flow: it processes data by copying a 2-D graphical form. It then presents the data dependencies using directed edges and arcs.

Challenges

Data-driven applications are designed to process terabytes and petabytes of data sets. However, it becomes a challenge to feed such volumes of data as data may not exist in a single location and may be distributed in different geographical location.

This consumes a lot of time and can result in delays in the data processing. The following are some of the major challenges faced in data intensive computing in the cloud.

  • A better signature generation – a technique which is vital for reducing data and improving processing speed.

  • Better Metadata management solution. The solution is used to handle diverse, complex and distributed data sources.

  • Design more advanced and flexible algorithms necessary for mining and processing large datasets.

  • Advanced computing platforms. These are designed to access in-memory multi-terabytes data structure.

Grid computing

Some of these challenges can be easily overcome through the smart use of grid computing. Grid computing provides high computational power as well as storage facility through extraction of various resources which can be found in different administrative domain.

Through the data network, users can perform data intensive computing and process the large data sets stored in different places.

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