Grid computing is a processor architecture that associates computer resources from various areas to reach an objective. In grid computing, an individual computer can connect with networks of computers that can perform the task together, thus working as a super processor. It is a form of interconnected computer systems where the machines utilize the same resources collectively. The concept of grid computing invented in the early 1990s as a symbol for making powerful computers as easy as to work with power grid. A computational grid is a collection of mixed type of computers and the resources spread across numerous administrative fields with the resolute of providing users easy access to these resources. Speaking technically, Grid computing enables the virtualization of distributed computing and data resources such as processing, network bandwidth and storage capacity to create a single system image, granting users and applications seamless access to vast information technology
Architecture of Grid Computing
It is a form of distributed computing that contains organizing and sharing computational power, data storage and interconnections across dynamic organizations. It is a model which is used to provide solutions for data sharing and analysis for engineering sciences, industry and commerce. It can be considered as data sharing systems with non-interactive workloads which involves a large number of files (Collection of data). Due to increasing in the number of applications, the utilization of Grid Infrastructure has radically better to meet the need of data sharing, computational, storage and other needs. All the resource needs of today’s demanding applications cannot simply meet in a single location or a site, therefore by using distributed resources can carry many benefits to the users of applications. It can be an effectual organization of heterogeneous, geographically distributed and dynamically available resource by deploying in Grid Computing.
In the current world data analysis plays a major role in major industries, thus handling all the data sharing and storing can give up to Grid Computing for better performance. Big data is business transformation. So every organization is trying to analyze their big data. Big data poses implementation problems in extreme conditions. There are some methods to use grid computing along with Hadoop.
METHODS OF GRID COMPUTING
∙ Distributed Supercomputing: It combines multiple high-capacity resources on a computational grid into a single, virtual distributed supercomputer. And Tackle problems that cannot be solved on a single system.
∙ High-Throughput Computing: Using the grid to schedule large numbers of loosely coupled or independent tasks, with the goal of putting unused processor cycles to work. Thus the high throughput computing was achieved.
∙ On-Demand Computing: It has a capability to meet short-term requirements for resources that are not locally accessible and its models real-time computing demands.
∙ Data-Intensive Computing: The focus is on synthesizing new information from data that is maintained in geographically distributed repositories, digital libraries, and databases. Data intensive computing is particularly useful for distributed data analysis.
∙ Logistical Networking :It concern with global scheduling and optimization of data movement. It is contrasts with traditional networking, which does not explicitly model storage resources in the network and by called "logistical" because of the analogy it bears with the systems of warehouses, depots, and distribution channels.
BENEFITS OF GRID COMPUTING
Exploiting underutilized Resources One of the basic uses of grid computing is to run an existing application on a different machine. The machine on which the application is normally run might be unusually busy due to a peak in activity.
∙ Parallel CPU capacity The potential for massive parallel CPU capacity is one of the most common visions and attractive features of a grid. This computing power is driving a new evolution in many fields.
∙ Virtual resources and virtual organizations for collaboration Another capability enabled by grid computing is to provide an environment for collaboration among a wider audience.
∙ Access to additional resources In addition to CPU and storage resources, a grid can provide access to other resources as well. The additional resources can be provided in additional numbers and/or capacity.
∙ Resource balancing For applications that are grid-enabled, the grid can offer a resource balancing effect by scheduling grid jobs on machines with low utilization.∙ Reliability High-end conventional computing systems use expensive hardware to increase reliability.