Grid Computing

The grand vision is often presented as an analogy to power grids where users (or electrical appliances) get access to electricity through wall sockets with no care or consideration for where or how the electricity is actually generated. Grid Computing aims to enable resource sharing and coordinated problem-solving in dynamic, multi-institutional virtual organizations.  we could spur the creation of a Computing Grid, analogous in form and utility to the electric power grid. The key element of grid computing is the underlying distributed computing infrastructure that supports cross-organization resource sharing. Grid computing uses parallel processing where a program is divided into several tasks and each computer on the grid is assigned a task to work on. The output is a combination of results from all computers on the grid. 

Figure 1: Shows how grid computing works. Credit to ecomputernotes.com for the image.

Benefits of grid computing

1. Ability to exploit underutilized resources

If a task has been assigned to a certain computer X on the grid which is busy due to peak activity, the grid control node will look for an idle computer on the grid and assign that task to it. Grid computing balances resource utilization in a way that if applications are grid-enabled, they can be moved to underutilized machines during such peaks.

2. Parallel CPU Capacity

Algorithms that can be partitioned into independently running parts. A CPU-intensive grid application can be thought of as many smaller sub-jobs, each executing on a different machine in the grid. This improves the scalability of the application since the jobs don’t communicate with each other.

3. Virtual Resources and Organizations

Grid computing is to provide an environment for collaboration among a wider audience. Sharing starts with data in the form of files or databases. A data grid can expand data capabilities in several ways. Sharing is not limited to files, but also includes other resources, such as specialized devices, software, services, licenses, and so on. These resources are virtualized to give them more uniform interoperability among heterogeneous grid participants. 

4. 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. For example, if a user needs to increase their total bandwidth to the Internet to implement a data mining search engine, the work can be split among grid machines that have independent connections to the Internet. 

5. Resource Balancing

A grid federates a large number of resources contributed by individual machines into a large single-system image. For applications that are grid-enabled, the grid can offer a resource-balancing effect by scheduling

Comparisons of Grid Computing with Cloud Computing

Architecture

Grids were introduced to address large-scale computation problems using a network of resource-sharing commodity machines that deliver the computation power affordable only by supercomputers and large dedicated clusters at that time. The major motivation was that these high-performance computing resources were expensive and hard to get access, so the starting point was to use federated resources that could comprise compute, storage, and network resources from multiple geographically distributed institutions, and such resources are generally heterogeneous and dynamic

Clouds are developed to address Internet-scale computing problems where some assumptions are different from those of the Grids. Clouds are usually referred to as a large pool of computing and/or storage resources, which can be accessed via standard protocols via an abstract interface

Applications

The main function of grid computing is job scheduling using all kinds of computing resources where a task is divided into several independent sub-tasks and each machine on a grid is assigned a task. After all the sub-tasks are completed they are sent back to the main machine which handles and processes all the tasks. Cloud computing involves resource pooling through grouping resources on an as-needed basis from clusters of servers.

Resource Management

Grid computing is based on a distributed system which means computing resources are distributed among different computing units which are located across different sites, countries, and continents. In cloud computing, computing resources are managed centrally are located over multiple servers in clusters in cloud providers’ private data center

When to choose Grid over cloud computing.

If the client wants to address large-scale computation problems he/she may have to choose Grid computing due to its ability to use a network of resource-sharing commodity machines that deliver the computation power affordable only by supercomputers and large dedicated clusters. Clouds were developed to address Internet-scale computing problems where some assumptions are different from those of the Grids. Clouds are usually referred to as a large pool of computing and/or storage resources, which can be accessed via standard protocols via an abstract interface