Grid Computing

Grid or Cloud computing is a general term for anything that involves delivering hosted services over the Internet. These services are broadly divided into three categories: Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS). The name cloud computing was inspired by the cloud symbol that's often used to represent the Internet in flow charts and diagrams.

A grid computing service has two distinct characteristics that differentiate it from traditional hosting. It is elastic meaning that you can have as much or as little of a service as you want at any given time, you needs nothing but a personal computer and Internet access. Significant innovations in virtualization and distributed computing, as well as improved access to high-speed Internet and a weak economy, have accelerated interest in cloud computing.

In the software-as-a-service cloud model, ePowerBilling supplies the hardware infrastructure, the software product and interacts with the user through a front-end portal. Services can be anything from web-based email to inventory control and database processing.

That means you are free to use the service and have real time access to your business accounts from anywhere!!!
When it comes to its billing process, ePowerBilling uses the full power of the Grid Computing methodology. In doing so, you will be able to assign multiple machines (computers) to the common task of billing. As billing may become increasingly timely as customer base grows, Grid Computing is the much needed solution to a problem where time is of the essence.
The primary advantage of distributed computing is that each node can be modest desk computer hardware, which when combined can produce similar computing resources to a multiprocessor supercomputer, but at lower cost. This arrangement is well suited for billing where multiple parallel computations can take place independently, without the need to communicate intermediate results between processors.

Shared Computing

Shared computing creates a “grid” from the unused resources in a network of participants (whether worldwide or internal to an organization). Typically this technique uses desktop computer instruction cycles that would otherwise be wasted at night, during lunch, or even in the scattered seconds throughout the day when the computer is waiting for user input or slow devices.
In practice, participating computers also donate some supporting amount of disk storage space, RAM, and network bandwidth, in addition to raw CPU power.

Grids versus conventional supercomputers

“Distributed” or “grid” computing in general is a special type of parralel computing that relies on complete computers (with onboard CPU, storage, power supply, network interface, etc.) connected to a network (private, public or the Internet) by a conventional network interface, such as Ethernet. This is in contrast to the traditional notion of a supercomputer, which has many processors connected by a local high-speed computer bus.

The primary advantage of distributed computing is that each node can be purchased as commodity hardware, which when combined can produce similar computing resources to a multiprocessor supercomputer, but at lower cost. This is due to the economies of scale of producing commodity hardware, compared to the lower efficiency of designing and constructing a small number of custom supercomputers. The primary performance disadvantage is that the various processors and local storage areas do not have high-speed connections. This arrangement is thus well suited to applications in which multiple parallel computations can take place independently, without the need to communicate intermediate results between processors.

The high-end scalability of geographically dispersed grids is generally favorable, due to the low need for connectivity between nodes relative to the capacity of the public Internet.

There are also some differences in programming and deployment. It can be costly and difficult to write programs so that they can be run in the environment of a supercomputer, which may have a custom operating system, or require the program to address concurrency issues. If a problem can be adequately parallelized, a “thin” layer of “grid” infrastructure can allow conventional, standalone programs to run on multiple machines (but each given a different part of the same problem). This makes it possible to write and debug on a single conventional machine, and eliminates complications due to multiple instances of the same program running in the same shared memory and storage space at the same time.