Disadvantages of Grid Computing Described

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Since its introduction, the concept of grid computing has acquired great popularity, even greater than the Web itself had at its beginning. The concept has not only found its place within numerous science projects (in medicine e.g.), but is also being used for various commercial applications. Furthermore, grid computing is particularly suitable for resource-demanding projects and enables scientists to work in a completely new way. Despite all its advantages, there are still features which have not yet been developed, and there are also certain disadvantages of grid computing which are discussed within this article.

What is Grid Computing?

Grid computing is a type of data management and computer infrastructure, designed as a support primarily for scientific research, but, as said in the introduction, also used in various commercial concepts, business research, entertainment and finally by governments of different countries. On its simplest level, the grid computing concept integrates four components: information, computation, networking and communication. When these components are connected into the grid, the result is a virtual platform which allows an advanced data and computation management. If this concept is implemented into the areas mentioned above (science, etc.), it provides a platform where resources can be dynamically linked together, while these resources are then used to support the execution of applications that require significant amounts of computer resources.

Disadvantages of Grid Computing

Grid computing has great potential, but there are still absent features waiting for implementation. Furthermore, there are still particular disadvantages of grid computing that must not be overlooked.

As expected, the first disadvantage concerns the relative immaturity of the concept. This is especially noticeable when talking about the missing features, non-defined standards and software. On the subject of software, some applications require modifications in order to use all the benefits of grid computing. It is also important to mention that applications which are not designed to use an MPI (Message Passing Interface) will have to revert to an SMP (Symmetric Multi-Processing).

The biggest disadvantage of grid computing though, concerns processes and their results. More specifically, the results of all processes are sent first on all nodes within the grid, and then collaboratively assessed. Before the final assessment is made, it is not possible to define or to declare a final outcome. This is particularly a problem when talking about time sensitive projects. Furthermore, another important disadvantage of grid computing is that it relies heavily on dispersed data management (which is a very important concept in cloud computing) and connectivity (connectivity errors may occur unexpectedly).

Furthermore, grid computing requires an advanced infrastructure: small servers, fast connections between the servers and finally, in order to maximize the potential of that infrastructure, it requires the use of quality tools, software and skilled technicians to manage the grid. In other words, when all these components are put together, it is obvious that this technology is costly. In certain occasions, it is not possible to change how data is displayed on screen. Naturally, as it is a case with every computing network, it is important to pay a maximum attention to security issues.

Finally, there is also a problem with people who don’t want to share their resources, despite the fact that everybody involved with the resource sharing would benefit.


Despite the described disadvantages of grid computing, this technology has many benefits and it seems likely that it will be used more and more in different computational grid projects, especially for biomedical, industrial and financial research, but also in chemistry and medicine. One of the most famous exponents of grid computing is the European Organization for Nuclear Research (CERN), as they use (among others) the concept for the Large Hadron Collider (LHC).


K Data Grid - https://www.kdatagrid.com/

Joseph, Joshy & Fellenstein, Craig. Grid Computing, Prentice Hall PTR, 2004.