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Edward Huskin
Edward Huskin 14 September 2020

Clash of the Computing Titans: Grid Computing vs. Cloud Computing

Essentially, grid computing and cloud computing are two platforms with the same objective; each is designed to process tasks using large computer infrastructures.

The “cloud” has been a buzzword in the tech and business world ever since cloud platforms like Google Cloud and Amazon Web Services became mainstream. The appeal of cloud computing lies in its “on demand” nature; from running applications to providing storage options, it allowed users to access the service anytime and anywhere with an internet connection. It’s a double-edged sword when it comes to security because, on the one hand, companies don’t have to worry about it, but on the other, they would have to put their trust on a third-party provider.

This is one of the main advantages of an in-memory data grid (IMDG): you have complete control over everything. This ownership adds a layer of security to your system that cannot be matched by any cloud service provider. Essentially, grid computing and cloud computing are two platforms with the same objective; each is designed to process tasks using large computer infrastructures. They are both designed for distributed computing, a computing philosophy that processes tasks through a network of computers so that the tasks are divided and computing power is combined.

More important than the similarities, however, are the differences because these will help you decide which solution is best for your business, and why.

The Argument for Cloud Computing

The biggest appeal of cloud computing is the fact that companies need not purchase their own computing infrastructure, which means they need not worry about maintaining it, either. Providing servers, updating applications, and decommissioning software or hardware when it becomes obsolete becomes the responsibility of the cloud service provider. Using a cloud-based platform enables companies to execute projects faster and implement new processes without the high cost of infrastructure procurement.

The cloud allows for large storage capacities and access to data whenever and wherever there’s an internet connection. This is especially useful in today’s workplace, where data gets larger and the workforce isn’t always in the office. A cloud-based platform makes it easy to access data regardless of your geographical location because the service is designed to provide seamless service. It also allows for secure and quick backup and restoration of data. on demand.

The cloud allows businesses to become more agile, but it comes at a cost. Cloud computing isn’t necessarily the cheapest option. Depending on the nature of your business and your computing needs, it may be more practical to invest in a more long-term in-house solution. There is also the concern regarding handing over sensitive company data to a third-party that may also be used by competitors.

The Argument for Grid Computing

Grid computing is designed to handle complex computing tasks by allowing computers within the network or grid to work in parallel. By harnessing the combined power of several computers, a data grid is able to provide massive processing power not possible with a single computer. In-memory data grids also eliminate the need to always access disk-based storage, which is the common source of bottlenecks within a network.

An IMDG is deployed on a cluster of server nodes, sharing the available RAM and CPU of all the computers in the cluster. By using RAM, high throughput and low latency is ensured and the movement of data through the network is limited. This results in quicker data processing and efficient memory use. Scalability is also made simpler; scaling an IMDG is as simple as adding new nodes to the cluster. This cluster can also be used to create your very own private cloud configuration, creating a hybrid platform that provides the best of both worlds.

One thing slowing down the adoption of IMDG is the fact that RAM is more expensive than disk. Prices have gone down considerably through the years, however, and for what you get, RAM will more than pay for itself in the long run. To address problems related to limited RAM, data can be processed against the full dataset, which is referred to as “persistent store” capability. This allows the amount of data to exceed the amount of memory and allows for the optimization of data so that frequently used data resides in-memory while also being stored on disk where all data resides. A persistent store also allows immediate processing against the dataset after a system reboot without waiting for the dataset to load into memory.

Making the Leap

Ultimately, the decision on which platform is best will depend on a number of factors—not least of which is business need. Learning about the technologies is a good start in assessing whether your business requires a streaming analytics engine for complex data processing powered by machine learning or a simple computing platform that will allow you to store files offsite and run applications from a remote server. Determining the answer to this question will help you make a decision that will give your business a competitive advantage.

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