Distributed Computing In Microsoft Azure

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Because not everyone understands the differences between grid computing and cloud, let’s start with a quick overview. While cloud computing and cloud technology are not synonymous, there are several synergies between the two, and combining the two makes a lot of sense.

Instead of a single computer, grid computing involves handling a computing issue with only an army of devices working in parallel. This method has several advantages:

Time savings: if you had 30 machines committed to the problem, a month’s worth of processing labor for a windows machine might be completed in a single day. By leveraging thousands of volunteer computers, the [email protected] project, the world’s largest grid computing initiative, has recorded 2 million years of aggregate computer power time in only 10 years of sequential time.

Less expensive resources: Instead of purchasing massive servers with top-of-the-line CPUs and memory, you can employ less costly materials to get work done. Granted, you’ll have to purchase more, but the smaller, less expensive machines are easier to repurpose.

Reliability: A computer and network system must be able to foresee individual computer breakdowns or changes in availability and not let this impede the job from being completed successfully.

Grid computing is not appropriate for all forms of work. The work is split into smaller tasks, which are completed in parallel by a loosely linked network of computers. Smart infrastructure is required to distribute the duties, collect the outcomes, and operate the system. Grid computing was first used by people who wanted to address massive computer issues, which is unsurprising. Grid computing is now employed in genomics, actuarial calculations, astronomy analysis, & film animation rendering, among other fields. But that’s changing: network virtualization is gaining traction as a solution to broad business problems, and the arrival of cloud computing will hasten this trend. Grid computing does not have to be used for massive computing tasks, and compute-intensive projects are not the only type of work that can profit from it. Grid computing is a suitable fit for any activity that is repetitive. Grid computing may make sense if you’re a large company with 4 million monthly invoices or a small firm with 1,000 credit card applications to approve. Because network virtualization is a couple of years older than cloud technology, much of today‚Äôs modern grid computing isn’t cloud-based. The following are the most prevalent approaches:

Dedicated machines: buy a lot of computers and set them aside for grid work.

Develop a Secure Distributed System in the Azure Cloud | by Nuwan Alawatta | Medium

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Network cycle stealing: when other machines in your business are inactive, such as overnight, repurpose them for grid work. At night, a business desktop might transform into a grid worker.

Applying the cycle stealing principle on a global scale via the Internet is known as global cycle theft. With almost 300,000 active computers, that’s how the [email protected] initiative works.

Cloud computing provides an alternative to grid computing that has several appealing features, including a flexible scale-up/scale-down business model and the availability of much of the infrastructure required that was previously custom-developed.

Grid Computing Business Applications

Distributed transactions across cloud databases - Azure SQL Database | Microsoft Learn

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Grid computing must have compelling business applications to gain broad acceptance. Let’s look at three types of commercial applications that benefit from grid computing while also providing significant business value. Data mining is the first example. Big data and other types of data mining can uncover fascinating patterns and relationships in corporate data.

A supermarket could employ data mining to analyze buying habits and make strategic decisions about whether or not to put particular items on sale.

Data mining could be used by a DVD rental chain to identify which other rentals to recommend to people, depending on what they’ve just rented.

An e-commerce website could use data mining to analyze real-time user navigation patterns and personalize the offers and adverts displayed.

The second scenario is decisioning, which necessitates the execution of a battery of the forward business rules in addition to making a judgment call. In some situations, choices must be made rapidly, but the computations required are difficult. The grid’s parallelism can be used to achieve faster response times that don’t decrease as the workload grows.

  • A credit institution could use a decision engine to calculate credit scores.
  • A financial institution could use a decision engine to approve loans quickly.
  • An insurance firm could use a decision engine to calculate risk and allocate policy rates to applicants.

The third scenario is batch processing, which is used when you need to manage bursts of high workloads but may not have the necessary in-house capability.

Electronic tax documents must be generated and delivered by a tax provider. The workload is high during tax season and moderates during the rest of the year. Using a grid method to document creation and delivery saves money by avoiding investing in massive in-house capabilities that would be unused most of the time.

An advertising campaign may require sending massive amounts of email accounts, faxes, mailings, or voice messages. The grid’s parallelism allows the campaign to be sent in a short, synchronized time frame, regardless of the amount of work involved.

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