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What are some of the positives of a demand-driven migration model?

Q:

What are some of the positives of a demand-driven migration model?

A:

Using a demand-driven migration model can help businesses to refine the ways in which they move data and upgrade from legacy systems to new cloud-delivered setups and other kinds of innovative plans. Using a demand-driven approach delivers some value in terms of efficiency and automation.

In a sense, demand-driven migration is a key automation feature in a plan to move data. One of the main benefits is that it decreases the time and effort required of system operators or administrators. These professionals can apply their time to other key tasks in maintaining and scaling operations.

As a way to automate, a demand-driven model can also eliminate some kinds of problems with data backups. For example, experts may talk about using an event-driven or demand-driven model to move data into storage media, rather than using an interval model that is scheduled and planned. A demand-driven model can help prevent problems related to monitoring thresholds – if human decision-makers have to look at capacity, and they fail to see a problem occurring, they may have to do damage control later, whereas automated migration provides the ability of machines to learn about the problem and correct it on their own.

In some cases, the use of a demand-driven model can help companies to avoid making decisions on old data. Where other migration models may only update systems periodically, some types of new autonomous systems are essentially updating in real time, so that numbers are always current.

Demand-driven migration and related processes can also help with some kinds of system problems such as the over-reserving of resources to be allocated in the future. In some cases, it can help with latency and packet loss, or other problems that occur based on high demand and high traffic in a network area.

Demand-driven systems are best when complemented by fully automated network services that can help predict both demand and costs. Cost forecasting is built into some of these services, for instance, to compare theoretical cloud use to an on-premises system. In other words, while the company is trying to migrate, they can be looking at predictive models to show “what-if” scenarios and how much each one would cost. They can compare monthly cloud costs to current one-time on-premises costs and make decisions about what's best for the company. All of this kind of automation and predictive analytics drives more efficient change and a greater level of success for a business that is moving to a newer operational model.

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Written by Justin Stoltzfus
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Justin Stoltzfus is a freelance writer for various Web and print publications. His work has appeared in online magazines including Preservation Online, a project of the National Historic Trust, and many other venues.
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