What are the factors for successful cloud deployment

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written 8.0 years ago by

teamques10 ★ 68k

Cloud migration assessments comprise assessments to understand the issues involved in the specific case of migration at the application level or the code, the design, the architecture, or usage levels.
The first step of the iterative process of the seven-step model of migration is basically at the assessment level. Proof of concepts or prototypes for various approaches to the migration along with the leveraging of pricing parameters enables one to make appropriate assessments.
These assessments are about the cost of migration as well as about the ROI that can be achieved in the case of production version.
The next process step is in isolating all systemic and environmental dependencies of the enterprise application components within the captive data center. This, in turn, yields a picture of the level of complexity of the migration.
After isolation is complete, one then goes about generating the mapping constructs between what shall possibly remain in the local captive data center and what goes onto the cloud. So a substantial part of the enterprise application needs to be rearchitected, redesigned, and reimplemented on the cloud.
This gets in just about the functionality of the original enterprise application. Due to this migration, it is possible perhaps that some functionality is lost.
In the next process step we leverage the intrinsic features of the cloud computing service to augment our enterprise application in its own small ways. Having done the augmentation, we validate and test the new form of the enterprise application with an extensive test suite that comprises testing the components of the enterprise application on the cloud as well. These test results could be positive or mixed.
In the latter case, we iterate and optimize as appropriate. After several such optimizing iterations, the migration is deemed successful.
This Seven-Step Model process is for optimizing and ensuring that the migration into the cloud is both robust and comprehensive.

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