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De-convolving migration methodology via detailed assessment and cognitive learning - Generating migration assessment report

机译：通过详细的评估和认知学习来消除迁移方法的不足-生成迁移评估报告

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摘要

The application (workload) migration process from the Mainframe to the Cloud environment turns out to be quite complicated: error-prone, time-consuming and costly. Even worse, the application may not work correctly after the sophisticated migration process. Existing approaches mainly complete this process in an ad-hoc manual manner and thus the chances of error are very high. Thus how to migrate the applications to the Cloud platform correctly and effectively poses a critical challenge for the IT service industry as well as the enterprise clients. This paper dissects the migration process into various touch-points to be addressed while deciding for the Migration and addresses it using an adaptive assessment to generate scores for various possible solution approaches to Migration; which are then used to decide upon an approach for the workload.
机译：从大型机到云环境的应用程序（工作负载）迁移过程结果非常复杂：错误 - 容易出错，耗时且昂贵。更糟糕的是，在复杂的迁移过程之后，应用程序可能无法正常工作。现有方法主要以ad-hoc手动方式完成此过程，因此误差的机会非常高。因此，如何正确地将应用程序迁移到云平台，并有效地对IT服务行业以及企业客户构成危急挑战。本文将迁移过程解剖到要解决的各种触摸点，同时决定使用自适应评估来为其寻找分数以获得各种可能的解决方案迁移方法;然后用于决定工作量的方法。

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