Enterprise adoption of cloud computing with application portfolio profiling and application portfolio assessment

Abstract

AbstractWith the continued appeal and adoption of cloud computing, an assessment of cloud run costs and migration affordability prior to adoption would assist enterprises that have several legacy applications targeted for cloud migration. However, as cloud migrations have become more prevalent, many have been characterised by unsuccessful migration or application modernisation attempts. The primary reason behind the failed attempts is insufficient planning upfront, to identify which legacy applications are suitable to realise the benefits of public or private cloud, leading to time and cost overruns. There is a need for strategic decision making for application portfolios to mitigate the risks of cost overruns and migration delays. Thus, a Rough Order of Magnitude (ROM) of cloud run costs for an application portfolio is required in the planning phase as an input into IT governance. To obtain the ROM cloud run costs, it is necessary to baseline application data, preferably through automated discovery, and perform quantitative analysis of the applications. Therefore, we propose an approach to (a) baseline application data using Application Portfolio Profiling (APP), and (b) perform quantitative analysis of applications using an Application Portfolio Assessment (APA), to inform the legacy application migration decision. APP and APA are proposed as part of a Cloud Computing Considerations for Companies (CCCC) framework that enables an enterprise to make an informed decision regarding which legacy applications are to be migrated as part of enterprise Cloud Computing adoption. This decision is important because of the change in operating model, infrastructure requirements, hidden costs and commercial models inherent with cloud computing adoption. We validate the proposed framework through applying it to a real-world use case scenario that provides the necessary coverage to test the proposed framework.