Application Portfolio Profiling and Appraisal as Part of Enterprise Adoption of Cloud Computing

Abstract

Computation resources have never been more affordable, powerful, or readily available than they are now, thanks to the Internet and the rapid advancement of processing and storage technologies. In response to this new trend in technological development, a brand-new computer paradigm is known as "cloud computing" has emerged. Resources are made available to end-users as general utilities that can be rented and released on-demand over the Internet. More and more corporate processes are being moved to the cloud as firms experience using the technology. Cloud adoption is substantially more complicated than we first thought when it comes down to data management, system integration, and managing various cloud providers. As cloud migration has grown more common, a number of have been plagued by failed application modernization initiatives or migration. However, many firms worldwide are finding that cloud computing is more expensive and more difficult to implement than they expected. Is cloud computing still up to the task in this scenario? Failures are largely due to a lack of planning in the early stages of identifying which legacy applications are best suited for private or public cloud deployment, resulting in cost and time overruns. Application portfolios need deliberate decision-making to reduce the risk of overruns and delays in migration. Consequently, a rough estimate of cloud operating costs (ROM) is required for inclusion in the planning phase of IT governance. ROM cloud run costs must first be gathered through an automated discovery process, followed by quantitative analysis. Accordingly, to assist in making an informed decision about whether or not to migrate legacy applications, we propose two methods: (1) gathering initial data through Application Portfolio Profiling (APP), and (2) performing Application Portfolio Assessment (APA). An Enterprise Cloud Computing Considerations Framework (CCCC) framework is used to help a corporation decide which legacy applications should be moved as part of the adoption process for Cloud Computing. In light of the shift in business models, infrastructure requirements, and other issues that arise when cloud computing is implemented, this decision is crucial.

Application of Convolution Neural Network for Digital Image Processing

“…A short-term memory model (LSTM) presented by researchers integrates these feature vectors and vectors that embed the name in each sentence directly with the pre-trained VGGNet as a feature reader. Simply by adding the image vector to the first phase of LSTM, which they discovered to improve the results, it was developed in this manner Due to convolutional neural networks' powerful features, great progress has been made in the study of semantic segregation (CNNs) (Chimakurthi, 2019b). Semantic classification techniques based on the most up-to-date research use status information gleaned from the hidden image using CNN feature extraction.…”