Legacy Digital Transformation: TCO and ROI Analysis

Legacy Digital Transformation is modernizing or migrating systems from non-digital or older digital technology to newer digital technologies. Digitalization is essential for information reading, processing, transforming, and storing. Social media, Cloud, and analytics are the major technologies in today\u27s digital world. Digitalization (business process) and Digital Transformation (the effect) are the core elements of newer global policies and processes. Recent COVID pandemic situation, Organizations are willing to digitalize their environment without losing business. Digital technologies help to improve their capabilities to transform processes that intern promote new business models. Applications cannot remain static and should modernize to meet the evolving business and technology needs. Business needs time to market, Agility, and reduce technical debt. Technology needs consist of APIs, better Security, Portability, Scalability, Cloud support, Deployment, Automation, and Integration. This paper elaborates different transformation/modernization approaches for Legacy systems written in very long or End of Life (EOL) systems to newer digital technologies to serve the business needs. EOL impacts application production, supportability, compliance, and security. Organizations spend money and resources on Digital Transformation for considering Investment versus Return on Investment, Agility of the System, and improved business processes. Migration and Modernization are critical for any Legacy Digital Transformation. Management takes decisions to proceed with Digital Transformation for considering Total Cost Ownership (TCO) and Return on Investment (ROI) of the program. The paper also includes a TCO-ROI calculator for Transformation from Legacy / Monolithic to new architectures like Microservices

Facing the Giant: a Grounded Theory Study of Decision-Making in Microservices Migrations

Background: Microservices migrations are challenging and expensive projects with many decisions that need to be made in a multitude of dimensions. Existing research tends to focus on technical issues and decisions (e.g., how to split services). Equally important organizational or business issues and their relations with technical aspects often remain out of scope or on a high level of abstraction. Aims: In this study, we aim to holistically chart the decision-making that happens on all dimensions of a migration project towards microservices (including, but not limited to, the technical dimension). Method: We investigate 16 different migration cases in a grounded theory interview study, with 19 participants that recently migrated towards microservices. This study strongly focuses on the human aspects of a migration, through stakeholders and their decisions. Results: We identify 3 decision-making processes consisting of 22decision-points and their alternative options. The decision-points are related to creating stakeholder engagement and assessing feasibility, technical implementation, and organizational restructuring. Conclusions: Our study provides an initial theory of decision-making in migrations to microservices. It also outfits practitioners with a roadmap of which decisions they should be prepared to make and at which point in the migration.Comment: 11 pages, 7 figure

Data Management in Microservices: State of the Practice, Challenges, and Research Directions

We are recently witnessing an increased adoption of microservice architectures by the industry for achieving scalability by functional decomposition, fault-tolerance by deployment of small and independent services, and polyglot persistence by the adoption of different database technologies specific to the needs of each service. Despite the accelerating industrial adoption and the extensive research on microservices, there is a lack of thorough investigation on the state of the practice and the major challenges faced by practitioners with regard to data management. To bridge this gap, this paper presents a detailed investigation of data management in microservices. Our exploratory study is based on the following methodology: we conducted a systematic literature review of articles reporting the adoption of microservices in industry, where more than 300 articles were filtered down to 11 representative studies; we analyzed a set of 9 popular open-source microservice-based applications, selected out of more than 20 open-source projects; furthermore, to strengthen our evidence, we conducted an online survey that we then used to cross-validate the findings of the previous steps with the perceptions and experiences of over 120 practitioners and researchers. Through this process, we were able to categorize the state of practice and reveal several principled challenges that cannot be solved by software engineering practices, but rather need system-level support to alleviate the burden of practitioners. Based on the observations we also identified a series of research directions to achieve this goal. Fundamentally, novel database systems and data management tools that support isolation for microservices, which include fault isolation, performance isolation, data ownership, and independent schema evolution across microservices must be built to address the needs of this growing architectural style

Information Collection Platform for Smart Nudging. A Microservice-Based Approach.

This thesis aims to explore the problem of integrating heterogeneous data sources into the Smart Nudge system. The Smart Nudge system is a system that produces personalised nudges that are contextually relevant to each user. The system relies on access to live data that could be constructed and presented in specific ways to influence users behaviour towards an agreed-upon goal. The goal is to ascertain the suitability of a microservice-based approach to designing the component that is responsible for integrating various data sources. A small prototype of two microservices provided a practical look at integrating real-world sources, namely a Norwegian weather service and a bus tracking service in Chicago. The proposed architecture is analysed using a set of requirements derived from a theoretical examination of the Smart Nudge system and a general theoretical look at decomposition techniques used to evaluate microservice architectures. Evaluating the prototype revealed that the Smart Nudge system is highly dependant on augmenting data sources with additional meta-data to produce personalised nudges. The analysis indicates that a data-driven microservice-based architecture seems well suited to resolving some of the problems and requirements that are somewhat unique to the Smart Nudge system setting