Microservice Transition and its Granularity Problem: A Systematic Mapping Study

Microservices have gained wide recognition and acceptance in software industries as an emerging architectural style for autonomic, scalable, and more reliable computing. The transition to microservices has been highly motivated by the need for better alignment of technical design decisions with improving value potentials of architectures. Despite microservices' popularity, research still lacks disciplined understanding of transition and consensus on the principles and activities underlying "micro-ing" architectures. In this paper, we report on a systematic mapping study that consolidates various views, approaches and activities that commonly assist in the transition to microservices. The study aims to provide a better understanding of the transition; it also contributes a working definition of the transition and technical activities underlying it. We term the transition and technical activities leading to microservice architectures as microservitization. We then shed light on a fundamental problem of microservitization: microservice granularity and reasoning about its adaptation as first-class entities. This study reviews state-of-the-art and -practice related to reasoning about microservice granularity; it reviews modelling approaches, aspects considered, guidelines and processes used to reason about microservice granularity. This study identifies opportunities for future research and development related to reasoning about microservice granularity.Comment: 36 pages including references, 6 figures, and 3 table

Models of everywhere revisited: a technological perspective

The concept ‘models of everywhere’ was first introduced in the mid 2000s as a means of reasoning about the environmental science of a place, changing the nature of the underlying modelling process, from one in which general model structures are used to one in which modelling becomes a learning process about specific places, in particular capturing the idiosyncrasies of that place. At one level, this is a straightforward concept, but at another it is a rich multi-dimensional conceptual framework involving the following key dimensions: models of everywhere, models of everything and models at all times, being constantly re-evaluated against the most current evidence. This is a compelling approach with the potential to deal with epistemic uncertainties and nonlinearities. However, the approach has, as yet, not been fully utilised or explored. This paper examines the concept of models of everywhere in the light of recent advances in technology. The paper argues that, when first proposed, technology was a limiting factor but now, with advances in areas such as Internet of Things, cloud computing and data analytics, many of the barriers have been alleviated. Consequently, it is timely to look again at the concept of models of everywhere in practical conditions as part of a trans-disciplinary effort to tackle the remaining research questions. The paper concludes by identifying the key elements of a research agenda that should underpin such experimentation and deployment

Rawlsian Individuals: Justice, Experiments, and Complexity

John Rawls’s A Theory of Justice is examined from the perspective of experimental methods in economics and complex adaptive systems simulations. This paper first discusses the justice principle selection process in Rawls’s representation of it as a hypothetical experiment. This hypothetical experiment fails to satisfy reasonable experimental controls, particularly as reflects the conception of the individual it employs. The second section of the paper discusses the differences between Rawls’s two conceptions of rational persons associated with his distinction between thin and full theories of the good. The third section uses his fuller conception of rational persons, life plans, and psychological laws in the third part of the book to offer an alternative view of the selection process understood as a complex adaptive system. The fourth section turns to a topic raised by this complex system approach, the status of normative reasoning in political-economic systems. The fifth section summarizes

Collectivized Intellectualism

We argue that the evolutionary function of reasoning is to allow us to secure more accurate beliefs and more effective intentions through collective deliberation. This sets our view apart both from traditional intellectualist accounts, which take the evolutionary function to be individual deliberation, and from interactionist accounts such as the one proposed by Mercier and Sperber, which agrees that the function of reasoning is collective but holds that it aims to disseminate, rather than come up with, accurate beliefs. We argue that our collectivized intellectualism offers the best explanation of the range of biases that human reasoning is prone to, and that it does better than interactionism at offering a function of reasoning that would have been adaptive for our distant ancestors who first evolved this capacity

Complexity, strategic thinking and organisational change

Comparative considerations of strategy from complexity paradigm and Newtonian paradigm perspectives are discussed in the light of three ideological dispositions towards the future. We term them defensive, opportunist, and goal oriented. Over the years, the strategy literature has identified a number of strategic archetypes (e.g. Miller and Freisen, 1978). What is interesting from our point of view is the patterns of reasoning that underpin them. The study of ideology has identified qualitative patterns of reasoning which underpin different types of strategic decision in both the fields of politics and strategic management. This paper considers three patterns of reasoning and considers how they relate to the complexity and Newtonian paradigms