This article presents our vision on the need of developing new managing technologies to harness distributed “computing continuum” systems. These systems are concurrently executed in multiple computing tiers: Cloud, Fog, Edge and IoT. This simple idea develops manifold challenges due to the inherent complexity inherited from the underlying infrastructures of these systems. This makes inappropriate the use of current methodologies for managing Internet distributed systems, which are based on the early systems that were based on client/server architectures and were completely specified by the application software. We present a new methodology to manage distributed “computing continuum” systems. This is based on a mathematical artifact called Markov Blanket, which sets these systems in a Markovian space, more suitable to cope with their complex characteristics. Furthermore, we develop the concept of equilibrium for these systems, providing a more flexible management framework compared with the one based on thresholds, currently in use for Internet-based distributed systems. Finally, we also link the equilibrium with the development of adaptive mechanisms. However, we are aware that developing the entire methodology requires a big effort and the use of learning techniques, therefore, we finish this article with an overview of the techniques required to develop this methodology.

Edge intelligence and, by extension, any distributed computing continuum system will bring to our future society a plethora of new and useful applications, which will certainly revolutionize our way of living. Nevertheless, managing these systems challenges all previously developed technologies for Internet-distributed systems. In this regard, this article presents a set of techniques and concepts that can help manage these systems; these are framed in the main paradigm for autonomic computing, the well-known monitor–analyze–plan–execute over shared knowledge, or MAPE-K. All in all, this article aims at unveiling research opportunities for these new systems, encouraging the community to work together toward new technologies to make edge intelligence a reality.

Computing paradigms have evolved significantly in recent decades, moving from large room-sized resources (processors and memory) to incredibly small computing nodes. Recently, the power of computing has attracted almost all current application fields. Currently, distributed computing continuum systems (DCCSs) are unleashing the era of a computing paradigm that unifies various computing resources, including cloud, fog/edge computing, the Internet of Things (IoT), and mobile devices into a seamless and integrated continuum. Its seamless infrastructure efficiently manages diverse processing loads and ensures a consistent user experience. Furthermore, it provides a holistic solution to meet modern computing needs. In this context, this paper presents a deeper understanding of DCCSs’ potential in today’s computing environment. First, we discuss the evolution of computing paradigms up to DCCS. The general architectures, components, and various computing devices are discussed, and the benefits and limitations of each computing paradigm are analyzed. After that, our discussion continues into various computing devices that constitute part of DCCS to achieve computational goals in current and futuristic applications. In addition, we delve into the key features and benefits of DCCS from the perspective of current computing needs. Furthermore, we provide a comprehensive overview of emerging applications (with a case study analysis) that desperately need DCCS architectures to perform their tasks. Finally, we describe the open challenges and possible developments that need to be made to DCCS to unleash its widespread potential for the majority of applications.

This article discusses four fundamental topics for future Distributed Computing Continuum Systems: their representation, model, lifelong learning, and business model. Further, it presents techniques and concepts that can be useful to define these four topics specifically for Distributed Computing Continuum Systems. Finally, this article presents a broad view of the synergies among the presented technique that can enable the development of future Distributed Computing Continuum Systems.

This article presents a simple solution to estimate the location of products in a retail store, using an autonomous ground robot with a radio frequency identification payload. The model used and explained in this article is designed to be as simple and versatile as possible, while achieving accurate location estimations when compared with other proposed models in the state of the art. In addition, the solution developed meets the business requirements of the retail industry, such as locating at stock keeping unit level, as opposed to item level, or expressing the location in terms of store fixtures (e.g., shelf and rack) as opposed to $(x,y)$ coordinates. The research results are obtained from experiments of the model in different environments, achieving accurate location estimations in a controlled laboratory environment. Moreover, for the first time, the model has been tested in a large retail store, where the results obtained met the requirements of the store owners.

As distributed continuum systems (DCSs) are envisioned, they will have a massive impact on our future society. Hence, it is of utmost importance to ensure that their impact is socially responsible. Equipping these systems with causal models brings features such as explainability, accountability, and auditability, which are needed to provide the right level of trust. Furthermore, by combining causality with graph-based service-level objectives, we can cope with dynamic and complex system requirements while achieving sustainable development of DCSs’ capacities and applications.