On Distributed Computing Continuum Systems

Abstract

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.

Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration

“…on current needs [37,38]. Accordingly, in this article, we collectively refer to edge and fog computing, MEC, and other similar distributed computing approaches with heterogeneous and opportunistic resources by the term computing continuum.…”

“…Dustdar et al [38] use Resources, Cost, and Quality as the three fundamental objective categories, which we adopt here. Further, the objectives of orchestration are often related to the essential resources (i.e., spectrum, time, energy, or funds) and can often be described as a combination of corresponding budgets or as a relation between the budgets and resource usage [23].…”

“…Computing continuum invalidates the idea of having singletier, centralized orchestration, such as exclusively employed on the cloud. On the contrary, the challenges in computing continuum, discussed in Section VI, push towards distributing orchestration along the tiers, addressing management from a holistic perspective [38].…”

Deep Learning for Resource Management in Internet of Things Networks: A Bibliometric Analysis and Comprehensive Review

“…A better data representation is important to remove noise and complexity from the dataset. This helps to obtain a data representation with a reduced size [98].…”

“…A better data representation also helps DL to efficiently learn the important information from the input data without memorizing noise, thus, speeding up the training and running of DL model. Also, a better data representation helps to build a reliable model [98]. The DL model building and training phase is used to train a DL algorithm on the training dataset while the model validation phase is used to validate the trained model using the test dataset.…”