## 1 Introduction  
In the ever-evolving technological landscape, marked by the omnipresence of the Internet and the unstoppable digitalization of processes and services, the consolidation of next-generation technology drivers, such as Artificial Intelligence (AI), the Internet of Things (IoT), and cloud computing, have opened up new possibilities to address current and future challenges across the fabric of our daily lives. However, this progress has also brought to the forefront the challenge of effectively managing the massive data generated by the resulting new systems. The current volume of data generated in the Internet is truly astonishing: in 2020 alone, the total amount of data created, captured, copied, and consumed globally was 64 trillion gigabytes [1], and that value is accelerating due to the growing adoption of IoT applications and services in various domains with a notable emphasis on home and industrial automation, healthcare, agriculture, and transportation. In fact, by the end of 2022, the number of worldwide active IoT devices reached 13.2 billion, which is a figure expected to increase to 34.4 billion by 2032 [2]. Most existing IoT architectures exhibit high centralization, heavily relying on the transfer of data processing, analytics, and decision-making processes to cloud solutions. While conceptually straightforward, cloud computing can lead to inefficiencies related to latency, network traffic management, computational processing, and power consumption [3]. Additionally, centralized IoT architectures struggle to meet the stringent requirements of cutting-edge time-sensitive applications whose performance and timeliness are compromised by the delays introduced when transferring data to the cloud and then back to on-site actuators [4]. Conversely, edge computing promotes the processing of data close to where it is generated in the physical world. By means of several techniques such as data fusion, trend analysis, and/or partial decision making, it is possible to minimize the amount of data being sent to the cloud. As a result, edge computing effectively tackles challenges related to network traffic, bandwidth (BW) consumption, latency, and overall energy usage. Moreover, edge distributed infrastructure contributes to enhanced resilience in the event of a significant incident (such as a large-scale cyberattack) by ensuring local resources and services remain accessible offline. Additionally, the fog computing concept was first introduced by Cisco Systems in 2012 [5], extending the edge computing domain by introducing an additional layer of distributed computing resources between the edge and the cloud. The wide ecosystem where IoT devices and the presented architectures converge to form a computing continuum is known as the IoT-edge-cloud continuum [6]. This approach extends the high-performance cloud data centers with energy-efficient and low-latency devices close to the data sources [7]. Typical workloads from AI-based data analytics are perfect candidates for this continuum, since raw data are generated by IoT devices, whereas data-intensive analysis is performed on centralized locations in the cloud [8]. For their part, edge servers operate as an extension of the centralized cloud in the context of an overall application that is managed in the cloud but executed at the edge. One of the main challenges of the IoT-edge-cloud continuum is the effective orchestration of distributed resources in a heterogeneous ecosystem; that is, the flexible and dynamic provisioning and management of computing, storage, and networking resources while ensuring the security and sovereignty of data. In fact, IoT applications can greatly benefit from smart orchestration to reduce energy consumption, overall latency, and message overload as well as achieve more efficient data storage and data transmission rate in large-scale scenarios. However, it involves a complex interplay of computational resources, learning models, algorithms, networking capabilities, and edge/IoT device mobility patterns [9].