## 1 Introduction  
In the last few years, the research community has observed a reshaping of network infrastructures, with a growing interest and adoption of the Cloud Continuum (CC) paradigm. The CC brings together an assortment of computational and storage resources, spreading them across different layers, resulting in the establishment of a unified ecosystem [1]. In this way, users would rely not only on the resources available at the Edge, but could also exploit computing resources provided by Fog or Cloud providers. Consequentially, the CC opens new possibilities for distributing the load of computationally intensive services such as Machine Learning applications, online gaming, Big Data management, and so on [2, 3]. It is important to note that all these new innovative options and the inclusion of a plethora of new devices in the ecosystem create a large number of potential threads, which require the adoption of new effective strategies and countermeasures [4]. In such a distributed and heterogeneous environment like the one that the CC brings to the table, effective service management becomes crucial to ensure seamless service delivery and resource optimization. However, efficiently coordinating resource allocation, exploitation, and management in the CC represents quite a challenge [5]. There is a need for service fabric management solutions capable of efficiently allocating multiple service components using a limited pool of devices distributed throughout the CC, also considering the peculiar characteristics of the resources available at each layer. In addition, the significant dynamicity of the CC scenario calls for adaptive/intelligent orchestrators that can autonomously learn the best allocation for services. The increasing use of Artificial Intelligence (AI) techniques has led to different and potentially very promising approaches [6], including self-learning ones. Among those, Reinforcement Learning (RL) is the one that for sure has accumulated most of the attention in the research community [7]. RL is a promising Machine Learning area that has gained popularity in a wide range of research fields such as Network Slicing [8], Network Function Virtualization [9], and resource allocation [10]. More specifically, Deep Reinforcement Learning (DRL) has recently emerged as a compelling technique increasingly proposed in service management research [11]. DRL approaches aim to extensively train an intelligent orchestrator to make it capable of effective service management decision-making in various conditions. Computational Intelligence (CI) solutions represent another promising approach, leveraging smart and gradient-free optimization techniques that can explore a relatively large solution space efficiently [12]. Leveraging CI, it is possible to realize orchestrators that can explore relatively quickly even large solution spaces, thus being able to effectively operate in dynamic conditions with a reactive posture with a minimal re-evaluation lag. Advanced Computational Intelligence solutions seem to be well suited for expensive [13] and dynamic optimization problems [14; 15]. Although some metaheuristic performance analyses in Fog environments have been conducted in recent times [16], establishing which of these solutions represents the most-suitable one for service management in the Cloud Continuum is still an open research question. In this paper, we aimed to investigate that question and, towards that goal, compared two DRL techniques, namely the Deep Q-Network (DQN) and Proximal Policy Optimization (PPO), with five Computational Intelligence techniques, namely Genetic Algorithms (GAS), Particle Swarm Optimization (PSO), Quantum-inspired Particle Swarm Optimization (QPSO), Multi-Swarm PSO (MPSO), and the Grey-Wolf Optimizer (GWO), to evaluate their performance for service management purposes. First, we conducted simulations to train and test these techniques in a realistic Cloud Continuum scenario characterized by limited computing resources at the Edge and Fog layers and unlimited resources at the Cloud.