## 2 Related Work  
Services and resource management in the Cloud Continuum comprise a challenging research topic that calls for innovative solutions capable of managing the multiple layers of computing resources. The work presented in [17] proposes a resource orchestration framework called ROMA to manage micro-service-based applications in a multi-tier computing and network environment that can save network and computing resources when compared to static deployment approaches. In [18], [PERSON] et al. propose a hierarchical and analytical model to overwhelm the resource availability problem in Cloud Continuum scenarios. They present multiple use cases to demonstrate how their model can improve the availability and scalability in Edge-Cloud environments. Moreover, the authors in [19] describe a model-based approach to automatically assign multiple software deployment plans to hundreds of Edge gateways and connected Internet of Things (IoT) devices in a continuously changing cyber-physical context. In recent years, the advent of the Cloud Continuum paradigm has called for new resource management proposals and methodologies to evaluate them. Currently, simulation is one of the most-adopted evaluation techniques in the literature for evaluating the performance of different techniques in Edge-Fog-Cloud scenarios. The authors in [20] propose a simulation approach at different scales to evaluate their Quantum-inspired solution to optimize task allocation in an Edge-Fog scenario. Specifically, they use the iFogSim simulation toolbox [21] for their experiments and make a comparison between their concept and state-of-the-art strategies, showing improvement in prediction efficiency and error reduction. In [22] [PERSON] et al. present an integrated system called Integrated Fuzzy-based System for Coordination and Management of Resources (IFS-CMR), evaluating it by simulation. Thanks to its three subsystems, it integrates Cloud-Fog-Edge computing in Software-Defined Vehicular Ad hoc Networks with flexible and efficient management of the abundance of resources available. The authors in [23] suggest STEP-ONE, a set of simulation tools to manage IoT systems. Specifically, it relies on the Business Process Model and Notation standards to handle IoT applications, defined as a plethora of processes executed between different resources. After an in-depth analysis of the most-relevant simulators in the Edge-Fog scenario and an accurate description of each component of STEP-ONE, they define a hypothetical smart city scenario to evaluate its capabilities at choosing the right process placement strategy. In [24], [PERSON] et al. present Fog-Resource-aware Adaptive Task Offloading (FRATO), a framework to select the best offloading policy adaptively and collaboratively based on the system circumstances, represented by the number of resources available. In their experiments, they conduct an expanded simulative analysis adopting FRATO to compare several offloading strategies and determine their performance by measuring the service provisioning delay in different scenarios. Computational Intelligence and RL approaches have been widely adopted to solve service and network management problems. In [25], the authors propose ETA-GA, a Genetic-Algorithm-based Efficient Task Allocation technique, which aims at efficiently allocating computing tasks--according to their data size--among a pool of virtual machines in accordance with the Cloud environment. Furthermore, in [26], [PERSON] et al. present a model of a Fog-Cloud environment and apply different metaheuristics to optimize several constraints, such as power consumption and service latency. Through simulation, they legitimize their modeling and prove that approaches based on GAs and PSO are more effective than the traditional ones in finding the best solution to their constraints. The authors in [27] use PSO to solve a joint resource allocation and a computation offloading decision strategy that minimizes the total computing overhead, completion time, and energy consumption. In [28], [PERSON] et al.