Editorial: Special issue on modelling and simulation in the cloud computing era

Modelling and Simulation (M&S) represents one of the fundamental methods to design and study complex systems in many industrial and scientific domains such as transport, energy, and aerospace. M&S techniques enable the analysis and evaluation of many design alternatives while avoiding risks, costs, and failures that come with experimentations on the real system; this opportunity becomes crucial, when real-world tests are too costly to conduct in terms of safety, time, and other resources (Fujimoto et al., 2017).

Cloud Computing has captured the interest of the scientific and industrial communities because of the benefits provided by its service models, i.e., Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). These services allow developers to rapidly implement solutions that exploit computing and data storage capacity, network resources, and scalability, without having to deal with common issues related to the configuration of the Cloud infrastructure that is automatically managed by a specific service. Cloud Computing can be defined as a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., servers, storage, and services) that can be rapidly provisioned and released with minimal effort in management (Mell & Grance, 2011). In this context, Cloud, Fog, and Edge computing allow organisations to exploit data processing and storage resources efficiently through the definition of a hierarchical data processing architecture. Fog and Edge computing are both extensions of the Cloud network, where at the lowest level there is the Edge, followed by the Fog level, and finally the Cloud level. The Edge layer allows reducing network traffic as data processing occurs locally on the devices. The Fog layer of the Cloud network architecture pushes intelligence down to the LAN layer, where data are processed in a gateway; thus, the Fog nodes are placed near devices with which it is communicating. Cloud, Fog, and Edge computing can offer suitable services to share and collaborate on M&S projects and perform complex simulation experiments faster and more efficiently through the Modelling and Simulation as a Service (MSaaS) model. While MSaaS provides an ever-increasing number of opportunities, it also poses a significant number of pitfalls. One of the major pitfalls is that Cloud infrastructures are massive at all scales; as a consequence, the definition of MSaaS solutions is difficult without a deep knowledge of the involved infrastructures and technologies (Cayirci, 2013).

The focus of this special issue is to provide current research results in M&S for Cloud Computing and vice versa. Specifically, the special issue aims at (i) presenting the current state-of-the-art about M&S solutions based on open standards, recent extensions, and innovations related to Cloud Computing technologies; (ii) identifying research directions and technologies that will drive innovations in M&S based on Cloud Computing Infrastructures, and (iii) adopts M&S techniques to formalise and study Cloud Computing environments and services.

The application of Cloud-based M&S has increased since 2010 (Mansouri et al., 2020) however, studies, particularly on methodological and technological aspects in the convergence of Cloud Computing and M&S disciplines, continue to be few (Hannay et al., 2021; Tolk, 2020; Zhou et al., 2022). To fill this gap in the literature, we invited researchers and practitioners to submit contributions on conceptual, methodological, and technical advances in High-performance simulation in the Cloud, System Dependability, and Performance Analysis through Big data in the Cloud, Parallel and Distributed simulation through the Cloud services.

The resulting contributions deal with a wide range of topics ranging from methodological aspects and development frameworks in adopting M&S in the Cloud Computing environment and vice versa, i.e., how Cloud Computing can support the definition of new M&S methods, models, and techniques. This special issue contains four papers, each of which is briefly described in the following.

The paper titled “Mobile Experimentation using Modeling and Simulation in the Fog/Cloud” (by Khaldoon Al-Zoubi and Gabriel Wainer) proposes a method and some algorithms to define Fog nodes as private services in which different middleware run on different Virtual Machines to expose services (Al-Zoubi & Wainer, 2021). The focus of the research is on the mobility of clients that perform experiments through mobile devices. The proposed method and algorithms take into consideration the position of clients to identify available services that are nearby the Fog zone. To increase mobility assistance, the paper delineates the concept of “mobile simulation experiment”, in which experiments move with the device as it moves away from a specified Fog zone.

The paper titled “Cloud DEVS-based Computation of UAVs Trajectories for Search and Rescue Missions” (by Juan Bordón-Ruiz, Eva Besada-Portas, and José López-Orozco) presents a Cloud framework, based on the Discrete Event System Specification (DEVS) formalism, that allows optimising target search strategies through Unmanned Aerial Vehicle (UAV; Bordón-Ruiz et al., 2022). The DEVS support provided by the framework allows to build different types of strategies for UAVs. Furthermore, the Cloud deployability allows to speed up simulations, also in presence of computationally heavy UAV models. The framework is implemented in xDEVS and automatically deploys simulation models over Docker containers in the Google Cloud infrastructure by using Kubernetes as an orchestration system.

In the paper titled “Efficient micro data centers deployment for mobile healthcare monitoring systems in IoT urban scenarios” (by Kevin Henares Vilaboa, José Luis Risco Martín, José Luis Ayala Rodrigo, and Román Hermida Correa), the authors analyse how M&S can help researchers in designing, analysing, and implementing Internet-of-Things (IoT) infrastructure by providing flexible and powerful mechanisms to study and compare different strategies (Henares et al., 2022). In this paper, Micro Data Centers (MDCs) are identified as a viable solution for reducing overwhelmed Cloud Data Center infrastructures. The paper proposes an M&S methodology to study the overall power consumption of a healthcare IoT scenario, where patients wear non-intrusive monitoring devices that periodically generate tasks executed in MDCs. The authors extract the layout of existing urban infrastructures, simulate the monitored population’s behaviour, and compare the power consumption of several data centre configurations.

The paper titled “Integration Process Simulator: a tool for performance evaluation of task scheduling of integration processes” (by Daniela Lopes Freire, Rafael Frantz, Fabricia Roos-Frantz, and Vitor Basto Fernandes) presents a simulation tool, called “Integration Process Simulator (IPS)”, that adopts scheduling heuristics to allocate tasks efficiently and effectively (Freire et al., 2022). IPS allows calculating and retrieving a set of metrics (e.g., throughput, number of processed messages, and number of unprocessed messages) useful to evaluate the performances of heuristics. Three heuristics of task scheduling on the execution of integration processes have experimented and results evaluated with statistical tests.

The aim of the special issue on “Modeling and Simulation in the Cloud Computing era” was to gather a critical mass of research to establish a route for the integrated use of novel approaches, techniques, and tools in the definition of future-generation simulators based on Cloud Computing infrastructures and services. We succeeded in engaging high-quality articles focused on Modelling and Simulation in the Cloud. The authors, peer reviewers, and members of the JoS editorial board were precious in the realisation of this special issue. We appreciate their efforts, and thank them all!

Disclosure statement

No potential conflict of interest was reported by the author(s).