The rapid growth of Cloud Computing and the expansion of large data centers have led to significant increases in energy consumption for hardware and cooling systems. This surge in power usage has raised operational costs, making energy efficiency a pressing issue for data center management. Virtual Machines (VMs) consolidation is a widely studied strategy to mitigate these costs by minimizing the number of active physical servers while maintaining user Service Level Agreements (SLAs). However, the success of consolidation largely depends on accurately forecasting VM resource needs. This paper presents the design and development of an energy-aware VMs allocation system that leverages predictive regression models to forecast future computational demands (CPU) of each VM. By anticipating these needs, the system optimally allocates VMs to available servers, effectively balancing energy savings with performance. The experimental evaluation, based on regression modeling exploited to forecast CPU and energy usage, shows a comparative analysis among several approaches and demonstrates the effectiveness of machine learning-driven allocations of VMs in optimizing resource utilization and reducing energy consumption.

The rapid growth of Cloud Computing and the expansion of large data centers have led to significant increases in energy consumption for hardware and cooling systems. This surge in power usage has raised operational costs, making energy efficiency a pressing issue for data center management. Virtual Machines (VMs) consolidation is a widely studied strategy to mitigate these costs by minimizing the number of active physical servers while maintaining user Service Level Agreements (SLAs). However, the success of consolidation largely depends on accurately forecasting VM resource needs. This paper presents the design and development of an energy-aware VMs allocation system that leverages predictive machine learning models to forecast future computational demands (CPU) of each VM. By anticipating these needs, the system optimally allocates VMs to available servers, effectively balancing energy savings with performance. A preliminary experimental evaluation, conducted on synthetic data, leverages predictive modeling within a server simulation to assess CPU and energy usage, demonstrating the effectiveness of energy-aware VMs allocation in optimizing resource utilization and reducing energy consumption.

This tutorial offers a practical introduction to the fascinating world of quantum computation and its application to optimization and machine learning problems. The participants will acquire hands-on experience in developing hybrid "Variational Quantum Algorithms", which combine classical and quantum computation, and in running them both on simulators and real quantum hardware provided by leading ICT companies. As a concrete use-case, of specific interest for the HPDC community, the tutorial will discuss the optimal assignment and scheduling of resources on the different nodes and layers of a Cloud/Edge architecture, a problem that is known to have NP-hard complexity.

In the field of Smart City applications, the analysis of urban data to detect city hotspots, i.e., regions where urban events (such as pollution peaks, virus infections, traffic spikes, and crimes) occur at a higher density than in the rest of the dataset, is becoming a common task. The detection of such hotspots can serve as a valuable organizational technique for framing detailed information about a metropolitan area, providing high-level spatial knowledge for planners, scientists, and policymakers. From the algorithmic viewpoint, classic density-based clustering algorithms are very effective in discovering hotspots characterized by homogeneous density; however, their application on multi-density data can produce inaccurate results. For such a reason, since metropolitan cities are characterized by areas with significantly variable densities, multi-density clustering approaches are more effective in discovering city hotspots. Moreover, the growing volumes of data collected in urban environments require the development of parallel approaches, in order to take advantage of scalable executions offered by Edge and Cloud environments. This paper describes the design and implementation of a parallel multi-density clustering algorithm aimed at analyzing high volumes of urban data in an efficient way. The experimental evaluation shows that the proposed parallel clustering approach takes out encouraging advantages in terms of execution time, speedup, and efficiency.

Modern Cloud/Edge architectures are composed of computing nodes belonging to multiple layers, including Cloud facilities, Edge/Fog nodes and sensors/actuators. In this paper, we present an architecture that includes also quantum computing devices, in two ways:

The management of thermal comfort in a building is a challenging and multifaced problem, because the use of objective parameters, for example, the energy consumption, should be combined with subjective requirements, related to human profile and preferences. This article exploits cognitive technologies, based on deep reinforcement learning (DRL), for the automatic control of the heating, ventilation, and air conditioning system in an office. The learning process is driven by a reward that includes multiple components, related to energy consumption, indoor temperature, and user perceptions, which are inferred by the human interactions with the system. This approach is inspired by the human-in-the-loop paradigm, which in our case helps the DRL controller to learn the requirements of users and readily adapt to them. Experimental results show that the appropriate balance of the reward components can be efficiently exploited to give the desired importance to the different objectives.

Detecting city hotspots in urban environments is a valuable organization methodology for framing detailed knowledge of a metropolitan area, providing high-level summaries for spatial urban datasets. Such knowledge is a valuable support for planner, scientist and policy-maker's decisions. Classic density-based clustering algorithms show to be suitable to discover hotspots characterized by homogeneous density, but their application on multi-density data can produce inaccurate results. For such a reason, since metropolitan cities are heavily characterized by variable densities, multi-density clustering approaches show higher effectiveness to discover city hotspots. Moreover, the growing volumes of data collected in urban environments require high-performance computing solutions, to guarantee efficient, scalable and elastic task executions. This paper describes the design and implementation of a parallel multi-density clustering algorithm, aimed at analyzing high volume of urban data in an efficient way. The experimental evaluation shows that the proposed parallel clustering approach takes out encouraging advantages in terms of execution time and speedup.

In this work, we propose an IoT edge-based energy management system devoted to minimizing the energy cost for the daily-use of in-home appliances. The proposed approach employs a load scheduling based on a load shifting technique, and it is designed to operate in an edge-computing environment naturally. The scheduling considers all together time-variable profiles for energy cost, energy production, and energy consumption for each shiftable appliance. Deadlines for load termination can also be expressed. In order to address these goals, the scheduling problem is formulated as a Markov decision process and then processed through a reinforcement learning technique. The approach is validated by the development of an agent-based real-world test case deployed in an edge context.

Leveraged by a large-scale diffusion of sensing networks and scanning devices in modern cities, huge volumes of geo-referenced urban data are collected every day. Such amount of information is analyzed to discover data-driven models, which can be exploited to tackle the major issues that cities face, including air pollution, virus diffusion, human mobility, traffic flows. In particular, the detection of city hotspots is becoming a valuable organization technique for framing detailed knowledge of a metropolitan area, providing high-level summaries for spatial datasets, which are valuable for planners, scientists, and policymakers. However, while classic density-based clustering algorithms show to be suitable to discover hotspots characterized by homogeneous density, their application on multi-density data can produce inaccurate results. For such a reason, since metropolitan cities are heavily characterized by variable densities, multi-density clustering seems to be more appropriate to discover city hotspots. This paper presents a study about how density-based clustering algorithms are suitable for discovering urban hotspots in a city, by showing a comparative analysis of single-density and multi-density clustering on both state-of-the-art data and real-world data. The experimental evaluation shows that, in an urban scenario, multi-density clustering achieves higher quality hotspots than a single-density approach.

This paper presents a novel approach for the management of buildings by leveraging cognitive technologies. The proposed approach exploits the Deep Reinforcement Learning paradigm to learn from both a physical and a simulated environment so as to optimize people comfort and energy consumption.

Smart Environments (SEs) are pervasive systems usually built on top of IoT-based sensing and actuation devices which are spread in an environment. The increase of the on-board computational capacity of the used devices opens to the possibility of naturally exploiting the edge computing paradigm in which the computation is pushed at the edge of the network. Anyway, despite the huge interest towards SEs, there is a lack of approaches for their design. This paper proposes an enhancement of the existing Smart Environment Metamodel (SEM) framework suited for designing SEs. The provided extension aims at taking into account issues related to edge computing, management of timing information and definition of the data types involved in data sources. The effectiveness of the whole proposal is assessed through a case study describing the development of a Smart Office.

The steadily increasing urbanization is causing significant economic and social transformations in urban areas and it will be posing several challenges in city management issues. In particular, given that the larger cities the higher crime rates, crime spiking is becoming one of the most important social problems in large urban areas. To handle with the increase in crimes, new technologies are enabling police departments to access growing volumes of crime-related data that can be analyzed to understand patterns and trends, finalized to an efficient deployment of police officers over the territory and more effective crime prevention. This paper presents an approach based on spatial analysis and auto-regressive models to automatically detect high-risk crime regions in urban areas and reliably forecast crime trends in each region. The final result of the algorithm is a spatio-temporal crime forecasting model, composed of a set of crime dense regions and a set of associated crime predictors, each one representing a predictive model for forecasting the number of crimes that will happen in its specific region. The experimental evaluation, performed on real-world data collected in a big area of Chicago, shows that the proposed approach achieves good accuracy in spatial and temporal crime forecasting over rolling time horizons.

Large-scale smart environments (LSEs) are open and dynamic systems typically extending over a wide area and including a huge number of interacting devices with a heterogeneous nature. Thus, during their deployment scalability and interoperability are key requirements to be definitely taken into account. To these, discovery and reputation assessment of services and objects have to be added, given that new devices and functionalities continuously join LSEs. In spite of the increasing interest in this topic, effective approaches to develop LSEs are still missing. This paper proposes an agent-based approach that leverages edge computing and Social Internet of Things paradigms in order to address the above mentioned issues. The effectiveness of such an approach is assessed through a sample case study involving a commercial road environment.

A smart environment is a physical environment enriched with sensing, actuation, communication and computation capabilities aiming at acquiring and exploiting knowledge about the environment so as to adapt it to inhabitants' preferences and requirements. In this domain, there is the need of tools supporting the design and analysis of applications. In this paper, a meta-model framework for smart environments is proposed. This framework allows to model applications by exploiting concepts closer to the smart environment domain. The proposed meta-model framework approaches the modelling from two different points of view, namely the functional and data perspectives. The functional perspective focuses on the services provided by the environment whereas the data perspective is used to characterize data sources of the environment. The effectiveness of the proposal is shown by applying it to the modelling of a smart environment scenario well known in literature.

Smart Cyber-Physical Environments are augmented physical environments whose behaviours are enhanced through the use of ICT technologies. The goal is to offer new services and functionalities devoted to meet people's needs and preferences, and to better exploit existing services and infrastructures. The use of IoT technologies, paired with the edge computing, fosters the development of Smart Environment applications having the important features of reliability, scalability and extensibility. This paper proposes an approach for the design and the implementation of Smart Cyber Physical Environment applications having the aforementioned features. The approach relies on the use of isapiens which is an IoT platform enabling edge computing through the exploitation of the agent metaphor. Such platform provides effective abstractions which are able to hide heterogeneity of both the adopted hardware devices and communication protocols. The approach is validated through a case study involving the realization of a Smart Office prototype for profiling and monitoring daily working activities and performing actuations in the environment on the basis of the obtained information.

Recent advancements in embedded systems, computer science and telecommunication fields open up to new application opportunities where many physical entities are disseminated across the world and connected through the Internet. This new scenario, often referred as Internet of Things (IoT), gives raise to different issues and challenges to be coped with. Indeed, each kind of physical device comes with different technology details, low level communication protocols and can exhibit different semantic behaviors. In addition, the interaction between human beings and the physical objects must be properly managed. Two main approaches exist to cope with the mentioned issues: (i) the development of new ad-hoc technologies and solutions to deal with the specific issues raising from the new scenario, or (ii) the exploitation of well-known technologies and solutions also in the new context so as to facilitate the integration of the physical stuff with the preexisting internet services. The latter approach is usually referred as Web of Things (WoT). This paper proposes a possible implementation of the WoT vision. The resulting architecture allows creating complex applications where physical resources, internet resources and human resources can properly interact with each other. The objects of the physical world are virtualized and managed through the Smart Object (SO) concept. The SOs are enclosed in a Smart Gateway which exposes them to the world through an uniform web API based on REST paradigm. The human beings interaction is achieved using the popular micro-blogging platform Twitter. The integration between all the entities is provided by adopting a WS-BPEL workflow technology. To validate the approach an example of a smart room environment controlled through Twitter in a crowd source fashion is detailed.