Quantum Process Units (QPUs) are becoming more widely accessible to the public. Nonetheless, they still are very susceptible to noise and feature only a small amount of qubits, making it possible to only execute short quantum computations. Facing this problem, several approaches were proposed to make the most of the present situation, either by distributing the Quantum load, sending different Quantum programs to different QPUs or by distributing Quantum program fragments, by cutting a Quantum program into multiple smaller chunks. Here, we propose a change of perspective. Due to the probabilistic nature of Quantum Mechanics, it is usually required to iterate the execution of a Quantum program numerous times or shots. We suggest considering the shots dimension while determining how to distribute quantum computations. In this paper, we design and develop a methodology to distribute the shots of a Quantum program among many QPUs. Exploiting multiple QPUs improves the resilience to potential QPUs failures. Our solution also enables users to directly encode, through a proposed DSL, their own distribution strategies according to their needs and considered scenarios, offering an expressive and customisable approach. Finally, we showcase a prototype implementation and discuss a life-like use case that can only be addressed by relying on our approach.

Quantum Computing is continuously evolving and expanding. As time goes by, more and more Quantum Computer implementations become available, each of them with their own features. In such a scenario, it can be difficult for developers to identify which Quantum Computer is the most suitable for their needs. In this paper, different from current works presenting strategies to select only one Quantum Computer, we propose a change of perspective. Indeed, due to the probabilistic nature of Quantum Mechanics, performing a computation in a Quantum Computer usually requires iterating the same execution many times (called shots), to eventually end with a distribution of the final results. Leveraging this need, our architecture enables selecting many Quantum Computers for the same circuit and spreading the shots among them. Such a mechanism offers also the possibility for developers to access the partial distributions obtained from the output of a subset of the selected computers. Finally, our architecture proposes to decouple the decision process from the actual execution of such a decision, by enabling developers to encode their specific custom policies.

The sustainable management of smart environments enabled by the Internet of Things (IoT) requires new methodologies and tools to suitably handle potentially many users and their objectives on cyber-physical systems, e.g. smart lighting, smart A/C. In this article, we propose a declarative framework to model IoT-enabled smart environments. Our methodology permits (i) expressing user roles and hierarchical environments, (ii) declaring customized policies to mediate user objectives into a target state and (iii) determining valid settings for IoT actuators to achieve such a target also reducing energy consumption. An open-source Prolog prototype of the framework is showcased over two lifelike motivating examples and its scalability is assessed at increasing sizes of the managed smart environment.

Monitoring the proper functioning and performance of an infrastructure spanning multiple Cloud datacentres is challenging. It requires continuously aggregating monitored data across multiple source machines and processing them so to obtain useful alerts and insights. In this article, we propose a simple open-source prototype tool to perform highly customisable fault and performance monitoring across multiple Clouds. Differently from commercial tools, our prototype is simpler to deploy and it can be configured through a declarative approach, by simply specifying data monitoring tasks and aggregation policies. We illustrate such peculiarities over a use case relying on three datacentres under the Italian Research and Education Network Consortium.

Developing and releasing multiservice applications rely upon a pipeline of automation tools known as Continuous Integration/Continuous Deployment. Among those tools, continuous reasoning is exploited by large companies to perform incremental static analyses on their code commits as soon as they are integrated into a shared codebase. In this article, we extend continuous reasoning towards the continuous QoS- and context-aware management of multiservice applications in Cloud-IoT scenarios. We propose a novel continuous reasoning methodology that supports runtime decision on service placement by reacting both to changes in the infrastructure and in the application requirements, and capable of suggesting migrations only for services affected by such changes. The methodology is prototyped in Prolog and assessed through simulations over a realistic use case and over a lifelike motivating scenario at increasing infrastructure sizes. Experimental results show that our approach brings considerable speed-up in comparison with an exhaustive search employing non-incremental reasoning.

Smart environments powered by the Internet of Things aim at improving our daily lives by automatically tuning ambient parameters (e.g. temperature, interior light) and by achieving energy savings through self-managing cyber-physical systems. Commercial solutions, however, only permit setting simple target goals on those parameters and do not consider mediating conflicting goals among different users and/or system administrators, and feature limited compatibility across different IoT verticals. In this article, we propose a declarative framework to represent smart environments, user-set goals and customisable mediation policies to reconcile contrasting goals encompassing multiple IoT systems. An open-source Prolog prototype of the framework is showcased over two lifelike motivating examples.

Smart environments enabled by the Internet of Things aim at improving our daily lives by automatically tuning ambient parameters and by achieving energy savings through self-managing cyber-physical systems. Commercial solutions, however, only permit setting simple target goals on those parameters and do not mediate between conflicting goals among different users and/or system administrators, nor across different IoT verticals. In this article, we propose a declarative approach (and its open-source Prolog prototype) to represent smart environments, user-set goals and customisable mediation policies to reconcile contrasting goals across multiple IoT systems.

Designing platforms and methodologies for the orchestration and management of multi-service applications in the Cloud-IoT continuum is becoming more and more a problem of primary importance, due to the current paradigm shifts in the applications development and the new computing paradigms. In light of these needs, in this thesis, we study and implement innovative orchestration techniques, based on the Continuous Reasoning approach, to achieve a scalable, continuous and QoS-compliant management of multi-service applications on large-scale, geographically distributed Cloud-IoT networks. We propose a declarative methodology, and its prototype FogBrainX, to speed up QoS-aware, decision-making in Cloud-IoT application management, and we design and develop a next-generation orchestrator prototype, FogArm, to perform continuous and QoS-compliant management of multi-service applications in Cloud-IoT settings, in continuity with either CI/CD pipelines and infrastructure distributed monitoring tools. Finally, both the methodology and the orchestrator are extensively assessed. To the best of our knowledge, FogArm represents a first complete prototype of a next-gen orchestrator for the continuous QoS-compliant management of multi-service applications on geographically distributed Cloud-IoT nodes.

This thesis aims at designing and prototyping a goal-oriented system for managing domotics IoT devices by suitably reconciling possibly conflicting goals set by different stakeholders. The prototype exploits micro:bit devices for sensing and actuating, extends the Web of Things standard with REST interfaces, and employs the Prolog language for reasoning.