Stefano Forti

Damiano Azzolini, Stefano Forti, Antonio Ielo

Cloud-edge computing requires applications to operate across diverse infrastructures, often triggered by cyber-physical events. Containers offer a lightweight deployment option but pulling images from central repositories can cause delays. This article presents a novel declarative approach and open-source prototype for replicating container images across the cloud-edge continuum. Considering resource availability, network QoS, and storage costs, we leverage logic programming to (i) determine optimal initial placements via Answer Set Programming (ASP) and (ii) adapt placements using Prolog-based continuous reasoning. We evaluate our solution through simulations, showcasing how combining ASP and Prolog continuous reasoning can balance cost optimisation and prompt decision-making in placement adaptation at increasing infrastructure sizes.

Giang Pham, Rebecca Finetti, Caterina Graziani et al.

Alkaptonuria (AKU) is an ultra-rare autosomal recessive metabolic disorder caused by mutations in the HGD (Homogentisate 1,2-Dioxygenase) gene, leading to a pathological accumulation of homogentisic acid (HGA) in body fluids and tissues. This leads to systemic manifestations, including premature spondyloarthropathy, renal and prostatic stones, and cardiovascular complications. Being ultra-rare, the amount of data related to the disease is limited, both in terms of clinical data and literature. Knowledge graphs (KGs) can help connect the limited knowledge about the disease (basic mechanisms, manifestations and existing therapies) with other knowledge; however, AKU is frequently underrepresented or entirely absent in existing biomedical KGs. In this work, we apply a text-mining methodology based on PubTator3 for large-scale extraction of biomedical relations. We construct two KGs of different sizes, validate them using existing biochemical knowledge and use them to extract genes, diseases and therapies possibly related to AKU. This computational framework reveals the systemic interactions of the disease, its comorbidities, and potential therapeutic targets, demonstrating the efficacy of our approach in analyzing rare metabolic disorders.

Jacopo Massa, Stefano Forti, Federica Paganelli et al.

Cloud-Edge applications like industrial control systems and connected vehicles demand stringent end-to-end latency guarantees. Among existing data plane candidate solutions for bounded latency networking, the guaranteed Latency-Based Forwarding (gLBF) approach ensures punctual delivery of traffic flows by managing per-hop delays to meet specific latency targets, while not requiring that per-flow states are maintained at each hop. However, as a forwarding plane mechanism, gLBF does not define the control mechanisms for determining feasible forwarding paths and per-hop latency budgets for packets to fulfil end-to-end latency objectives. In this work, we propose such a control mechanism implemented in Prolog that complies with gLBF specifications, called declarative gLBF (dgLBF). The declarative nature of Prolog allows our prototype to be concise (~120 lines of code) and easy to extend. We show how the core dgLBF implementation is extended to add reliability mechanisms, path protection, and fate-sharing avoidance to enhance fault tolerance and robustness. Finally, we evaluate the system's performance through simulative experiments under different network topologies and with increasing traffic load to simulate saturated network conditions, scaling up to 6000 flows. Our results show a quasi-linear degradation in placement times and system resilience under heavy traffic.

Giuseppe Bisicchia, Stefano Forti, Antonio Brogi

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.

Stefano Forti, Antonio Brogi

Green software engineering aims at reducing the environmental impact due to developing, deploying, and managing software systems. Meanwhile, Cloud-IoT paradigms can contribute to improving energy and carbon efficiency of application deployments by (i) reducing the amount of data and the distance they must travel across the network, (ii) by exploiting idle edge devices to support application deployment. In this article, we propose a declarative methodology and its Prolog prototype for determining placements of application services onto Cloud-IoT infrastructures so to optimise energy and carbon efficiency, also considering different infrastructure power sources and operational costs. The proposal is assessed over a motivating example.

Stefano Forti, Antonio Brogi

Continuous reasoning has proven effective in incrementally analysing changes in application codebases within Continuous Integration/Continuous Deployment (CI/CD) software release pipelines. In this article, we present a novel declarative continuous reasoning approach to support the management of multi-service applications over the Cloud-IoT continuum, in particular when infrastructure variations impede meeting application's hardware, software, IoT or network QoS requirements. We show how such an approach brings considerable speed-ups compared to non-incremental reasoning.

Stefano Forti, Gian-Luigi Ferrari, Antonio Brogi

Assessing the security level of IoT applications to be deployed to heterogeneous Cloud-Edge infrastructures operated by different providers is a non-trivial task. In this article, we present a methodology that permits to express security requirements for IoT applications, as well as infrastructure security capabilities, in a simple and declarative manner, and to automatically obtain an explainable assessment of the security level of the possible application deployments. The methodology also considers the impact of trust relations among different stakeholders using or managing Cloud-Edge infrastructures. A lifelike example is used to showcase the prototyped implementation of the methodology.