In this paper, we investigate the diffusion containment problem through a novel hyperparametric influence minimization model. This model integrates a hyperparametric diffusion framework into the classical influence minimization paradigm, enabling practical, flexible, and fine-grained control over diffusion dynamics via feature interventions on nodes. The objective is to minimize the diffusion from initial seeds, by optimizing the interventions on node feature values. We analyze the challenges and intrinsic properties of hyperparametric influence minimization and derive an upper-bound on the spread, which quantifies the total uncertainty of nodes remaining inactive during the diffusion process. We prove that it exhibits supermodularity in the context of the node selection problem. Based on that, we further design greedy-based algorithms to solve the problem, which outperform the state-of-the-art methods.

In this paper, we study cascading failures in power grids through the lens of information diffusion models. Similar to the spread of rumors or influence in an online social network, it has been observed that failures (outages) in a power grid can spread contagiously, driven by viral spread mechanisms. We employ a stochastic diffusion model that is Markovian (memoryless) and local (the activation of one node, i.e., transmission line, can only be caused by its neighbors). Our model integrates viral diffusion principles with physics-based concepts, by correlating the diffusion weights (contagion probabilities between transmission lines) with the hyperparametric Information Cascades (IC) model. We show that this diffusion model can be learned from traces of cascading failures, enabling accurate modeling and prediction of failure propagation. This approach facilitates actionable information through well-understood and efficient graph analysis methods and graph diffusion simulations. Furthermore, by leveraging the hyperparametric model, we can predict diffusion and mitigate the risks of cascading failures even in unseen grid configurations, whereas existing methods falter due to a lack of training data. Extensive experiments based on a benchmark power grid and simulations therein show that our approach effectively captures the failure diffusion phenomena and guides decisions to strengthen the grid, reducing the risk of large-scale cascading failures. Additionally, we characterize our model's sample complexity, improving upon the existing bound.

One of the main DevOps practices is the automation of resource provisioning and deployment of complex software. This automation is enabled by the explicit definition of Infrastructure-as-Code (IaC), i.e., a set of scripts, often written in different modeling languages, which defines the infrastructure to be provisioned and applications to be deployed. We introduce the DevOps Modeling Language (DOML), a new Cloud modeling language for infrastructure deployments. DOML is a modeling approach that can be mapped into multiple IaC languages, addressing infrastructure provisioning, application deployment and configuration. The idea behind DOML is to use a single modeling paradigm which can help to reduce the need of deep technical expertise in using different specialized IaC languages. We present the DOML’s principles and discuss the related work on IaC languages. Furthermore, the advantages of the DOML for the end-user are demonstrated in comparison with some state-of-the-art IaC languages such as Ansible, Terraform, and Cloudify, and an evaluation of its effectiveness through several examples and a case study is provided.

Abstract One of the main DevOps practices is the automation of resource provisioning and deployment of complex software. This automation is enabled by the explicit definition of Infrastructure-as-Code (IaC), i.e., a set of scripts, often written in different modelling languages, which defines the infrastructure and applications to be deployed. We introduce the DevOps Modelling Language (DOML), a new Cloud modelling language for infrastructure deployments. DOML is a modelling approach that can be mapped into multiple IaC languages, addressing infrastructure provisioning, application deployment and configuration at once. The idea behind DOML is to use a single modelling paradigm which can help to reduce the need of deep technical expertise in using different specialised IaC languages. We present the DOML’s principles and discuss the related work on IaC languages. We demonstrate the DOML advantages for the end-user in comparison with state-of-the-art IaC languages such as Ansible, Terraform, and Cloudify, and show its effectiveness through an example.