Fog computing aims at extending the Cloud by bringing computational power, storage, and communication capabilities to the edge of the network, in support of the IoT. Segmentation, distribution, and adaptive deployment of functionalities over the continuum from Things to Cloud are challenging tasks, due to the intrinsic heterogeneity, hierarchical structure, and very large scale infrastructure they will have to exploit. In this paper, we propose a simple, yet general, model to support the QoS-aware deployment of multicomponent IoT applications to Fog infrastructures. The model describes operational systemic qualities of the available infrastructure (latency and bandwidth), interactions among software components and Things, and business policies. Algorithms to determine eligible deployments for an application to a Fog infrastructure are presented. A Java tool, FogTorch, based on the proposed model has been prototyped.

Deploying composite applications to Fog nodes in a QoS-and context-aware manner is challenging due to the heterogeneity and scale of Fog infrastructures. Application components must be provided with the software and hardware capabilities they need. Communication links that support interactions between components must meet certain QoS (latency and bandwidth). On the other hand, different Fog and Cloud nodes provide different software and hardware capabilities, and actual communication links support different QoS over time. In this paper we present a prototype (FogTorchII) capable of determining deployments of composite applications to Fog infrastructures, which fulfil software, hardware and QoS requirements. FogTorchII exploits Monte Carlo simulations to take into account possible variations of the QoS of communication links. It classifies eligible deployments both in terms of QoS-assurance and of Fog resource consumption. We illustrate the utility of FogTorchII over a motivating example where we compare different possible deployments for a smart agriculture application.

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.