This page summarises the research publications supporting the FogAtlas framework.
Abstract: Fog computing extends cloud computing technology to the edge of the infrastructure to let IoT applications access objects' data with reduced latency, location awareness and dynamic computation. By displacing workloads from the central cloud to the edge devices, fog computing overcomes communication bottlenecks avoiding raw data transfer to the central cloud, thus paving the way for the next generation IoT-based applications. In this paper we study scheduling and placement of applications in fog computing, which is key to ensure profitability for the involved stakeholders. We consider a scenario where the emerging microservice architecture allows for the design of applications as cascades of coupled microservice modules. It results into a mixed integer non linear problem involving constraints on both application data flows and computation placement. Due to the complexity of the original problem, we resort to a simplified version, which is further solved using a greedy algorithm. This algorithm is the core placement logic of the FogAtlas platform, a fog computing platform based on existing virtualization technologies. Extensive numerical results validate the model and the scalability of the proposed solution, showing it attains performance close to the optimal solution and, in our real implementation, it scales well with respect to the number of served applications.
Abstract: In this paper we present Foggy, an architectural framework and software platform based on Open Source technologies. Foggy orchestrates application workload, negotiates resources and supports IoT operations for multi-tier, distributed, heterogeneous and decentralized Cloud Computing systems. Foggy is tailored for emerging domains such as 5G Networks and IoT, which demand resources and services to be distributed and located close to data sources and users following the Fog Computing paradigm. Foggy provides a platform for infrastructure owners and tenants (i.e., application providers) offering functionality of negotiation, scheduling and workload placement taking into account traditional requirements (e.g. based on RAM, CPU, disk) and non-traditional ones (e.g. based on networking) as well as diversified constraints on location and access rights. Economics and pricing of resources can also be considered by the Foggy model in a near future. The ability of Foggy to find a trade-off between infrastructure owners' and tenants' needs, in terms of efficient and optimized use of the infrastructure while satisfying the application requirements, is demonstrated through three use cases in the video surveillance and vehicle tracking contexts.
Abstract: We introduce Cloud4IoT, a platform offering automatic deployment, orchestration and dynamic configuration of IoT support software components and data-intensive applications for data processing and analytics, thus enabling plug-and-play integration of new sensor objects and dynamic workload scalability. Cloud4IoT enables the concept of Infrastructure as Code in the IoT context: it empowers IoT operations with the flexibility and elasticity of Cloud services. Furthermore it shifts traditionally centralized Cloud architectures towards a more distributed and decentralized computation paradigm, as required by IoT technologies, bridging the gap between Cloud Computing and IoT ecosystems. Thus, Cloud4IoT is playing a role similar to the one covered by solutions like Fog Computing, Cloudlets or Mobile Edge Cloud. The hierarchical architecture of Cloud4IoThosts a central Cloud platform and multiple remote edge Cloud modules supporting dedicated devices, namely the IoT Gateways, through which new sensor objects are made accessible to the platform. Overall, the platform is designed in order to support systems where IoT-based and data intensive applications may pose specific requirements for low latency, restricted available bandwidth, or data locality. Cloud4IoT is built on several Open Source technologies for containerisation and implementations of standards, protocols and services for the IoT. We present the implementation of the platform and demonstrate it in two different use cases.