Research

FogAtLas RESEARCH

This page summarises the research publications supporting the FogAtlas framework.

Distributed Cloud Intelligence: Implementing An ETSI MANO-Compliant Predictive Cloud Bursting Solution using Openstack and Kubernetes

Authors: Francescomaria Faticanti (1) , Jason Zormpas (2) , Sergey Drozdov (2) , Kewin Rausch (1) , Orlando Avila Garca (3) , Fragkiskos Sardis (2) , Silvio Cretti (1) , Mohsen Amiribesheli (2) , and Domenico Siracusa (1)

(1) Fondazione Bruno Kessler, Italy -- (2) Konica Minolta Laboratory Europe -- (3) Atos, Spain

Abstract: Energy analysis, forecasting and optimization methods play a fundamental role in managing Combine Heat and Power (CHP) systems for energy production, in order to find the most suitable operational point. Indeed, several industries owning such cogeneration systems can significantly reduce overall costs by applying diverse techniques to predict, in real-time, the optimal load of the system. However, this is a complex task that requires processing a large amount of information from multiple data sources (IoT sensors, smart meters and much more), and, in most of the cases, is manually carried out by the energy manager of the company owning the CHP. For this reason, resorting to machine learning methods and new advanced technologies such as fog computing can significantly ease and automate real-time analyses and predictions for energy management systems that deal with huge amounts of data. In this paper we present GEM-Analytics, a new platform that exploits fog computing to enable AI-based methods for energy analysis at the edge of the network. In particular, we present two use cases involving CHP plants that need for optimal strategies to reduce the overall energy supply costs. In all the case studies we show that our platform can improve the energy load predictions compared to baselines thus reducing the costs incurred by industrial customers.

GEM-Analytics: Cloud-to-Edge AI-Powered Energy Management

Authors: Daniele Tovazzi (1) , Francescomaria Faticanti (2) , Domenico Siracusa (2) , Claudio Peroni (1) , Silvio Cretti (2) , Tommaso Gazzini (1)

(1) Energenius Srl, Italy -- (2) Fondazione Bruno Kessler, Italy

Abstract: Energy analysis, forecasting and optimization methods play a fundamental role in managing Combine Heat and Power (CHP) systems for energy production, in order to find the most suitable operational point. Indeed, several industries owning such cogeneration systems can significantly reduce overall costs by applying diverse techniques to predict, in real-time, the optimal load of the system. However, this is a complex task that requires processing a large amount of information from multiple data sources (IoT sensors, smart meters and much more), and, in most of the cases, is manually carried out by the energy manager of the company owning the CHP. For this reason, resorting to machine learning methods and new advanced technologies such as fog computing can significantly ease and automate real-time analyses and predictions for energy management systems that deal with huge amounts of data. In this paper we present GEM-Analytics, a new platform that exploits fog computing to enable AI-based methods for energy analysis at the edge of the network. In particular, we present two use cases involving CHP plants that need for optimal strategies to reduce the overall energy supply costs. In all the case studies we show that our platform can improve the energy load predictions compared to baselines thus reducing the costs incurred by industrial customers.

Deployment of Application Microservices in Multi-Domain Federated Fog Environments

Authors: Francescomaria Faticanti (1), Marco Savi (2), Francesco De Pellegrini (3), Petar Kochovski (4), Vlado Stankovski (4), Domenico Siracusa (1)

(1) Fondazione Bruno Kessler, Italy -- (2) University of Milano-Bicocca, Italy -- (3) University of Avignon, France -- (4) University of Ljubljana, Slovenia

Abstract: In this paper we consider the problem of initial resource selection for a single-domain fog provider lacking sufficient resources for the complete deployment of a batch of IoT applications. To overcome resources shortage, it is possible to lease assets from other domains across a federation of cloud-fog infrastructures to meet the requirements of those applications:the fog provider seeks to minimise the number of external resources to be rented in order to successfully deploy the applications’ demands exceeding own infrastructure capacity. To this aim, we introduce a general framework for the deployment of applications across multiple domains of cloud-fog providers while guaranteeing resources locality constraints. The resource allocation problem is presented in the form of an integer linear program, and we provide a heuristic method that explores the resource assignment space in a breadth-first fashion. Extensive numerical results demonstrate the efficiency of the proposed approach in terms of deployment cost and feasibility with respect to standard approaches adopted in the literature.

Optimal Blind and Adaptive Fog Orchestration under Local Processor Sharing

Authors: Francesco De Pellegrini (1), Francescomaria Faticanti (2), Mandar Datar (3), Eitan Altman (1), Domenico Siracusa (2)

(1) University of Avignon, France -- (2) Fondazione Bruno Kessler, Italy -- (3) INRIA, France

Abstract: This paper studies the tradeoff between running cost and processing delay in order to optimally orchestrate multiple fog applications. Fog applications process batches of objects’ data along chains of containerised microservice modules, which can run either for free on a local fog server or run in cloud at a cost. Processor sharing techniques, in turn, affect the applications’ processing delay on a local edge server depending on the number of application modules running on the same server. The fog orchestrator copes with local server congestion by offloading part of computation to the cloud trading off processing delay for a finite budget. Such problem can be described in a convex optimisation framework valid for a large class of processor sharing techniques. The optimal solution is in threshold form and depends solely on the order induced by the marginal delays of N fog applications. This reduces the original multidimensional problem to an unidimensional one which can be solved in O(N2) by a parallelised search algorithm under complete system information. Finally, an online learning procedure based on a primal-dual stochastic approximation algorithm is designed in order to drive optimal reconfiguration decisions in the dark, by requiring only the unbiased estimation of the marginal delays. Extensive numerical results characterise the structure of the optimal solution, the system performance and the advantage attained with respect to baseline algorithmic solutions.

A Blockchain-based Brokerage Platform for Fog Computing Resource Federation [pdf]

Authors: Marco Savi (1), Daniele Santoro (1), Katarzyna Di Meo (1), Daniele Pizzolli (1), Miguel Pincheira (1), Raffaele Giaffreda (1), Silvio Cretti (1), Seung-woo Kum (2), Domenico Siracusa (1)

(1) Fondazione Bruno Kessler, Trento, Italy -- (2) Korea Electronics Technology Institute, Seoul, Korea

Abstract: This demonstration aims at showcasing an initial version of the DECENTER Brokerage Platform, which leverages an Ethereum blockchain to enable resource federation among different Fog Computing infrastructures. We consider a scenario where an Italian Infrastructure Provider wants to seamlessly extend its pool of resources to get access to an IP camera located in Korea, so that it can deploy an application to locally perform text recognition from a live video stream.

Cutting Throughput on the Edge:App-Aware Placement in Fog Computing [pdf]

Authors: Francescomaria Faticanti, Francesco De Pellegrini, Domenico Siracusa, Daniele Santoro, Silvio Cretti

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.

Foggy: A Platform for Workload Orchestration in a Fog Computing Environment [pdf]

Authors: Daniele Santoro, Daniel Zozin, Daniele Pizzolli, Francesco De Pellegrini, Silvio Cretti

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.

Cloud4IoT: a heterogeneous, distributed and autonomic cloud platform for the IoT [pdf]

Authors: Daniele Pizzolli, Giuseppe Cossu, Daniele Santoro, Luca Capra, Corentin Dupont, Dukas Charalampos, Francesco De Pellegrini, Fabio Antonelli, Silvio Cretti

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.