Managing resources continuity from the edge to the cloud: Architecture and performance

The wide spread deployment of smart edge devices and applications that require real-time data processing, have with no doubt created the need to extend the reach of cloud computing to the edge, recently also referred to as Fog or Edge Computing. Fog computing implements the idea of extending the cloud where thePostprint (author's final draft

An Integrated Edge and Fog System for Future Communication Networks

Put together, the edge and fog form a large diverse pool of computing and networking resources from different owners that can be leveraged towards low latency applications as well as for alleviating high traffic volume in future networks including 5G and beyond. This paper sets out a framework for the integration of edge and fog computing and networking leveraging on ongoing specifications by ETSI MEC ISG and the OpenFog Consortium. It also presents the technological gaps that need to be addressed before such an integrated solution can be developed. These noticeably include challenges relating to the volatility of resources, heterogeneity of underlying technologies, virtualization of devices, and security issues. The framework presented is a Launchpad for a complete solution under development by the 5G-CORAL consortium.This work has been partially funded by the H2020 collaborative Europe/Taiwan research project 5G-CORAL (grant num. 761586

Sviluppo, Deployment e Validazione Sperimentale di Architetture Distribuite di Machine Learning su Piattaforma fog05

Ultimamente sta crescendo sempre di più l'interesse riguardo al fog computing e alle possibilità che offre, tra cui la capacità di poter fruire di una capacità computazionale considerevole anche nei nodi più vicini all’utente finale: questo permetterebbe di migliorare diversi parametri di qualità di un servizio come la latenza nella sua fornitura e il costo richiesto per le comunicazioni. In questa tesi, sfruttando le considerazioni sopra, abbiamo creato e testato due architetture di machine learning distribuito e poi le abbiamo utilizzate per fornire un servizio di predizione (legato al condition monitoring) che migliorasse la soluzione cloud relativamente ai parametri citati prima. Poi, è stata utilizzata la piattaforma fog05, un tool che permette la gestione efficiente delle varie risorse presenti in una rete, per eseguire il deployment delle architetture sopra. Gli obiettivi erano due: validare le architetture in termini di accuratezza e velocità di convergenza e confermare la capacità di fog05 di gestire deployment complessi come quelli necessari nel nostro caso. Innanzitutto, sono state scelte le architetture: per una, ci siamo basati sul concetto di gossip learning, per l'altra, sul federated learning. Poi, queste architetture sono state implementate attraverso Keras e ne è stato testato il funzionamento: è emerso chiaramente come, in casi d'uso come quello in esame, gli approcci distribuiti riescano a fornire performance di poco inferiori a una soluzione centralizzata. Infine, è stato eseguito con successo il deployment delle architetture utilizzando fog05, incapsulando le funzionalità di quest'ultimo dentro un orchestratore creato ad-hoc al fine di gestire nella maniera più automatizzata e resiliente possibile la fornitura del servizio offerto dalle architetture sopra

Performance and efficiency optimization of multi-layer IoT edge architecture

Abstract. Internet of Things (IoT) has become a backbone technology that connects together various devices with diverse capabilities. It is a technology, which enables ubiquitously available digital services for end-users. IoT applications for mission-critical scenarios need strict performance indicators such as of latency, scalability, security and privacy. To fulfil these requirements, IoT also requires support from relevant enabling technologies, such as cloud, edge, virtualization and fifth generation mobile communication (5G) technologies. For Latency-critical applications and services, long routes between the traditional cloud server and end-devices (sensors /actuators) is not a feasible approach for computing at these data centres, although these traditional clouds provide very high computational and storage for current IoT system. MEC model can be used to overcome this challenge, which brings the CC computational capacity within or next on the access network base stations. However, the capacity to perform the most critical processes at the local network layer is often necessary to cope with the access network issues. Therefore, this thesis compares the two existing IoT models such as traditional cloud-IoT model, a MEC-based edge-cloud-IoT model, with proposed local edge-cloud-IoT model with respect to their performance and efficiency, using iFogSim simulator. The results consolidate our research team’s previous findings that utilizing the three-tier edge-IoT architecture, capable of optimally utilizing the computational capacity of each of the three tiers, is an effective measure to reduce energy consumption, improve end-to-end latency and minimize operational costs in latency-critical It applications

Hybrid clouds for data-Intensive, 5G-Enabled IoT applications: an overview, key issues and relevant architecture

Hybrid cloud multi-access edge computing (MEC) deployments have been proposed as efficient means to support Internet of Things (IoT) applications, relying on a plethora of nodes and data. In this paper, an overview on the area of hybrid clouds considering relevant research areas is given, providing technologies and mechanisms for the formation of such MEC deployments, as well as emphasizing several key issues that should be tackled by novel approaches, especially under the 5G paradigm. Furthermore, a decentralized hybrid cloud MEC architecture, resulting in a Platform-as-a-Service (PaaS) is proposed and its main building blocks and layers are thoroughly described. Aiming to offer a broad perspective on the business potential of such a platform, the stakeholder ecosystem is also analyzed. Finally, two use cases in the context of smart cities and mobile health are presented, aimed at showing how the proposed PaaS enables the development of respective IoT applications.Peer ReviewedPostprint (published version

Secure Cloud-Edge Deployments, with Trust

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