Edge Computing for Extreme Reliability and Scalability

The massive number of Internet of Things (IoT) devices and their continuous data collection will lead to a rapid increase in the scale of collected data. Processing all these collected data at the central cloud server is inefficient, and even is unfeasible or unnecessary. Hence, the task of processing the data is pushed to the network edges introducing the concept of Edge Computing. Processing the information closer to the source of data (e.g., on gateways and on edge micro-servers) not only reduces the huge workload of central cloud, also decreases the latency for real-time applications by avoiding the unreliable and unpredictable network latency to communicate with the central cloud

5G-Kube: Complex Telco Core Infrastructure Deployment Made Low-Cost

Network Function Virtualization (NFV) along with Software Defined Networking (SDN) have brought an evolution in telecommunications laying out the bases for 5G networks and its softwarization. Accordingly, new implementations of telecom standards, such as the 3GPP 5G Core, are defined as fully-virtualized infrastructures consisting of different components and leveraging a cloud-native approach. At the same time, standard-oriented solutions, such as ETSI Management and Orchestration (MANO), have emerged to master the complexity of Virtualized Network Functions (VNFs) orchestration, including 5G Core VNFs. While MANO operates at the NFV level, it also leverages existing cloud infrastructures for the deployment of VNFs by interoperating with resource orchestrators at the cloud level. From the business perspective, that requires telco operators to interact with different technology providers, from NFV/MANO software producers to cloud computing providers, and to hire technicians proficient in the technologies of both telco and computing worlds, that are a rather difficult human resourcing to find. The main claim of the article is that the Development and Operations (DevOps) tools in the IT world are mature enough to leverage them directly in the telco world, without superimposing other interlaced standard/software. That allows to significantly reduce OPEX cost of complex telco infrastructures by supporting all needed automation and by avoiding the combined use of (too) complex layered standards/software stacks, such as in the case of MANO. Accordingly, in this article, we leverage container-based technologies and Kubernetes to design and evaluate a novel deployment approach, called 5G-Kube, for softwarized 5G core networks. 5G-Kube, which is openly to the community, has been also evaluated in two different use cases of the 5G Core and Kubernetes deployment fitting, namely, Industry 4.0 and Smart Cities

Modeling Digital Twins of Kubernetes-Based Applications

Kubernetes provides several functions that can help service providers to deal with the management of complex container-based applications. However, most of these functions need a time-consuming and costly customization process to address service-specific requirements. The adoption of Digital Twin (DT) solutions can ease the configuration process by enabling the evaluation of multiple configurations and custom policies by means of simulation-based what-if scenario analysis. To facilitate this process, this paper proposes KubeTwin, a framework to enable the definition and evaluation of DTs of Kubernetes applications. Specifically, this work presents an in- novative simulation-based inference approach to define accurate DT models for a Kubernetes environment. We experimentally validate the proposed solution by implementing a DT model of an image recognition application that we tested under different conditions to verify the accuracy of the DT model. The soundness of these results demonstrates the validity of the KubeTwin approach and calls for further investigation

Software Defined Networking for Quality-aware Management of Multi-hop Spontaneous Networks

The Software Defined Networking (SDN) approach has recently demonstrated its effectiveness in simplifying the dynamic management of networking capabilities of infrastructure environments such as datacenters, e.g., by greatly enhancing the flexibility of dispatching features provided by industrial-grade switches. Inspired by the previous and more traditional scenario, we propose SDN adoption in infrastructure-less distributed multi-hop spontaneous networks based on the impromptu collaboration of fixed/mobile devices, with the goal of significantly improving the Quality of Service perceived by final users. To this purpose, the paper outlines our primary guidelines and reference architecture to support quality-aware packet dispatching. In particular, we present how collaborative nodes can exploit the SDN approach to appropriately manage the quality of different traffic flows, by avoiding undesired interference and by taking into consideration network capabilities/conditions and application-level requirements

An SDN-Enabled Architecture for IT/OT Converged Networks: A Proposal and Qualitative Analysis under DDoS Attacks

Real-time business practices require huge amounts of data directly from the production assets. This new thirst for accurate and timely data has forced the convergence of the traditionally business-focused information technology (IT) environment with the production-focused operational technology (OT). Recently, software-defined network (SDN) methodologies have benefitted OT networks with enhanced situational awareness, centralized configuration, deny-by-default forwarding rules, and increased performance. What makes SDNs so innovative is the separation between the control plane and the data plane, centralizing the command in the controllers. However, due to their young age, the use of SDNs in the industry context has not yet matured comprehensive SDN-based architectures for IT/OT networks, which are also resistant to security attacks such as denial-of-service ones, which may occur in SDN-based industrial IoT (IIoT) networks. One main motivation is that the lack of comprehensive SDN-based architectures for IT/OT networks making it difficult to effectively simulate, analyze, and identify proper detection and mitigation strategies for DoS attacks in IT/OT networks. No consolidated security solutions are available that provide DoS detection and mitigation strategies in IT/OT networks. Along this direction, this paper’s contributions are twofold. On the one hand, this paper proposes a convergent IT/OT SDN-based architecture applied in a real implementation of an IT/OT support infrastructure called SIRDAM4.0 within the context of the SBDIOI40 project. On the other hand, this paper proposes a qualitative analysis on how this architecture works under DoS attacks, focusing on what the specific problems and vulnerabilities are. In particular, we simulated several distributed denial-of-service (DDoS) attack scenarios within the context of the proposed architecture to show the minimum effort needed by the attacker to hack the network, and our obtained experimental results show how it is possible to compromise the network, thus considerably worsening the performance and, in general, the functioning of the network. Finally, we conclude our analysis with a brief description on the importance of employing machine learning approaches for attack detection and for mitigation techniques

A Layered Middleware for OT/IT Convergence to Empower Industry 5.0 Applications

We are still in the midst of Industry 4.0 (I4.0), with more manufacturing lines being labeled as smart thanks to the integration of advanced ICT in Cyber–Physical Systems (CPS). While I4.0 aims to provision cognitive CPS systems, the nascent Industry 5.0 (I5.0) era goes a step beyond, aiming to build cross-border, sustainable, and circular value chains benefiting society as a whole. An enabler of this vision is the integration of data and AI in the industrial decision-making process, which does not exhibit yet a coordination between the Operation and Information Technology domains (OT/IT). This work proposes an architectural approach and an accompanying software prototype addressing the OT/IT convergence problem. The approach is based on a two-layered middleware solution, where each layer aims to better serve the specific differentiated requirements of the OT and IT layers. The proposal is validated in a real testbed, employing actual machine data, showing the capacity of the components to gracefully scale and serve increasing data volumes

A Support Infrastructure for Machine Learning at the Edge in Smart City Surveillance

Nowadays, the massive usage of mobile and IoT applications generate large amounts of data. Due to several reasons, including latency and bandwidth, it is not practical to send all generated data to the cloud. Recent standardization efforts, namely, Fog computing and the Multi-access Edge Computing (MEC), provide an extension of Cloud computing storage and network resources placed in a geographically distributed manner at the edge of the network closer to mobiles and IoT devices. These paradigms allow low latency, high bandwidth, and location-based awareness. In this paper, we present an infrastructure to support distributed Machine Learning (ML) by enabling edge devices to collaboratively learn a shared model while keeping local knowledge stored at the edge of the network. In addition, we claim the possibility of improving the model through the cloud that acts as a supervisor of the system that contains the global knowledge of the entire system through the integration of local edge models. We describe our architectural proposal and analyze a case study, namely video streaming processing for face recognition, deployed in a collaborative edge network. Finally, we report experimental results that show the potential advantages of using our approach instead of ML algorithms completely expected at the cloud infrastructure

DRIVE: Discovery seRvice for fully-Integrated 5G enVironmEnt in the IoT

A lot of research is being carried out about Internet of Things (IoT) and in the following years it will emerge more and more in our lives. Furthermore, with the advent of future fully-integrated 5G networks, new constraints need to be satisfied such as ultra-reliability and low-latency. With the help of Fog computing and the Multi-access Edge Computing (MEC) framework, services can be offered to the end users in a fast and practical way. Our work presents DRIVE a framework for service discovery in a 5G environment. However, in order to guarantee dynamic distribution and best management of services, we plan to deploy those services as container (e.g. Docker container). Moreover, we propose distribution of edge services at three different layer of communication: Application, Service, and Communication Layer. Given the above considerations, we propose an edge node, placed at the edge of network, that acts as the \uabbrain\ubb and take over the computation. The main innovative elements of the proposed framework, compared to the existing literature, include the possibility to select the working layer, the dynamic reconfiguration of the edge node and the field experimental results about the performance achieved by our solution over rapidly deployable environments with resourcelimited edge nodes such as Raspberry Pi devices