The emerging 5G paradigm will enable multi-radio smartphones to run high-rate stream applications. However, since current smartphones remain resource and battery-limited, the 5G era opens new challenges on how to actually support these applications. In principle, the service orchestration capability of the Fog and Cloud Computing paradigms could be an effective means of dynamically providing resource-augmentation to smartphones. Motivated by these considerations, the peculiar focus of this paper is on the joint and adaptive optimization of the resource and task allocations of mobile stream applications in 5G-supported multi-tier Mobile-Fog-Cloud virtualized ecosystems. The objective is the minimization of the computing-plus-network energy of the overall ecosystem under hard constraints on the minimum streaming rate and the maximum computing-plus-networking resources. To this end, (i) we model the target ecosystem energy by explicitly accounting for the virtualized and multi-core nature of the Fog/Cloud servers; (ii) since the resulting problem is nonconvex and involves both continuous and discrete variables, we develop an optimality-preserving decomposition into the cascade of a (continuous) resource allocation sub-problem and a (discrete) task-allocation sub-problem; (iii) we numerically solve the first sub-problem through a suitably designed set of gradient-based adaptive iterations, while we approach the solution of the second sub-problem by resorting to an ad-hoc-developed elitary Genetic algorithm. Finally, we design the main blocks of EcoMobiFog, a technological virtualized platform for supporting the developed solver. Extensive numerical tests confirm that the energy-delay performance of the proposed solving framework is typically within a few per-cent the benchmark one of the exhaustive searchbased solution.

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The incoming era of the Fifth Generation (5G) Fog Computing (FC)-supported Radio Access Networks (shortly, 5G FOGRANs) aims at exploiting the virtualization of the computing/networking resources. The goal is to allow battery-powered wireless devices to augment their limited native resources through the seamless (e.g., live) migration of Virtual Machines (VMs) towards nearby Fog data centers. For this purpose, the bandwidths of the multiple heterogeneous Wireless Network Interface Cards (WNICs) typically equipping the current wireless devices may be used in parallel under the control of the emerging MultiPathTCP (MPTCP) protocol. However, due to the fading and mobility-induced phenomena impairing the underlying RANs, the energy consumptions and migration delays of the current state-of-the-art VM migration techniques may drastically reduce their expected benefits. Motivated by these considerations, in this paper, we analytically characterize, implement in software and numerically test the optimal minimum-energy Settable-Complexity Bandwidth Manager (SCBM) for the live migration of VMs over MPTCP-supported 5G FOGRAN connections. The key features of the proposed SCBM are that: (i) its implementation complexity is settable on-line, in order to attain the targeted energy consumption-vs.-implementation complexity tradeoff; (ii) it minimizes the energy consumed by the migration process under hard QoS constraints on the allowed migration time and downtime;and, (iii) by leveraging a suitably designed adaptive mechanism, it is capable to quickly react to (possibly, unpredicted) fading and/or mobility-induced abrupt changes of the operating conditions without requiring and forecasting support. We numerically test and compare the energy and delay performances of the proposed adaptive SCBM under several 3G/4G/WiFi FOGRAN scenarios by considering a number of synthetic and real-world workloads. The obtained numerical results point out that: (i) the energy savings of the proposed adaptive SCBM over the benchmark one currently implemented by legacy Xen, KWM and VMware hypervisors are typically over 25% and approach 70% when the migrated applications are memory-write intensive; and, (ii) the MPTCP may reduce the energy consumption of the proposed SCBM over legacy SinglePathTCP (SPTCP) more than 50% under strict real-time limits on the allowed migration times.

Chest imaging can represent a powerful tool for detecting the Coronavirus disease 2019 (COVID-19). Among the available technologies, the chest Computed Tomography (CT) scan is an effective approach for reliable and early detection of the disease. However, it could be difficult to rapidly identify by human inspection anomalous area in CT images belonging to the COVID-19 disease. Hence, it becomes necessary the exploitation of suitable automatic algorithms able to quick and precisely identify the disease, possibly by using few labeled input data, because large amounts of CT scans are not usually available for the COVID-19 disease. The method proposed in this paper is based on the exploitation of the compact and meaningful hidden representation provided by a Deep Denoising Convolutional Autoencoder (DDCAE). Specifically, the proposed DDCAE, trained on some target CT scans in an unsupervised way, is used to build up a robust statistical representation generating a target histogram. A suitable statistical distance measures how this target histogram is far from a companion histogram evaluated on an unknown test scan: if this distance is greater of a threshold, the test image is labeled as anomaly, i.e. the scan belongs to a patient affected by COVID-19 disease. Some experimental results and comparisons with other state-of-the-art methods show the effectiveness of the proposed approach reaching a top accuracy of 100% and similar high values for other metrics. In conclusion, by using a statistical representation of the hidden features provided by DDCAEs, the developed architecture is able to differentiate COVID-19 from normal and pneumonia scans with high reliability and at low computational cost.

It is expected that the pervasive deployment of multi-tier 5G-supported Mobile-Fog-Cloudtechnological computing platforms will constitute an effective means to support the real-time execution of future Internet applications by resource- and energy-limited mobile devices. Increasing interest in this emerging networking-computing technology demands the optimization and performance evaluation of several parts of the underlying infrastructures. However, field trials are challenging due to their operational costs, and in every case, the obtained results could be difficult to repeat and customize. These emerging Mobile-Fog-Cloud ecosystems still lack, indeed, customizable software tools for the performance simulation of their computing-networking building blocks. Motivated by these considerations, in this contribution, we present VirtFogSim. It is a MATLAB-supported software toolbox that allows the dynamic joint optimization and tracking of the energy and delay performance of Mobile-Fog-Cloud systems for the execution of applications described by general Directed Application Graphs (DAGs). In a nutshell, the main peculiar features of the proposed VirtFogSim toolbox are that: (i) it allows the joint dynamic energy-aware optimization of the placement of the application tasks and the allocation of the needed computing-networking resources under hard constraints on acceptable overall execution times; (ii) it allows the repeatable and customizable simulation of the resulting energy-delay performance of the overall system; (iii) it allows the dynamic tracking of the performed resource allocation under time-varying operational environments, as those typically featuring mobile applications; (iv) it is equipped with a user-friendly Graphic User Interface (GUI) that supports a number of graphic formats for data rendering; and (v) its MATLAB code is optimized for running atop multi-core parallel execution platforms. To check both the actual optimization and scalability capabilities of the VirtFogSim toolbox, a number of experimental setups featuring different use cases and operational environments are simulated, and their performances are compared.