Machine Learning-Powered Management Architectures for Edge Services in 5G Networks

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Thermal boundary resistance at Si/Ge interfaces determined by approach-to-equilibrium molecular dynamics simulations

The thermal boundary resistance of Si/Ge interfaces as been determined using approach-to-equilibrium molecular dynamics simulations. Assuming a reciprocal linear dependence of the thermal boundary resistance, a length-independent bulk thermal boundary resistance could be extracted from the calculation resulting in a value of 3.76x10$^{-9}$ m$^2$ K/W for a sharp Si/Ge interface and thermal transport from Si to Ge. Introducing an interface with finite thickness of 0.5 nm consisting of a SiGe alloy, the bulk thermal resistance slightly decreases compared to the sharp Si/Ge interface. Further growth of the boundary leads to an increase in the bulk thermal boundary resistance. When the heat flow is inverted (Ge to Si), the thermal boundary resistance is found to be higher. From the differences in the thermal boundary resistance for different heat flow direction, the rectification factor of the Si/Ge has been determined and is found to significantly decrease when the sharp interface is moderated by introduction of a SiGe alloy in the boundary layer.Comment: 7 pages, 6 figure

An RL approach to radio resource management in heterogeneous virtual RANs

Proceedings of: 2021 16th Annual Conference on Wireless On-demand Network Systems and Services Conference (WONS), 9-11 March 2021, Klosters, Switzerland.5G networks are primarily designed to support a wide range of services characterized by diverse key performance indicators (KPIs). A fundamental component of 5G networks, and a pivotal factor to the fulfillment of the services KPIs, is the virtual radio access network (RAN), which allows high flexibility on the control of the radio link. However, to fully exploit the potentiality of virtual RANs in non-stationary environments, an efficient mapping of the rapidly varying context to radio control decisions is not only essential, but also challenging owing to the non-trivial interdependence of network and channel conditions. In this paper, we propose CAREM, an RL framework for dynamic radio resource allocation, which selects the best link and modulation and coding scheme (MCS) for packet transmission, so as to meet the KPI requirements in heterogeneous virtual RANs. To show its effectiveness in real-world conditions, we provide a proof-of-concept through actual testbed implementation. Experimental results demonstrate that CAREM enables an efficient radio resource allocation, for any of the considered time periodicity of the decision-making process.This work has been supported by the EC H2020 5GPPP 5GROWTH project (Grant No. 856709.

A Context-aware Radio Resource Management in Heterogeneous Virtual RANs

New-generation wireless networks are designed to support a wide range of services with diverse key performance indicators (KPIs) requirements. A fundamental component of such networks, and a pivotal factor to the fulfillment of the services target KPIs, is the virtual radio access network (vRAN), which allows high flexibility on the control of the radio link. However, to fully exploit the potentiality of vRANs in non- stationary environments, an efficient mapping of the rapidly varying context to radio control decisions is not only essential, but also challenging owing to the interdependence of user traffic demand, channel conditions, and resource allocation. In this paper, we propose CAREM, a reinforcement learning framework for dynamic radio resource allocation in heterogeneous vRANs, which selects the best available link and transmission parameters for packet transfer, so as to meet the KPI requirements. To show its effectiveness in real-world conditions, we provide a proof-of- concept through a testbed implementation. Experimental results demonstrate that CAREM enables an efficient radio resource allocation, for different time periodicity of the decision-making process as well as under different settings and traffic demand. Furthermore, the results show that CAREM outperforms state- of-the-art solutions as well as standard cellular technologies: when compared to the closest existing scheme based on neural network and the standard LTE, it exhibits an improvement of about one order of magnitude in both packet loss and latency, while it provides a 65% latency improvement with respect to relatively simpler and more popular contextual bandit approach

Fair and Scalable Orchestration of Network and Compute Resources for Virtual Edge Services

The combination of service virtualization and edge computing allows for low latency services, while keeping data storage and processing local. However, given the limited resources available at the edge, a conflict in resource usage arises when both virtualized user applications and network functions need to be supported. Further, the concurrent resource request by user applications and network functions is often entangled, since the data generated by the former has to be transferred by the latter, and vice versa. In this paper, we first show through experimental tests the correlation between a video-based application and a vRAN. Then, owing to the complex involved dynamics, we develop a scalable reinforcement learning framework for resource orchestration at the edge, which leverages a Pareto analysis for provable fair and efficient decisions. We validate our framework, named VERA, through a real-time proof-of-concept implementation, which we also use to obtain datasets reporting real-world operational conditions and performance. Using such experimental datasets, we demonstrate that VERA meets the KPI targets for over 96% of the observation period and performs similarly when executed in our real-time implementation, with KPI differences below 12.4%. Further, its scaling cost is 54% lower than a centralized framework based on deep-Q networks

VERA: Resource Orchestration for Virtualized Services at the Edge

The combination of service virtualization and edge computing allows mobile users to enjoy low latency services, while keeping data storage and processing local. However, the network edge has limited resource availability, and when both virtualized user applications and network functions need to be supported concurrently, a natural conflict in resource usage arises. In this paper, we focus on computing and radio resources and develop a framework for resource orchestration at the edge that leverages a model-free reinforcement learning approach and a Pareto analysis, which is proved to make fair and efficient decisions. Through our testbed, we demonstrate the effectiveness of our solution in resource-limited scenarios where standard multi-agent solutions violate the system’s capacity constraints systematically, e.g., over 70% violation rate with 2 vCPUs in our testbed. Index Terms—Virtual RAN, virtualized services, resource or- chestration, machine learning, experimental testbe

Sleep‐related hypermotor epilepsy and non‐rapid eye movement parasomnias: Differences in the periodic and aperiodic component of the electroencephalographic power spectra

Over the last two decades, our understanding of clinical and pathophysiological aspects of sleep-related epileptic and non-epileptic paroxysmal behaviours has improved considerably, although it is far from complete. Indeed, even if many core characteristics of sleep-related hypermotor epilepsy and non-rapid eye movement parasomnias have been clarified, some crucial points remain controversial, and the overlap of the behavioural patterns between these disorders represents a diagnostic challenge. In this work, we focused on segments of multichannel sleep electroencephalogram free from clinical episodes, from two groups of subjects affected by sleep-related hypermotor epilepsy (N = 15) and non-rapid eye movement parasomnias (N = 16), respectively. We examined sleep stages N2 and N3 of the first part of the night (cycles 1 and 2), and assessed the existence of differences in the periodic and aperiodic components of the electroencephalogram power spectra between the two groups, using the Fitting Oscillations & One Over f (FOOOF) toolbox. A significant difference in the gamma frequency band was found, with an increased relative power in sleep-related hypermotor epilepsy subjects, during both N2 (p < .001) and N3 (p < .001), and a significant higher slope of the aperiodic component in non-rapid eye movement parasomnias, compared with sleep-related hypermotor epilepsy, during N3 (p = .012). We suggest that the relative power of the gamma band and the slope extracted from the aperiodic component of the electroencephalogram signal may be helpful to characterize differences between subjects affected by non-rapid eye movement parasomnias and those affected by sleep-related hypermotor epilepsy

Razionale sull'impiego del fenofibrato come terapia aggiuntiva nell'epilessia frontale notturna (NFLE) farmacoresistente

Nocturnal Frontal Lobe Epilepsy (NFLE) is characterized by onset during infancy or childhood with persistence in adulthood, family history of similar nocturnal episodes simulating non-REM parasomnias (sleep terrors or sleepwalking), general absence of morphological substrates, often by normal interictal electroencephalographical recordings (EEGs) during wakefulness. A family history of epilepsy may be present with Mendelian autosomal dominant inheritance has been described in some families. Recent studies indicate the involvement of neuronal nicotinic acetylcholine receptors (nAChRs) in the molecular mechanisms of NFLE. Mutations in the genes encoding for the α4 (CHRNA4) and ß2 (CHRNB2) subunits of the nAChR induce changes in the biophysical properties of nAChR, resulting generally in a “gain of function”. Preclinical studies report that activation of a nuclear receptor called type peroxisome proliferator-activated receptor (PPAR-α) by endogenous molecules or by medications (e.g. fenofibrate) reduces the activity of the nAChR and, therefore, may decrease the frequency of seizures. Thus, we hypothesize that negative modulation of nAChRs might represent a therapeutic strategy to be explored for pharmacological treatment of this form of epilepsy, which only partially responds to conventional antiepileptic drugs. In fact, carbamazepine, the current medication for NFLE, abolishes the seizures only in one third of the patients. The aim of the project is: 1)_to verify the clinical efficacy of adjunctive therapy with fenofibrate in pharmacoresistant NFLE and ADNFLE patients; focousing on the analysis of the polysomnographic action of the PPAR- agonist (fenofibrate). 2)_to demonstrate the subtended mechanism of efficacy by means of electrophysiological and behavioral experiments in an animal model of the disease: particularly, transgenic mice carrying the mutation in the nAChR 4 subunit (Chrna4S252F) homologous to that found in the humans. Given that a PPAR-α agonist, FENOFIBRATE, already clinically utilized for lipid metabolism disorders, provides a promising therapeutic avenue in the treatment of NFLE\ADNFLE