Orchestrating energy-efficient vRANs: Bayesian learning and experimental results

Virtualized base stations (vBS) can be implemented in diverse commodity platforms and are expected to bring unprecedented operational flexibility and cost efficiency to the next generation of cellular networks. However, their widespread adoption is hampered by their complex configuration options that affect in a non-traditional fashion both their performance and their power consumption requirements. Following an in-depth experimental analysis in a bespoke testbed, we characterize the vBS power cost profile and reveal previously unknown couplings between their various control knobs. Motivated by these findings, we develop a Bayesian learning framework for the orchestration of vBSs and design two novel algorithms: (i) BP-vRAN, which employs online learning to balance the vBS performance and energy consumption, and (ii) SBP-vRAN, which augments our optimization approach with safe controls that maximize performance while respecting hard power constraints. We show that our approaches are data-efficient, i.e., converge an order of magnitude faster than state-of-the-art Deep Reinforcement Learning methods, and achieve optimal performance. We demonstrate the efficacy of these solutions in an experimental prototype using real traffic traces.This work has been supported by the European Commission through Grant No. 101017109 (DAEMON project), and the CERCA Programme/Generalitat de Catalunya

Increased bile resistance in Salmonella enterica mutants lacking Prc periplasmic protease

Prc is a periplasmic protease involved in processing of penicillin-binding protein 3 (PBP3). Lack of Prc suppressesbile sensitivity in Dam-, Wec-, PhoP-, DamX-, and SeqA- mutants of Salmonella enterica, and increases bile resistance in thewild type. Changes in the activity of penicillin binding proteins PBP3, PBP4, PBP5/6 and PBP7 are detected in a Prc-background, suggesting that peptidoglycan remodeling might contribute to bile resistance. [Int Microbiol 2013; 16(2):87-92]Keywords: Salmonella; bile; Prc protease; peptidoglycan; penicillin-binding protein

Demo: vrAIn proof-of-concept: a deep learning approach for virtualized RAN resource control

Proceeding of: 25th Annual International Conference on Mobile Computing and Networking (MobiCom'19), October 21-25, 2019, Los Cabos, Mexico.While the application of the NFV paradigm into the network is proceeding full steam ahead, there is still one last milestone to be achieved in this context: the virtualization of the radio access network (vRAN). Due to the very complex dependency between the radio conditions and the computing resources needed to provide the baseband processing functionality, attaining an efficient resource control is particularly challenging. In this demonstration, we will showcase vrAIn, a vRAN dynamic resource controller that employs deep reinforcement learning to perform resource assignment decisions. vrAIn, which is implemented using an open-source LTE stack over a Linux platform, can achieve substantial savings in the used CPU resources while maintaining the target QoS for the attached terminals and maximizing throughput when there is a deficit of computational capacity.The work of University Carlos III of Madrid was supported by H2020 5G-MoNArch project (grant agreement no. 761445) and H2020 5G-TOURS project (grant agreement no. 856950). The work of NEC Laboratories Europe was supported by H2020 5G-TRANSFORMER project (grant agreement no. 761536) and 5GROWTH project (grant agreement no. 856709). The work of University of Cartagena was supported by Grant AEI/FEDER TEC2016-76465-C2-1-R (AIM) and Grant FPU14/03701

vrAIn: Deep Learning based Orchestration for Computing and Radio Resources in vRANs

In Press / En PrensaThe virtualization of radio access networks (vRAN) is the last milestone in the NFV revolution. However, the complexrelationship between computing and radio dynamics make vRAN resource control particularly daunting. We present vrAIn, a resourceorchestrator for vRANs based on deep reinforcement learning. First, we use an autoencoder to project high-dimensional context data(traffic and channel quality patterns) into a latent representation. Then, we use a deep deterministic policy gradient (DDPG) algorithmbased on an actor-critic neural network structure and a classifier to map contexts into resource control decisions.We have evaluated vrAIn experimentally, using an open-source LTE stack over different platforms, and via simulations over aproduction RAN. Our results show that: (i) vrAIn provides savings in computing capacity of up to 30% over CPU-agnostic methods;(ii) it improves the probability of meeting QoS targets by 25% over static policies; (iii) upon computing capacity under-provisioning,vrAIn improves throughput by 25% over state-of-the-art schemes; and (iv) it performs close to an optimal offline oracle. To ourknowledge, this is the first work that thoroughly studies the computational behavior of vRANs and the first approach to a model-freesolution that does not need to assume any particular platform or context.This work was partially supported by the European Commission through Grant No. 856709 (5Growth) and Grant No. 856950 (5G-TOURS); by Science Foundation Ireland (SFI) through Grant No. 17/CDA/4760; and AEI/FEDER through project AIM under Grant No. TEC2016-76465-C2-1-R. Furthermore, the work is closely related to the EU project DAEMON (Grant No. 101017109)

Chagas disease in Latin American migrants: a Spanish challenge

AbstractChagas’ disease affects millions in Latin America and is the leading cause of cardiomyopathy and death due to cardiovascular disease in patients aged 30–50 years. As a consequence of immigration it has settled in several European countries, where besides imported cases, autochthonous infections arise through vertical transmission and blood/organ donation. All Latin American immigrants who attended our Unit were screened for T. cruzi infection (ELISA and IFAT ± PCR). An ECG and echocardiogram were requested for all positive patients, and oesophageal manometry, barium swallow and barium enema were requested according to patient symptoms. All patients under 50 years without severe cardiac involvement and who had not received correct treatment previously were treated with benznidazole 5 mg/kg/day for 60 days. Patients were followed-up with serology and PCR 1 month after treatment ended and every 6 months thereafter. A total of 1146 Latin Americans were screened for T. cruzi (357 positive serology results). The typical patient profile was a Bolivian female, of rural origin, in her fourth decade of life, without evidence of visceral involvement. Treatment tolerance was poor, with 29.7% discontinuing treatment due to adverse reactions. Among those with adverse reactions (52%), the most frequent were cutaneous hypersensitivity (68.7%), gastrointestinal upset (20%) and nervous system disturbances (16.2%). T. cruzi infection is no longer limited to Latin America. Poor treatment tolerance can limit current treatment options. More epidemiological data are necessary to estimate the magnitude of a problem of great relevance for public health and health resource planning

Wheat Straw Open Burning: Emissions and Impact on Climate Change

The state of Baja California, Mexico, is the second national wheat producer. Mexicali, the capital of Baja California, is the primary wheat producer, and it represents the most significant crop in the valley, with 90,609 ha of a cultivated surface by 2015; it leads to a wheat production of 585,334 t and a generation of 661,446 t of wheat straw as agricultural residue. The 15% of this waste has various uses. The 85% of wheat straw is open burnt in situ to prepare the farmland for the next agricultural cycle. Through the development of an emissions and energy model on iThink®, the emissions of 6,185 t of PM, 35,983 t of CO, and 1,125 t of CH4 considering a headfire burning or 3,373 t of PM, 30,360 t of CO, and 731 t of CH4 by backfire burning were estimated. Also, the wheat straw wasted energy was estimated at 8.15 PJ by 2015, with a lower heating value of 14.50 MJ/kg determined experimentally. The results highlight that for each hectare of harvested wheat, 6.205 t of wheat straw are generated and burnt. It represents the emission of pollutants and 89,972.50 MJ of wasted energy

Repercution of Lactobacillus acidophilus and Kluyveromyces fragilis (L-4UCLV) on the bioproductive parameters of pigs

With the objective of evaluating the repercussion of Lactobacillus acidophilus and Kluyveromyces fragilis L-4UCLV on the productive indicators and the health of the pigs in the different productive stages, two experiments (I and II) were carried out. 14 and 42 hybrid pigs CC21 (Yorkshire - Landrace / L35 Duroc) were used, respectively, distributed in two and three groups of 7 and 14 animals each. Control (T1); prepared-A (T2) and prepared-B (T3). T2, contained L. acidophillus and T3, L. acidophilus more K. fragilis (L-4 UCLV). A completely randomized design was used to evaluate weight gain (WG), average daily gain (ADG), feed conversion (FC), food efficiency (FE), incidence of diarrhea and mortality. WG, ADG, FC and FE were higher (