Discovering ship navigation patterns towards environmental impact modeling

In this work a data pipe-line to manage and extract patterns from time-series is described. The patterns found with a combination of Conditional Restricted Boltzmann Machine (CRBM) and k-Means algorithms are then validated using a visualization tool. The motivation of finding these patterns is to leverage future emission model

Sequence-to-sequence models for workload interference prediction on batch processing datacenters

Co-scheduling of jobs in data centers is a challenging scenario where jobs can compete for resources, leading to severe slowdowns or failed executions. Efficient job placement on environments where resources are shared requires awareness on how jobs interfere during execution, to go far beyond ineffective resource overbooking techniques. Current techniques, most of which already involve machine learning and job modeling, are based on workload behavior summarization over time, rather than focusing on effective job requirements at each instant of the execution. In this work, we propose a methodology for modeling co-scheduling of jobs on data centers, based on their behavior towards resources and execution time and using sequence-to-sequence models based on recurrent neural networks. The goal is to forecast co-executed jobs footprint on resources throughout their execution time, from the profile shown by the individual jobs, in order to enhance resource manager and scheduler placement decisions. The methods presented herein are validated by using High Performance Computing benchmarks based on different frameworks (such as Hadoop and Spark) and applications (CPU bound, IO bound, machine learning, SQL queries...). Experiments show that the model can correctly identify the resource usage trends from previously seen and even unseen co-scheduled jobs.This work is supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 639595); [Generalitat] de Catalunya under contract 2014SGR1051; the ICREA Academia program; and the BSC-CNS Severo Ochoa program (SEV-2015-0493); the Spanish Ministry of Economy under contract TIN2015-65316-P and the Generalitat.Peer ReviewedPostprint (author's final draft

Theta-Scan: Leveraging behavior-driven forecasting for vertical auto-scaling in container cloud

Detection of behavior patterns on resource usage in containerized Cloud applications is necessary for proper resource provisioning. Applications can use CPU/Memory with repetitive patterns, following a trend over time independently. By identifying such patterns, resource forecasting models can be fit better, reducing over/under-provisioning via fewer resizing operations. Here we present ThetaScan, a time-series analysis method for vertical auto-scaling of containers in the Cloud, based on the detection of stationarity/trending and periodicity on resource consumption. Our method leverages the Theta Forecaster algorithm with deseasonalization that, in our provisioning scenario, only requires the estimated periodicity for resource consumption as principal hyper-parameter. Commonly used behavior detection methods require manual hyper-parameter tuning, making them infeasible for automation. Besides, it can be used at multi-scales (minute/hour/day), detecting hourly and daily patterns to improve resource usage prediction. Experiments show that we can detect behaviors in resource consumption that common methods miss, without requiring extensive manual tuning. We can reduce the resizing triggers compared to fixed-size scheduling around ~ 10% – 15%, reduce over-provisioning of CPU and Memory through periodic-based provisioning. Also a ~ 60% on multiscale resource forecasting for traces showing periodicity at different levels in respect to single-scale.This work has been partially supported by the Spanish Government (contract PID2019-107255GB) and by Generalitat de Catalunya (contract 2014-SGR-1051).Peer ReviewedPostprint (author's final draft

A highly parameterizable framework for Conditional Restricted Boltzmann Machine based workloads accelerated with FPGAs and OpenCL

© 2020 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Conditional Restricted Boltzmann Machine (CRBM) is a promising candidate for a multidimensional system modeling that can learn a probability distribution over a set of data. It is a specific type of an artificial neural network with one input (visible) and one output (hidden) layer. Recently published works demonstrate that CRBM is a suitable mechanism for modeling multidimensional time series such as human motion, workload characterization, city traffic analysis. The process of learning and inference of these systems relies on linear algebra functions like matrix–matrix multiplication, and for higher data sets, they are very compute-intensive. In this paper, we present a configurable framework for CRBM based workloads for arbitrary large models. We show how to accelerate the learning process of CRBM with FPGAs and OpenCL, and we conduct an extensive scalability study for different model sizes and system configurations. We show significant improvement in performance/Watt for large models and batch sizes (from 1.51x up to 5.71x depending on the host configuration) when we use FPGA and OpenCL for the acceleration, and limited benefits for small models comparing to the state-of-the-art CPU solution.This work was supported by the European Research Council(ERC) under the European Union’s Horizon 2020 research andinnovation programme (grant agreements No 639595); the Min-istry of Economy of Spain under contract TIN2015-65316-P andGeneralitat de Catalunya, Spain under contract 2014SGR1051;the ICREA, Spain Academia program; the BSC-CNS Severo Ochoaprogram, Spain (SEV-2015-0493) and Intel Corporation, UnitedStatesPeer ReviewedPostprint (published version

Automatic generation of workload profiles using unsupervised learning pipelines

The complexity of resource usage and power consumption on cloud-based applications makes the understanding of application behavior through expert examination difficult. The difficulty increases when applications are seen as “black boxes”, where only external monitoring can be retrieved. Furthermore, given the different amount of scenarios and applications, automation is required. Here we examine and model application behavior by finding behavior phases. We use Conditional Restricted Boltzmann Machines (CRBM) to model time-series containing resources traces measurements like CPU, Memory and IO. CRBMs can be used to map a given given historic window of trace behaviour into a single vector. This low dimensional and time-aware vector can be passed through clustering methods, from simplistic ones like k-means to more complex ones like those based on Hidden Markov Models (HMM). We use these methods to find phases of similar behaviour in the workloads. Our experimental evaluation shows that the proposed method is able to identify different phases of resource consumption across different workloads. We show that the distinct phases contain specific resource patterns that distinguish them.Peer ReviewedPostprint (published version

La autonomía de las mujeres en escenarios económicos cambiantes

Autonomía de las mujeres en escenarios económicos cambiantes, recoge contribuciones de ministras y autoridades de los mecanismos para el adelanto de las mujeres de América Latina y el Caribe, llevadas a cabo en la 58a reunión de la Mesa Directiva de la Conferencia Regional sobre la Mujer de América Latina y el Caribe en enero de 2019, así como en las reuniones preparatorias de la XIV Conferencia Regional sobre la Mujer de América Latina y el Caribe, en los debates e intercambios en el marco del Diálogo de Especialistas: Los Desafíos de la Economía desde una Perspectiva de Género, organizado por la CEPAL y el Programa de las Naciones Unidas para el Desarrollo (PNUD) en junio de 2019. De igual forma, recoge la información presentada en los informes nacionales elaborados por los países para el proceso de examen exhaustivo a 25 años de la aprobación de la Declaración y Plataforma de Beijing y de los informes nacionales sobre el avance en la aplicación de la Estrategia de Montevideo para la Implementación de la Agenda Regional de Género en el Marco del Desarrollo Sostenible hacia 2030. La primera parte del documento, evalúa los avances realizados para lograr mayores niveles de igualdad de género y autonomía de las mujeres, donde afirman, el progreso significativo de algunos países en la construcción y jerarquización de estructuras institucionales para el diseño y la coordinación de políticas de igualdad de género, lo cual ha conducido a la consolidación de un marco normativo sólido para hacer frente a la violencia y para conseguir una participación más igualitaria de las mujeres en los procesos de toma de decisiones. En la segunda parte, se analizan los procesos de globalización económica y financiera, la revolución digital, la economía del cuidado y el cambio climático, con sus principales efectos en la vida de las mujeres en un contexto económico cambiante. Por medio, de la implementación de políticas públicas adecuadas, lo cual representa una oportunidad para transitar hacia un nuevo estilo de desarrollo que ponga la igualdad de género en el centro

A deletion at Adamts9-magi1 Locus is associated with psoriatic arthritis risk

Objective: Copy number variants (CNVs) have been associated with the risk to develop multiple autoimmune diseases. Our objective was to identify CNVs associated with the risk to develop psoriatic arthritis (PsA) using a genome-wide analysis approach. Methods: A total of 835 patients with PsA and 1498 healthy controls were genotyped for CNVs using the Illumina HumanHap610 BeadChip genotyping platform. Genomic CNVs were characterised using CNstream analysis software and analysed for association using the χ2 test. The most significant genomic CNV associations with PsA risk were independently tested in a validation sample of 1133 patients with PsA and 1831 healthy controls. In order to test for the specificity of the variants with PsA aetiology, we also analysed the association to a cohort of 822 patients with purely cutaneous psoriasis (PsC). Results: A total of 165 common CNVs were identified in the genome-wide analysis. We found a highly significant association of an intergenic deletion between ADAMTS9 and MAGI1 genes on chromosome 3p14.1 (p=0.00014). Using the independent patient and control cohort, we validated the association between ADAMTS9-MAGI1 deletion and PsA risk (p=0.032). Using next-generation sequencing, we characterised the 26 kb associated deletion. Finally, analysing the PsC cohort we found a lower frequency of the deletion compared with the PsA cohort (p=0.0088) and a similar frequency to that of healthy controls (p>0.3). Conclusions: The present genome-wide scan for CNVs associated with PsA risk has identified a new deletion associated with disease risk and which is also differential from PsC risk

Interacción entre clima y ocupación humana en la configuración del paisaje vegetal del Parque Nacional de Aigüestortes i Estany de Sant Maurici a lo largo de los últimos 15.000 años

The vegetation of the National Park of Aigüestortes i Estany de St Maurici is the result of an interaction between climate, plant community dynamics and the human occupation of the territory. The OCUPAproject aimed to reconstruct this interaction across the last millennia combining methods from palaeoecology and archaeology. The study focused primarily on the Sant Nicolau valley and built on the multidisciplinary analysis of the sedimentary archive of two lakes (Llebreta and Redó) and a number of archaeological sites located in shelters and outdoors. There is archaeological evidence of human presencesince 9000 yr cal BP, and a continuous record since 7500 yr cal BP. At early stages, humans transformed the surroundings of the shelters occupied and lithic tools indicate contacts with locations far away (i.e.,the Ebro plains). Since more than 3000 years ago, there has been human impact on the vegetation withoutinterruption until present. Initially, the impacts were mostly related to livestock: use of fire to open grazing lands, soil erosion and, during the medieval period, forestry and eutrophication of lakes. The agriculture impact in the lower part of the valley (e.g., Llebreta) occurred about 2100 yr ago, although some cereal grains and tools for harvesting have been found for the Neolithic. In the medieval period, the impact was higher than during the last centuries. In general, the changes in the human land use approximately follow the major changes in climate, but the specific causal link is likely related to the social and cultural dynamics of a broader territory since the Neolithic

Herramienta para el control de los pagos a particulares y otras formas de gestión no estatal en la UP Poder Popular Provincial Cienfuegos

Los cambios que se han venido realizando para el reordenamiento de la economía cubana y su adecuación a las condiciones actuales de la economía mundial han incrementado la actividad de nuevas formas de gestión y su consecuente interrelación con las entidades estatales. Con el incremento de la utilización por las empresas y unidades presupuestadas de las producciones y los servicios que brindan estos actores económicos se ha hecho necesario establecer mayor control sobre las producciones y servicios que se reciben de los mismos ya que constituye un interés especial del país conocer cómo se desarrolla esta interrelación, así como su tendencia

Proactive container auto-scaling for cloud native machine learning services

Understanding the resource usage behaviors of the ever-increasing machine learning workloads are critical to cloud providers offering Machine Learning (ML) services. Capable of auto-scaling resources for customer workloads can significantly improve resource utilization, thus greatly reducing the cost. Here we leverage the AI4DL framework [1] to characterize workload and discover resource consumption phases. We advance the existing technology to an incremental phase discovery method that applies to more general types of ML workload for both training and inference. We use a time-window MultiLayer Perceptron (MLP) to predict phases in containers with different types of workload. Then, we propose a predictive vertical auto-scaling policy to resize the container dynamically according to phase predictions. We evaluate our predictive auto-scaling policies on 561 long-running containers with multiple types of ML workloads. The predictive policy can reduce up to 38% of allocated CPU compared to the default resource provisioning policies by developers. By comparing our predictive policies with commonly used reactive auto-scaling policies, we find that they can accurately predict sudden phase transitions (with an F1-score of 0.92) and significantly reduce the number of out-of-memory errors (350 vs. 20). Besides, we show that the predictive auto-scaling policy maintains the number of resizing operations close to the best reactive policies.Peer ReviewedPostprint (author's final draft