Application of the Empirical Mode Decomposition On the Characterization and Forecasting of the Arrival Data of an Enterprise Cluster

Characterization and forecasting are two important processes in capacity planning. While they are closely related, their approaches have been different. In this research, a decomposition method called Empirical Mode Decomposition (EMD) has been applied as a preprocessing tool in order to bridge the input of both characterization and forecasting processes of the job arrivals of an enterprise cluster. Based on the facts that an enterprise cluster follows a standard preset working schedule and that EMD has the capability to extract hidden patterns within a data stream, we have developed a set of procedures that can preprocess the data for characterization as well as for forecasting. This comprehensive empirical study demonstrates that the addition of the preprocessing step is an improvement over the standard approaches in both characterization and forecasting. In addition, it is also shown that EMD is better than the popular wavelet-based decomposition in term of extracting different patterns from within a data stream

Machine learning for the subsurface characterization at core, well, and reservoir scales

The development of machine learning techniques and the digitization of the subsurface geophysical/petrophysical measurements provides a new opportunity for the industries focusing on exploration and extraction of subsurface earth resources, such as oil, gas, coal, geothermal energy, mining, and sequestration. With more data and more computation power, the traditional methods for subsurface characterization and engineering that are adopted by these industries can be automized and improved. New phenomenon can be discovered, and new understandings may be acquired from the analysis of big data. The studies conducted in this dissertation explore the possibility of applying machine learning to improve the characterization of geological materials and geomaterials. Accurate characterization of subsurface hydrocarbon reservoirs is essential for economical oil and gas reservoir development. The characterization of reservoir formation requires the integration interpretation of data from different sources. Large-scale seismic measurements, intermediate-scale well logging measurements, and small-scale core sample measurements help engineers understand the characteristics of the hydrocarbon reservoirs. Seismic data acquisition is expensive and core samples are sparse and have limited volume. Consequently, well log acquisition provides essential information that improves seismic analysis and core analysis. However, the well logging data may be missing due to financial or operational challenges or may be contaminated due to complex downhole environment. At the near-wellbore scale, I solve the data constraint problem in the reservoir characterization by applying machine learning models to generate synthetic sonic traveltime and NMR logs that are crucial for geomechanical and pore-scale characterization, respectively. At the core scale, I solve the problems in fracture characterization by processing the multipoint sonic wave propagation measurements using machine learning to characterize the dispersion, orientation, and distribution of cracks embedded in material. At reservoir scale, I utilize reinforcement learning models to achieve automatic history matching by using a fast-marching-based reservoir simulator to estimate reservoir permeability that controls pressure transient response of the well. The application of machine learning provides new insights into traditional subsurface characterization techniques. First, by applying shallow and deep machine learning models, sonic logs and NMR T2 logs can be acquired from other easy-to-acquire well logs with high accuracy. Second, the development of the sonic wave propagation simulator enables the characterization of crack-bearing materials with the simple wavefront arrival times. Third, the combination of reinforcement learning algorithms and encapsulated reservoir simulation provides a possible solution for automatic history matching

Rise of the Planet of Serverless Computing: A Systematic Review

Serverless computing is an emerging cloud computing paradigm, being adopted to develop a wide range of software applications. It allows developers to focus on the application logic in the granularity of function, thereby freeing developers from tedious and error-prone infrastructure management. Meanwhile, its unique characteristic poses new challenges to the development and deployment of serverless-based applications. To tackle these challenges, enormous research efforts have been devoted. This paper provides a comprehensive literature review to characterize the current research state of serverless computing. Specifically, this paper covers 164 papers on 17 research directions of serverless computing, including performance optimization, programming framework, application migration, multi-cloud development, testing and debugging, etc. It also derives research trends, focus, and commonly-used platforms for serverless computing, as well as promising research opportunities

Development of an acoustic measurement system of the Modulus of Elasticity in trees, logs and boards

The objective of this Bachelor’s Thesis is to develop a portable electronic device capable of quantifying the stiffness of the wood of standing trees, logs and boards using non-destructive testing (NDT) by means of acoustic wave analysis. As an indicator of stiffness, the Modulus of Elasticity (MOE) is used, a standard figure in the industry. This way, wood from forestry can be characterized and classified for different purposes. This Thesis is part of LIFE Wood For Future, a project of the University of Granada (UGR) financed by the European Union’s LIFE programme. LIFE Wood For Future aims to recover the cultivation of poplar (populus sp.) in the Vega de Granada, by proving the quality of its wood through innovative structural bioproducts. Recovering the poplar groves of Granada would have great benefits for the Metropolitan Area: creation of local and sustainable jobs, improvement of biodiversity, and increase in the absorption of carbon dioxide in the long term, helping to reduce the endemic air pollution of Granada. This Final Degree Project has been developed in collaboration with the ADIME research group of the Higher Technical School of Building Engineering (ETSIE) and the aerospace electronics group GranaSat of the UGR. The goal of the developed device, named Tree Inspection Kit (or TIK), is to be an innovative, portable and easy-to-use tool for non-destructive diagnosis and classification of wood by measuring its MOE. TIK is equipped with the necessary electronics to quantify the Time of Flight (ToF) of an acoustic wave that propagates inside a piece of wood. In order to do this, two piezoelectric probes are used, nailed in the wood and separated a given distance longitudinally. The MOE can be derived from the propagation speed of the longitudinal acoustic wave if the density of the is known. For this reason, this device has the possibility of connecting a load cell for weighing logs or boards to estimate their density. It also has an expansion port reserved for future functionality. A methodology based on the Engineering Design Process (EDP) has been followed. The scope of this project embraces all aspects of the development of an electronic product from start to finish: conceptualization, specification of requirements, design, manufacture and verification. A project of this reach requires planning, advanced knowledge of signal analysis, electronics, design and manufacture of Printed Circuit Boards (PCB) and product design, as well as the development of a firmware for the embedded system, based on a RTOS. Prior to the design of the electronics, a Reverse Engineering process of some similar products of the competition is performed; as well as an exhaustive analysis of the signals coming from the piezoelectric sensors that are going to be used, and the frequency response characterization of the piezoelectric probes themselves. This project has as its ultimate goal the demonstration of the multidisciplinary knowledge of engineering, and the capacity of analysis, design and manufacturing by the author; his skill and professionalism in CAD and EDA software required for these tasks, as well as in the documentation of the entire process.El presente Trabajo de Fin de Grado tiene como objetivo el desarrollo de un dispositivo electrónico portátil capaz de cuantificar la rigidez de la madera de árboles en pie, trozas y tablas usando ensayos no destructivos (Non-Destructive Testing, NDT) por medio del análisis de ondas acústicas. Como indicador de la rigidez se usa el Módulo de Elasticidad (MOE), una figura estándar en la industria. Este TFG forma parte de LIFE Wood For Future, un proyecto de la Universidad de Granada (UGR) financiado por el programa LIFE de la Unión Europea. LIFEWood For Future tiene como objetivo recuperar el cultivo del chopo (populus sp.) en la Vega de Granada demostrando la viabilidad de su madera a través de bioproductos estructurales innovadores. Recuperar las choperas de Granada tendría grandes beneficios para la zona del Área Metropolitana: creación de puestos de trabajo locales y sostenibles, mejora de la biodiversidad, e incremento de la tasa de absorción de dióxido de carbono a largo plazo, contribuyendo a reducir la contaminación endémica del aire en Granada. Este Trabajo de Fin de Grado se ha desarrollado con la colaboración del grupo de investigación ADIME de la Escuela Técnica Superior de Ingeniería de Edificación (ETSIE) y el grupo de electrónica aeroespacial GranaSat de la UGR. El objetivo del dispositivo, denominado Tree Inspection Kit (TIK), es ser una herramienta innovadora, portátil y fácil de usar para el diagnóstico y clasificación no destructiva de la madera por medio de su MOE. TIK está dotado de la electrónica necesaria para medir el tiempo de tránsito (ToF) de una onda acústica que se propaga en el interior de una pieza de madera. Para ello, se utilizan dos sondas piezoeléctricas clavadas en la madera y separadas longitudinalmente una distancia conocida. De la velocidad de propagación de la onda longitudinal se puede derivar el MOE, previo conocimiento de la densidad del material. Por ello, este dispositivo cuenta con la posibilidad de conectarle una célula de carga y pesar trozas o tablas para estimar su densidad. También tiene un puerto de expansión reservado para funcionalidad futura. Se ha seguido una metodología basada en el Proceso de Diseño de Ingeniería (Engineering Design Process, EDP), abarcando todos los aspectos del desarrollo de un producto electrónico de principio a fin: conceptualización, especificación de requisitos, diseño, fabricación y verificación. Un proyecto de este alcance requiere de planificación, conocimientos avanzados de análisis de señales, de electrónica, de diseño y fabricación de Placas de Circuito Impreso (PCB) y de diseño de producto, así como el desarrollo de un firmware para el sistema empotrado, basado en un RTOS. Previo al diseño de la electrónica, se realiza un proceso de Ingeniería Inversa (Reverse Engineering) de algunos productos similares de la competencia; al igual que un exhaustivo análisis de las señales provenientes de los sensores piezoeléctricos que van a utilizarse y la caracterización en frecuencia de las propias sondas piezoeléctricas. Este proyecto tiene como fin último la demostración de los conocimientos multidisciplinares propios de la ingeniería y la capacidad de análisis, diseño y fabricación por parte del autor; su habilidad y profesionalidad en el software CAD y EDA requerido para estas tareas, así como en la documentación de todo el proceso.Unión Europe

Workload characterization, modeling, and prediction in grid Computing

Workloads play an important role in experimental performance studies of computer systems. This thesis presents a comprehensive characterization of real workloads on production clusters and Grids. A variety of correlation structures and rich scaling behavior are identified in workload attributes such as job arrivals and run times, including pseudo-periodicity, long range dependence, and strong temporal locality. Based on the analytic results workload models are developed to fit the real data. For job arrivals three different kinds of autocorrelations are investigated. For short to middle range dependent data, Markov modulated Poisson processes (MMPP) are good models because they can capture correlations between interarrival times while remaining analytically tractable. For long range dependent and multifractal processes, the multifractal wavelet model (MWM) is able to reconstruct the scaling behavior and it provides a coherent wavelet framework for analysis and synthesis. Pseudo-periodicity is a special kind of autocorrelation and it can be modeled by a matching pursuit approach. For workload attributes such as run time a new model is proposed that can fit not only the marginal distribution but also the second order statistics such as the autocorrelation function (ACF). The development of workload models enable the simulation studies of Grid scheduling strategies. By using the synthetic traces, the performance impacts of workload correlations in Grid scheduling is quantitatively evaluated. The results indicate that autocorrelations in workload attributes can cause performance degradation, in some situations the difference can be up to several orders of magnitude. The larger the autocorrelation, the worse the performance, it is proved both at the cluster and Grid level. This study shows the importance of realistic workload models in performance evaluation studies. Regarding performance predictions, this thesis treats the targeted resources as a ``black box'' and takes a statistical approach. It is shown that statistical learning based methods, after a well-thought and fine-tuned design, are able to deliver good accuracy and performance.UBL - phd migration 201

Data-Driven Methods for Data Center Operations Support

During the last decade, cloud technologies have been evolving at an impressive pace, such that we are now living in a cloud-native era where developers can leverage on an unprecedented landscape of (possibly managed) services for orchestration, compute, storage, load-balancing, monitoring, etc. The possibility to have on-demand access to a diverse set of configurable virtualized resources allows for building more elastic, flexible and highly-resilient distributed applications. Behind the scenes, cloud providers sustain the heavy burden of maintaining the underlying infrastructures, consisting in large-scale distributed systems, partitioned and replicated among many geographically dislocated data centers to guarantee scalability, robustness to failures, high availability and low latency. The larger the scale, the more cloud providers have to deal with complex interactions among the various components, such that monitoring, diagnosing and troubleshooting issues become incredibly daunting tasks. To keep up with these challenges, development and operations practices have undergone significant transformations, especially in terms of improving the automations that make releasing new software, and responding to unforeseen issues, faster and sustainable at scale. The resulting paradigm is nowadays referred to as DevOps. However, while such automations can be very sophisticated, traditional DevOps practices fundamentally rely on reactive mechanisms, that typically require careful manual tuning and supervision from human experts. To minimize the risk of outages—and the related costs—it is crucial to provide DevOps teams with suitable tools that can enable a proactive approach to data center operations. This work presents a comprehensive data-driven framework to address the most relevant problems that can be experienced in large-scale distributed cloud infrastructures. These environments are indeed characterized by a very large availability of diverse data, collected at each level of the stack, such as: time-series (e.g., physical host measurements, virtual machine or container metrics, networking components logs, application KPIs); graphs (e.g., network topologies, fault graphs reporting dependencies among hardware and software components, performance issues propagation networks); and text (e.g., source code, system logs, version control system history, code review feedbacks). Such data are also typically updated with relatively high frequency, and subject to distribution drifts caused by continuous configuration changes to the underlying infrastructure. In such a highly dynamic scenario, traditional model-driven approaches alone may be inadequate at capturing the complexity of the interactions among system components. DevOps teams would certainly benefit from having robust data-driven methods to support their decisions based on historical information. For instance, effective anomaly detection capabilities may also help in conducting more precise and efficient root-cause analysis. Also, leveraging on accurate forecasting and intelligent control strategies would improve resource management. Given their ability to deal with high-dimensional, complex data, Deep Learning-based methods are the most straightforward option for the realization of the aforementioned support tools. On the other hand, because of their complexity, this kind of models often requires huge processing power, and suitable hardware, to be operated effectively at scale. These aspects must be carefully addressed when applying such methods in the context of data center operations. Automated operations approaches must be dependable and cost-efficient, not to degrade the services they are built to improve. i

New decision support tools for forest tactical and operational planning

Doutoramento em Engenharia Florestal e dos Recursos Florestais - Instituto Superior de AgronomiaThe economic importance of the forest resources and the Portuguese forest-based industries motivated several studies over the last 15 years, particularly on strategic forest planning. This thesis focuses on the forest planning processes at tactical and operational level (FTOP). These problems relate to harvesting, transportation, storing, and delivering the forest products to the mills. Innovative Operation Research methods and Decision Support Systems (DSS) were developed to address some of these problems that are prevalent in Portugal. Specifically, Study I integrates harvest scheduling, pulpwood assortment, and assignment decisions at tactical level. The solution method was based in problem decomposition, combining heuristics and mathematical programming algorithms. Study II presents a solution approach based on Revenue Management principles for the reception of Raw Materials. This operational problem avoids truck congestion during the operation of pulpwood delivery. Study III uses Enterprise Architecture to design a DSS for integrating the operations performed over the pulpwood supply chain. Study IV tests this approach on a toolbox that handled the complexity of the interactions among the agents engaged on forest planning at regional level. Study V proposes an innovative technological framework that combines forest planning with forest operations' control