176 research outputs found

    Internet of Things and Neural Network Based Energy Optimization and Predictive Maintenance Techniques in Heterogeneous Data Centers

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    Rapid growth of cloud-based systems is accelerating growth of data centers. Private and public cloud service providers are increasingly deploying data centers all around the world. The need for edge locations by cloud computing providers has created large demand for leasing space and power from midsize data centers in smaller cities. Midsize data centers are typically modular and heterogeneous demanding 100% availability along with high service level agreements. Data centers are recognized as an increasingly troublesome percentage of electricity consumption. Growing energy costs and environmental responsibility have placed the data center industry, particularly midsize data centers under increasing pressure to improve its operational efficiency. The power consumption is mainly due to servers and networking devices on computing side and cooling systems on the facility side. The facility side systems have complex interactions with each other. The static control logic and high number of configuration and nonlinear interdependency create challenges in understanding and optimizing energy efficiency. Doing analytical or experimental approach to determine optimum configuration is very challenging however, a learning based approach has proven to be effective for optimizing complex operations. Machine learning methodologies have proven to be effective for optimizing complex systems. In this thesis, we utilize a learning engine that learns from operationally collected data to accurately predict Power Usage Effectiveness (PUE) and creation of intelligent method to validate and test results. We explore new techniques on how to design and implement Internet of Things (IoT) platform to collect, store and analyze data. First, we study using machine learning framework to predictively detect issues in facility side systems in a modular midsize data center. We propose ways to recognize gaps between optimal values and operational values to identify potential issues. Second, we study using machine learning techniques to optimize power usage in facility side systems in a modular midsize data center. We have experimented with neural network controllers to further optimize the data suite cooling system energy consumption in real time. We designed, implemented, and deployed an Internet of Things framework to collect relevant information from facility side infrastructure. We designed flexible configuration controllers to connect all facility side infrastructure within data center ecosystem. We addressed resiliency by creating reductant controls network and mission critical alerting via edge device. The data collected was also used to enhance service processes that improved operational service level metrics. We observed high impact on service metrics with faster response time (increased 77%) and first time resolution went up by 32%. Further, our experimental results show that we can predictively identify issues in the cooling systems. And, the anomalies in the systems can be identified 30 days to 60 days ahead. We also see the potential to optimize power usage efficiency in the range of 3% to 6%. In the future, more samples of issues and corrective actions can be analyzed to create practical implementation of neural network based controller for real-time optimization.Ph.D.Information Systems Engineering, College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/136074/1/Final Dissertation Vishal Singh.pdfDescription of Final Dissertation Vishal Singh.pdf : Dissertatio

    The Children's Respiratory and Environmental Workgroup (CREW) birth cohort consortium: design, methods, and study population

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    Background: Single birth cohort studies have been the basis for many discoveries about early life risk factors for childhood asthma but are limited in scope by sample size and characteristics of the local environment and population. The Children’s Respiratory and Environmental Workgroup (CREW) was established to integrate multiple established asthma birth cohorts and to investigate asthma phenotypes and associated causal pathways (endotypes), focusing on how they are influenced by interactions between genetics, lifestyle, and environmental exposures during the prenatal period and early childhood. Methods and results: CREW is funded by the NIH Environmental influences on Child Health Outcomes (ECHO) program, and consists of 12 individual cohorts and three additional scientific centers. The CREW study population is diverse in terms of race, ethnicity, geographical distribution, and year of recruitment. We hypothesize that there are phenotypes in childhood asthma that differ based on clinical characteristics and underlying molecular mechanisms. Furthermore, we propose that asthma endotypes and their defining biomarkers can be identified based on personal and early life environmental risk factors. CREW has three phases: 1) to pool and harmonize existing data from each cohort, 2) to collect new data using standardized procedures, and 3) to enroll new families during the prenatal period to supplement and enrich extant data and enable unified systems approaches for identifying asthma phenotypes and endotypes. Conclusions: The overall goal of CREW program is to develop a better understanding of how early life environmental exposures and host factors interact to promote the development of specific asthma endotypes.HHS/NIH [5UG3OD023282]; Columbia University [P01ES09600, R01 ES008977, P30ES09089, R01 ES013163, R827027]; Tucson Children's Respiratory Study (TCRS) [NHLBI 132523]; Infant Immune Study (IIS) [HL-56177]; Childhood Origins of Asthma Study (COAST) [P01 HL070831, U10 HL064305, R01 HL061879]; Wayne County Health, Environment, Allergy and Asthma Longitudinal Study (WHEALS) [R01 AI050681, R56 AI050681, R01 AI061774, R21 AI059415, K01 AI070606, R21 AI069271, R01 HL113010, R21 ES022321, P01 AI089473, R21 AI080066, R01 AI110450, R01 HD082147]; Fund for Henry Ford Health System; Childhood Allergy Study (CAS) [R01 AI024156, R03 HL067427, R01 AI051598]; Blue Cross Foundation Johnson; Fund for Henry Ford Hospital; Microbes, Allergy, Asthma and Pets (MAAP) [P01 AI089473]; Infant Susceptibility to Pulmonary Infections and Asthma following RSV Exposure (INSPIRE) [NIH/NIAID U19 AI 095227, NIH/NCATS UL1 TR 002243, NIH/NIAID K24 AI 077930, NIH/NHLBI R21 HD 087864, NIH/NHLBI X01 HL 134583]; Wisconsin Infant Study Cohort (WISC) [U19 AI104317, NCATS UL1TR000427]; Upper Midwest Agricultural Safety and Health Center (UMASH) [U54 OH010170]; RTI International, Research Triangle Park, North Carolina, USA; NIH [U24OD023382]; Urban Environment and Childhood Asthma Study (URECA) [NO1-AI-25482, HHSN272200900052C, HHSN272201000052I, NCRR/NIH RR00052, M01RR00533, 1UL1RR025771, M01RR00071, 1UL1RR024156, UL1TR001079, 5UL1RR024992-02, NCATS/NIH UL1TR000040]; Cincinnati Childhood Allergy and Air Pollution Study (CCAAPS) [R01 ES11170, R01 ES019890]; Epidemiology of Home Allergens and Asthma Study (EHAAS) [R01 AI035786]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

    The Value of Rents: Global Commodity Chains and Small Cocoa Producers in Ecuador

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    Drawing on the Marxian theory of ground rent this paper develops an analysis of “global commodity chains” (GCC) with agrarian roots. There is an acknowledgement that the concentrated downstream governance of primary commodity based GCC has created a set of ‘asymmetrical’ power relations which blocks the transmission of value upstream towards small producers. This paper argues that this research under-specifies what is meant by value and rent and in doing so marginalises the analysis of value production before its journey through inter-firm relations. We demonstrate the importance of theorising the value constitution of commodities produced on the land and the forces that contest the payment of ground rent and thereby shape the geography of GCC. Based on empirical research conducted around Ecuador’s ‘post-neoliberal’ cocoa re-activation plan, we identify the class politics and production mechanisms through which value and rent escapes the hands of a stratified network of small owner producers

    Real-Time Sensor Networks and Systems for the Industrial IoT

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    The Industrial Internet of Things (Industrial IoT—IIoT) has emerged as the core construct behind the various cyber-physical systems constituting a principal dimension of the fourth Industrial Revolution. While initially born as the concept behind specific industrial applications of generic IoT technologies, for the optimization of operational efficiency in automation and control, it quickly enabled the achievement of the total convergence of Operational (OT) and Information Technologies (IT). The IIoT has now surpassed the traditional borders of automation and control functions in the process and manufacturing industry, shifting towards a wider domain of functions and industries, embraced under the dominant global initiatives and architectural frameworks of Industry 4.0 (or Industrie 4.0) in Germany, Industrial Internet in the US, Society 5.0 in Japan, and Made-in-China 2025 in China. As real-time embedded systems are quickly achieving ubiquity in everyday life and in industrial environments, and many processes already depend on real-time cyber-physical systems and embedded sensors, the integration of IoT with cognitive computing and real-time data exchange is essential for real-time analytics and realization of digital twins in smart environments and services under the various frameworks’ provisions. In this context, real-time sensor networks and systems for the Industrial IoT encompass multiple technologies and raise significant design, optimization, integration and exploitation challenges. The ten articles in this Special Issue describe advances in real-time sensor networks and systems that are significant enablers of the Industrial IoT paradigm. In the relevant landscape, the domain of wireless networking technologies is centrally positioned, as expected

    Data Quality Management in ETL Process under Resource Constraints

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    Currently, access to data is necessary for many companies, particularly those engaged in marketing, to make decisions that will improve the quality of their services and businesses. They frequently find the knowledge they need from several sources in a variety of formats. Following a dedication to the quality of information offered to data consumers, a system will be implemented to consolidate all these data sources for analysis and decision-making. This study addresses the evaluation of data quality (DQ) in an ETL process developed to support a marketing data management platform. More specifically, this study addressed the problem of evaluating the quality of data with a high-volume trait. Addressing the problem of DQ assessment at high ingestion rates is beyond the scope of this study, which focuses on data quality assessment with limitations to vertical or horizontal scaling of the ingestion system. We also analyze the use of the model developed on real data to assume an improvement in the quality of the data in the ETL. The methodology used consisted of studying each feature related to the characterization of high-quality data and analyzing the impact of those in an ETL concerned with voluminous data. We propose algorithms for improving a more generalizable integration DQ assessment. We conducted a practical implementation study of the different criteria and characteristics proposed to evaluate the impact of the data collected throughout the process of data Extraction, Transformation, and Loading. We highlight a quality assessment framework that models the different necessary parts of the process, including data sources, metrics characterizing data quality, data destination, and the analysis and performance of the algorithms used in the assessment process. The ETL practical implementation in this research is based on a Direct Acyclic Graph (DAG) model, with the main purpose of extracting, transforming, and transmitting data from this first service to the rest of the Marketing Data Management Platform infrastructure, which is considered as the end user. The evaluation and quality are based on the development of algorithms that take source data as input in combination with predefined properties encompassing the expected result of the ETL transformation to produce the evaluation result. The evaluation findings may be used to support or contradict the standards for quality. Decisions are made in the event of a DQ failure to improve and enhance the data. We suggest including data checks at the very end of the ETL data manipulation process as well as a model for data volume reduction using algorithms that are intended to make the procedure more generic to enable quick review. The quality of the data evaluated during the test is a statistical representation of the ingested dataset, which provides an accurate profile that enables user applications to retrieve high-quality data without delay. The main contributions of this thesis are: i) the development of an ETL service in a Marketing Data Management Platform and ii) an examination of data reduction models with a view to assessing data quality. Chapter 1 presents a literature review of this research and describes the basic concepts and their definitions in other research, including sampling, ETL, Data Quality and Big Data. Chapter 2 We present the manner in which the ETL system fits into the framework of the data-management platform and how the entire architecture is modelled. Chapter 3 presents the outcomes of the experiment. The experimental findings, which were obtained using various types of actual data, are presented in this chapter. The performance over time and the effect of the startified sample are depicted in graphs. The closing part presents the conclusions of this thesis and discusses the prospective research.Data Quality Management in ETL Process under Resource Constraints 1 Notes 3 INTRODUCTION 9 1.1 ETL 12 1.1.1 ETL definitions 13 1.1.2 ETL tools review 14 1.2 Data quality 16 1.2.1 Data Quality Dimensions 17 1.2.2 Data Quality Objectives in the Context of ETL 18 1.2.3 ISO Data Quality Standards 19 1.3 Tcp-di benchmark 20 1.4 Big data 21 1.4.1 Vs and BIG Data 21 1.4.2 Batch processing and Big Data 22 1.5 Sampling for big data 23 1.5.1 A taxonomy for Big Data sampling techniques 24 CHAPTER 2. SYSTEM MODELING 26 2.1 ETL model 27 2.2 System architecture overview 29 2.2.1 Metadata store 30 2.2.2 Horizontal autoscaling environment 31 2.2.3 Workflow runner 33 CHAPTER 3. MEASUREMENT RESULTS 35 3.1 Estimation of the Population Mean 36 3.2 Performance evaluation of stratified random sampling for DQ assessment 37 CHAPTER 4. LABOUR PROTECTION AND SAFETY IN EMERGENCY 44 4.1 Introduction 44 4.2 Need for guidelines 45 4.2.1. Software quality 45 4.2.2 Static analysis 47 4.2.3 Automated static analysis tools 49 4.3 Universal standards 51 4.4 Challenges in safety critical systems 52 4.5 Similarities between Different Standards 53 4.6 Conclusion to safety 53 CONCLUSIONS 54 BIBLIOGRAPHY 5

    Containing the Ship of State: Managing Mobility in an Age of Logistics

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    University of Minnesota Ph.D. dissertation. 2018. Major: Political Science. Advisor: Raymond Duvall. 1 computer file (PDF); 400 pages.This dissertation argues that global logistical circulation, although often taken for granted as a banal economic process, is a political project central to the making of world order. To make this argument, it examines the social and political economic impacts of the concomitant rise of logistical management and shipping containerization as twin operations intensifying the global circulation of commercial capital. Since the 1960s, businesses have increasingly experimented with just-in-time logistical techniques to speed the realization of surplus value, leading to the rise of global transoceanic networks of distribution that reorganize commercial circulation across distinct yet densely interconnected political geographies. As logistical management systems have sought to regularize, standardize, and create flexible networks for circulating goods across vast distances around the world, they have become crucial to the expanded reproduction of capital. Accordingly, states have also adopted logistics-oriented growth strategies, investing in organizing and securing a socio-spatial order that produces a world safe for the movement of commercial capital, often in ways that inhibit the social and spatial mobility of vulnerable populations that live and work along global supply chains. The empirical focus of the dissertation is a multi-sited ethnographic study of the Trans-Pacific shipping passage between the US and China. Understanding logistics as both a material practice and calculative rationality, this dissertation employs an ethnographic approach to interrogate the effects of logistics’ global rise through four cuts: 1) A theoretical and historical analysis of the rise of logistics management and shipping containerization in the 1960s, 2) the securitization of goods movement in US maritime cargo policy, 3) the expansion of logistical infrastructure across the world’s oceans and in Los Angeles and Singapore, and 4) the seafaring labor process. My overarching claim is that logistical practices and rationalities exacerbate growing and often contradictory tensions between the mobility of capital and the containment of people and infrastructure that facilitate global circulation. Rather than understand containment as a static process of sequestration or enclosure that impedes the ability for capital and people to circulate, processes of containment have gained fundamentally productive functions that intensify and facilitate, rather than prevent or deter the long-distance expansion of capitalist networks. In this way, logistics produces a set of relations in which moving the world’s goods across space comes to be understood as normative and desirable, while containing the human lives that do this work is seen as necessary and productive
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