Optimizing the total energy consumption and CO₂ emissions by distributing computational workload among worldwide dispersed data centers

Major internet service providers have built and are currently building the world's largest data centres (DCs), which has already resulted in significant global energy consumption. Energy saving measures, from chip to building level, have been introduced gradually in recent decades. However, there is further potential for savings by assessing the performance of different DCs on a wider scale and evaluating information technology (IT) workload distribution strategies among these DCs. This paper proposes a methodology to optimize the electricity consumption and CO2 emissions by distributing IT workload across multiple imaginary DCs. The DCs are modelled and controlled in a virtual test environment based on a building energy simulation (BES) tool (TRNSYS). A controller tool (Matlab) is used to support testing and tuning of the optimization algorithm. A case study, consisting of the distribution of IT workload across four different types of data centers in multiple locations with different climate conditions, is presented. The case study will illustrate.</p

Optimizing the total energy consumption and CO₂ emissions by distributing computational workload among worldwide dispersed data centers

Major internet service providers have built and are currently building the world's largest data centres (DCs), which has already resulted in significant global energy consumption. Energy saving measures, from chip to building level, have been introduced gradually in recent decades. However, there is further potential for savings by assessing the performance of different DCs on a wider scale and evaluating information technology (IT) workload distribution strategies among these DCs. This paper proposes a methodology to optimize the electricity consumption and CO2 emissions by distributing IT workload across multiple imaginary DCs. The DCs are modelled and controlled in a virtual test environment based on a building energy simulation (BES) tool (TRNSYS). A controller tool (Matlab) is used to support testing and tuning of the optimization algorithm. A case study, consisting of the distribution of IT workload across four different types of data centers in multiple locations with different climate conditions, is presented. The case study will illustrate.</p

Management And Security Of Multi-Cloud Applications

Single cloud management platform technology has reached maturity and is quite successful in information technology applications. Enterprises and application service providers are increasingly adopting a multi-cloud strategy to reduce the risk of cloud service provider lock-in and cloud blackouts and, at the same time, get the benefits like competitive pricing, the flexibility of resource provisioning and better points of presence. Another class of applications that are getting cloud service providers increasingly interested in is the carriers\u27 virtualized network services. However, virtualized carrier services require high levels of availability and performance and impose stringent requirements on cloud services. They necessitate the use of multi-cloud management and innovative techniques for placement and performance management. We consider two classes of distributed applications – the virtual network services and the next generation of healthcare – that would benefit immensely from deployment over multiple clouds. This thesis deals with the design and development of new processes and algorithms to enable these classes of applications. We have evolved a method for optimization of multi-cloud platforms that will pave the way for obtaining optimized placement for both classes of services. The approach that we have followed for placement itself is predictive cost optimized latency controlled virtual resource placement for both types of applications. To improve the availability of virtual network services, we have made innovative use of the machine and deep learning for developing a framework for fault detection and localization. Finally, to secure patient data flowing through the wide expanse of sensors, cloud hierarchy, virtualized network, and visualization domain, we have evolved hierarchical autoencoder models for data in motion between the IoT domain and the multi-cloud domain and within the multi-cloud hierarchy

Machine learning approaches for tomato crop yield prediction in precision agriculture

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe objective of this project was to apply ML techniques to predict processing tomato crop yield given information on soil properties, weather conditions, and applied fertilizers. Besides being robust enough for predicting tomato productivity, the model needed to be interpretable and transparent for the business. The models assessed were Decision Trees Regression, ensemble bagging models like Random Forest Regression, and boosting techniques like Gradient Boosting Regression, and Support Vector Regression. Overall, Gradient Boosting and Support Vector models presented the best performance. For improving the predictive power, we combined the predictions of our two best models into a stacked approach with a Ridge Regression as the final model. The generalization error of the final chosen model on new data was 9.02 ton/ha for the MAE metric, 9.5% for the MAPE, and 13.5 ton/ha for the RMSE. This means that our model can predict tomato crop yield with an approximate error of 9 ton/ha. Even though our final model was complex and not intrinsically interpretable, we were able to apply model-agnostic interpretation methods like the SHAP summary plot to better understand the feature importance and feature effects, and the Accumulated Local Effects (ALE) plot, to explain how features influence the outcome of the model on average. In general, the objectives of the project were accomplished and the company was satisfied with the result of the model and its interpretation

predictSLUMS: A new model for identifying and predicting informal settlements and slums in cities from street intersections using machine learning

Identifying current and future informal regions within cities remains a crucial issue for policymakers and governments in developing countries. The delineation process of identifying such regions in cities requires a lot of resources. While there are various studies that identify informal settlements based on satellite image classification, relying on both supervised or unsupervised machine learning approaches, these models either require multiple input data to function or need further development with regards to precision. In this paper, we introduce a novel method for identifying and predicting informal settlements using only street intersections data, regardless of the variation of urban form, number of floors, materials used for construction or street width. With such minimal input data, we attempt to provide planners and policy-makers with a pragmatic tool that can aid in identifying informal zones in cities. The algorithm of the model is based on spatial statistics and a machine learning approach, using Multinomial Logistic Regression (MNL) and Artificial Neural Networks (ANN). The proposed model relies on defining informal settlements based on two ubiquitous characteristics that these regions tend to be filled in with smaller subdivided lots of housing relative to the formal areas within the local context, and the paucity of services and infrastructure within the boundary of these settlements that require relatively bigger lots. We applied the model in five major cities in Egypt and India that have spatial structures in which informality is present. These cities are Greater Cairo, Alexandria, Hurghada and Minya in Egypt, and Mumbai in India. The predictSLUMS model shows high validity and accuracy for identifying and predicting informality within the same city the model was trained on or in different ones of a similar context.Comment: 26 page

GEOSPATIAL-BASED ENVIRONMENTAL MODELLING FOR COASTAL DUNE ZONE MANAGEMENT

Tomaintain biodiversity and ecological functionof coastal dune areas, itis important that practical and effective environmentalmanagemental strategies are developed. Advances in geospatial technologies offer a potentially very useful source of data for studies in this environment. This research project aimto developgeospatialdata-basedenvironmentalmodellingforcoastaldunecomplexestocontributetoeffectiveconservationstrategieswithparticularreferencetotheBuckroneydunecomplexinCo.Wicklow,Ireland.Theprojectconducteda general comparison ofdifferent geospatial data collection methodsfor topographic modelling of the Buckroney dune complex. These data collection methodsincludedsmall-scale survey data from aerial photogrammetry, optical satellite imagery, radar and LiDAR data, and ground-based, large-scale survey data from Total Station(TS), Real Time Kinematic (RTK) Global Positioning System(GPS), terrestrial laser scanners (TLS) and Unmanned Aircraft Systems (UAS).The results identifiedthe advantages and disadvantages of the respective technologies and demonstrated thatspatial data from high-end methods based on LiDAR, TLS and UAS technologiesenabled high-resolution and high-accuracy 3D datasetto be gathered quickly and relatively easily for the Buckroney dune complex. Analysis of the 3D topographic modelling based on LiDAR, TLS and UAS technologieshighlighted the efficacy of UAS technology, in particular,for 3D topographicmodellingof the study site.Theproject then exploredthe application of a UAS-mounted multispectral sensor for 3D vegetation mappingof the site. The Sequoia multispectral sensorused in this researchhas green, red, red-edge and near-infrared(NIR)wavebands, and a normal RGB sensor. The outcomesincludedan orthomosiac model, a 3D surface model and multispectral imageryof the study site. Nineclassification strategies were usedto examine the efficacyof UAS-IVmounted multispectral data for vegetation mapping. These strategies involved different band combinations based on the three multispectral bands from the RGB sensor, the four multispectral bands from the multispectral sensor and sixwidely used vegetation indices. There were 235 sample areas (1 m × 1 m) used for anaccuracy assessment of the classification of thevegetation mapping. The results showed vegetation type classification accuracies ranging from 52% to 75%. The resultdemonstrated that the addition of UAS-mounted multispectral data improvedthe classification accuracy of coastal vegetation mapping of the Buckroney dune complex

Use and Improvement of Remote Sensing and Geospatial Technologies in Support of Crop Area and Yield Estimations in the West African Sahel

In arid and semi-arid West Africa, agricultural production and regional food security depend largely on small-scale subsistence farming and rainfed crops, both of which are vulnerable to climate variability and drought. Efforts made to improve crop monitoring and our ability to estimate crop production (areas planted and yield estimations by crop type) in the major agricultural zones of the region are critical paths for minimizing climate risks and to support food security planning. The main objective of this dissertation research was to contribute to these efforts using remote sensing technologies. In this regard, the first analysis documented the low reliability of existing land cover products for cropland area estimation (Chapter 2). Then two satellite remote sensing-based datasets were developed that 1) accurately map cropland areas in the five countries of Sahelian West Africa (Senegal, Mauritania, Mali, Burkina Faso and Niger; Chapter 3), and 2) focus on the country of Mali to identify the location and prevalence of the major subsistence crops (millet, sorghum, maize and non-irrigated rice; Chapter 4). The regional cropland area product is distributed as the West African Sahel Cropland area at 30 m (WASC30). The development of the new dataset involved high density training data (380,000 samples) developed by USGS in collaboration with CILSS for training about 200 locally optimized random forest (RF) classifiers using Landsat 8 surface reflectances and vegetation indices and the Google Earth Engine platform. WASC30 greatly improves earlier estimates through inclusion of cropland information for both rainfed and irrigated areas mapped with a class-specific accuracy of 79% across the West Africa Sahel. Used as a mask in crop monitoring systems, the new cropland area data could bring critical insights by reducing uncertainties in xv identification of croplands as crop growth condition metrics are extracted. WASC30 allowed us to derive detailed statistics on cultivated areas in the Sahel, at country and agroclimatic scales. Intensive agricultural zones were highlighted as well. The second dataset, mapping crop types for the country of Mali, is meant to separate signals of different crop types for improved crop yield estimation. The crop type map was used to derive detailed agricultural statistics (e.g. acreage by crop types, spatial distribution) at finer administrative scales than has previously been possible. The crop fraction information by crop type extracted from the map, gives additional details on farmers preferences by regions, and the natural adaptability of different crop types. The final analysis of this dissertation explores the use of ensemble machine learning techniques to predict maize yield in Mali (Chapter 5). Climate data (precipitation and temperature), and vegetation indices (Normalized Difference Vegetation Index, NDVI, the Enhanced Vegetation Index, EVI, and the Normalized Difference Water Index, NDWI) are used as predictors, while actual yields collected in 2017 by the Malian Ministry of Agriculture are the reference data. Random forest presented better predictive performance as compared to boosted regression trees (BRT). Results showed that climate variables have more predictive power for maize yield compared to vegetation indices. Among vegetation indices, the NDWI appeared to be the most influential predictor, maybe because of water requirement of maize and the sensitivity of this index to water in semi-arid regions. Tested with two different independent datasets, one constituted by 20% of the reference information, and another including observed yields for year 2018 (a one-year-left analysis), maize yield predictions were promising for year 2017 (RMSE = 362 kg/ha), but showed higher error for 2018 (RMSE = 707 kg/ha). That is, the fitted model may not capture accurately year to year variabilities in predicted maize yield. In this analysis, predictions were limited to field samples (~600 fields) across the country of Mali. It would be valuable in the future to predict maize yield for each pixel of the new developed crop type map. That will lead to a detailed spatial analysis of maize yield, allowing identification of low yielding regions for targeted interventions which could improve food security. Keywords: Agricultural identification of croplands as crop growth condition metrics are extracted. WASC30 allowed us to derive detailed statistics on cultivated areas in the Sahel, at country and agroclimatic scales. Intensive agricultural zones were highlighted as well. The second dataset, mapping crop types for the country of Mali, is meant to separate signals of different crop types for improved crop yield estimation. The crop type map was used to derive detailed agricultural statistics (e.g. acreage by crop types, spatial distribution) at finer administrative scales than has previously been possible. The crop fraction information by crop type extracted from the map, gives additional details on farmers preferences by regions, and the natural adaptability of different crop types. The final analysis of this dissertation explores the use of ensemble machine learning techniques to predict maize yield in Mali (Chapter 5). Climate data (precipitation and temperature), and vegetation indices (Normalized Difference Vegetation Index, NDVI, the Enhanced Vegetation Index, EVI, and the Normalized Difference Water Index, NDWI) are used as predictors, while actual yields collected in 2017 by the Malian Ministry of Agriculture are the reference data. Random forest presented better predictive performance as compared to boosted regression trees (BRT). Results showed that climate variables have more predictive power for maize yield compared to vegetation indices. Among vegetation indices, the NDWI appeared to be the most influential predictor, maybe because of water requirement of maize and the sensitivity of this index to water in semi-arid regions. Tested with two different independent datasets, one constituted by 20% of the reference information, and another including observed yields for year 2018 (a one-year-left analysis), maize yield predictions were promising for year 2017 (RMSE = 362 kg/ha), but showed higher error for 2018 (RMSE = 707 kg/ha). That is, the fitted model may not capture accurately year to year variabilities in predicted maize yield. In this analysis, predictions were limited to field samples (~600 fields) across the country of Mali. It would be valuable in the future to predict maize yield for each pixel of the new developed crop type map. That will lead to a detailed spatial analysis of maize yield, allowing identification of low yielding regions for targeted interventions which could improve food security

A Framework for Integrating Transportation Into Smart Cities

In recent years, economic, environmental, and political forces have quickly given rise to “Smart Cities” -- an array of strategies that can transform transportation in cities. Using a multi-method approach to research and develop a framework for smart cities, this study provides a framework that can be employed to: Understand what a smart city is and how to replicate smart city successes; The role of pilot projects, metrics, and evaluations to test, implement, and replicate strategies; and Understand the role of shared micromobility, big data, and other key issues impacting communities. This research provides recommendations for policy and professional practice as it relates to integrating transportation into smart cities