Machine Learning and Cloud Computing for Remote Monitoring of Wave Piercing Catamarans: A Case Study using MATLAB on Amazon Web Services

2020 ◽  
pp. 83-94
Author(s):  
Babak Shabani ◽  
Jason Ali-Lavroff ◽  
Damien Holloway ◽  
Spiridon Penev ◽  
Daniele Dessi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Web Services ◽  
Data Processing ◽  
High Speed ◽  
Short Term Memory ◽  
Wave Load ◽  
Efficient Manner ◽  
Amazon Web Services

Wave load cycles, wet-deck slamming events, accelerations and motion comfort are important considerations for high- speed catamarans operating in moderate to large waves. This paper provides an overview of data analytics methods and cloud computing resources for remotely monitoring motions and structural responses of a 111 m high-speed catamaran. To satisfy the data processing requirements, MATLAB Reference Architectures on Amazon Web Services (AWS) were used. Such combination enabled fast parallel computing and advanced feature engineering in a time-efficient manner. A MATLAB Production Server on AWS has been set up for near real-time analytics and execution of functions developed according to the class guidelines. A case study using Long Short-Term Memory (LSTM) networks for ship speed and Motion Sickness Incidence (MSI) is provided and discussed. Such data architecture provides a flexible and scalable solution, leading to deeper insights through big data processing and machine learning, which supports hull monitoring functions as a service.

scholarly journals Biomedical Cloud Computing With Amazon Web Services

2011 ◽  
Vol 7 (8) ◽  
pp. e1002147 ◽  
Author(s):  
Vincent A. Fusaro ◽  
Prasad Patil ◽  
Erik Gafni ◽  
Dennis P. Wall ◽  
Peter J. Tonellato
Keyword(s):  
Cloud Computing ◽  
Web Services ◽  
Amazon Web Services

scholarly journals An Investigation of Microsoft Azure and Amazon Web Services from Users’ Perspectives

2019 ◽  
Vol 14 (10) ◽  
pp. 217 ◽  
Author(s):  
Rizik M. H. Al-Sayyed ◽  
Wadi’ A. Hijawi ◽  
Anwar M. Bashiti ◽  
Ibrahim AlJarah ◽  
Nadim Obeid ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Web Services ◽  
Service Level Agreement ◽  
Service Level ◽  
Point Of View ◽  
Survey Study ◽  
Utility Computing ◽  
User Survey ◽  
Amazon Web Services ◽  
Microsoft Azure

Cloud computing is one of the paradigms that have undertaken to deliver the utility computing concept. It views computing as a utility similar to water and electricity. We aim in this paper to make an investigation of two highly efficacious Cloud platforms: Microsoft Azure (Azure) and Amazon Web Services (AWS) from users’ perspectives the point of view of users. We highlight and compare in depth the features of Azure and AWS from users’ perspectives. The features which we shall focus on include (1) Pricing, (2) Availability, (3) Confidentiality, (4) Secrecy, (5) Tier Account and (6) Service Level Agreement (SLA). The study shows that Azure is more appropriate when considering Pricing and Availability (Error Rate) while AWS is more appropriate when considering Tier account. Our user survey study and its statistical analysis agreed with the arguments made for each of the six comparisons factors.

CONDE: Climate simulation ON DEmand using HPCaaS

2020 ◽  
Author(s):  
Diego A. Pérez Montes ◽  
Juan A. Añel ◽  
Javier Rodeiro
Keyword(s):  
Cloud Computing ◽  
Web Services ◽  
Climate Simulation ◽  
On Demand ◽  
Ensemble Simulations ◽  
Amazon Web Services ◽  
Computing Environments ◽  
Microsoft Azure

<p><strong>CONDE (Climate simulation ON DEmand)</strong> is the final result of our work and research about climate and meteorological simulations over an HPC as a Service (HPCaaS) model. On our architecture we run very large climate ensemble simulations using a, adapted, WRF version that is executed on-demand and that can be deployed over different Cloud Computing environments (like Amazon Web Services, Microsoft Azure or Google Cloud) and that uses BOINC as middleware for the tasks execution and results gathering. Here, we also present as well some basic examples of applications and experiments to verify that the simulations ran in our system are correct and show valid results. </p>

Serverless Computing Using Amazon Web Services

2020 ◽  
Vol 17 (8) ◽  
pp. 3581-3585
Author(s):  
M. S. Roobini ◽  
Selvasurya Sampathkumar ◽  
Shaik Khadar Basha ◽  
Anitha Ponraj
Keyword(s):  
Cloud Computing ◽  
Web Services ◽  
Software Design ◽  
Web Application ◽  
Cloud Provider ◽  
Business Logic ◽  
Compute Model ◽  
Amazon Web Services ◽  
The Way

In the last decade cloud computing transformed the way in which we build applications. The boom in cloud computing helped to develop new software design and architecture. Helping the developers to focus more on the business logic than the infrastructure. FaaS (function as a service) compute model it gave developers to concentrate only on the application code and rest of the factors will be taken care by the cloud provider. Here we present a serverless architecture of a web application built using AWS services and provide detail analysis of lambda function and micro service software design implemented using these AWS services.

scholarly journals Long Paddock: climate risk and grazing information for Australian rangelands and grazing communities

2019 ◽  
Vol 41 (3) ◽  
pp. 225 ◽  
Author(s):  
G. Stone ◽  
R. Dalla Pozza ◽  
J. Carter ◽  
G. McKeon
Keyword(s):  
Cloud Computing ◽  
Web Services ◽  
Natural Resource ◽  
Climate Risk ◽  
Resource Base ◽  
Computing Platform ◽  
Amazon Web Services ◽  
Cloud Computing Platform ◽  
Natural Resource Base ◽  
Tailored Information

The Queensland Government’s Long Paddock website has been redeveloped on Amazon Web Services cloud computing platform, to provide Australian rangelands and grazing communities (i.e. rural landholders, managers, pastoralists (graziers), researchers, advisors, students, consultants and extension providers) with easier access to seasonal climate and pasture condition information. The website provides free, tailored information and services to support management decisions to maximise productivity, while maintaining the natural resource base. For example, historical rainfall and pasture analyses (i.e. maps, posters and data) have been developed to assist in communicating the risk of multi-year droughts that are a feature of Queensland’s highly variable climate.

scholarly journals Successes and struggles with computational reproducibility: Lessons from the Fragile Families Challenge

2019 ◽  
Author(s):  
David Liu ◽  
Matthew Salganik
Keyword(s):  
Cloud Computing ◽  
Web Services ◽  
Fragile Families ◽  
Special Issue ◽  
Computing Environment ◽  
Raw Data ◽  
Academic Researchers ◽  
Amazon Web Services ◽  
Software Engineers ◽  
Published Paper

Reproducibility is fundamental to science, and an important component of reproducibility is computational reproducibility: the ability of a researcher to recreate the results in a published paper using the original author's raw data and code. Although most people agree that computational reproducibility is important, it is still difficult to achieve in practice. In this paper, we describe our approach to enabling computational reproducibility for the 12 papers in this special issue of Socius about the Fragile Families Challenge. Our approach draws on two tools commonly used by professional software engineers but not widely used by academic researchers: software containers (e.g., Docker) and cloud computing (e.g., Amazon Web Services). These tools enabled us to standardize the computing environment around each submission, which will ease computational reproducibility both today and in the future. Drawing on our successes and struggles, we conclude with recommendations to authors and journals.

Costs of Cloud Computing for a Biometry Department

2013 ◽  
Vol 52 (01) ◽  
pp. 72-79 ◽  
Author(s):  
S. Hieke ◽  
H. Binder ◽  
G. Schwarzer ◽  
J. Knaus
Keyword(s):  
Cloud Computing ◽  
Indirect Costs ◽  
Research Unit ◽  
Main Memory ◽  
Actual System ◽  
Hardware Virtualization ◽  
System Utilization ◽  
Amazon Web Services ◽  
Computational Resources

SummaryBackground: “Cloud” computing providers, such as the Amazon Web Services (AWS), offer stable and scalable computational resources based on hardware virtualization, with short, usually hourly, billing periods. The idea of pay-as-you-use seems appealing for biometry research units which have only limited access to university or corporate data center resources or grids.Objectives: This case study compares the costs of an existing heterogeneous on-site hardware pool in a Medical Biometry and Statistics department to a comparable AWS offer.Methods: The “total cost of ownership”, including all direct costs, is determined for the on-site hardware, and hourly prices are derived, based on actual system utilization during the year 2011. Indirect costs, which are difficult to quantify are not included in this comparison, but nevertheless some rough guidance from our experience is given. To indicate the scale of costs for a methodological research project, a simulation study of a permutation-based statistical approach is performed using AWS and on-site hardware.Results: In the presented case, with a system utilization of 25 –30 percent and 3 – 5-year amortization, on-site hardware can result in smaller costs, compared to hourly rental in the cloud dependent on the instance chosen. Renting cloud instances with sufficient main memory is a deciding factor in this comparison.Conclusions: Costs for on-site hardware may vary, depending on the specific infrastructure at a research unit, but have only moderate impact on the overall comparison and subsequent decision for obtaining affordable scientific computing resources. Overall utilization has a much stronger impact as it determines the actual computing hours needed per year. Taking this into account, cloud computing might still be a viable option for projects with limited maturity, or as a supplement for short peaks in demand.

Security Issues in Fog Computing and ML-Based Solutions

2021 ◽  
pp. 209-234
Author(s):  
Himanshu Sahu ◽  
Gaytri
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Data Processing ◽  
Fog Computing ◽  
Machine Learning Techniques ◽  
Special Focus ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Security Issues ◽  
Learning Techniques

IoT requires data processing, which is provided by the cloud and fog computing. Fog computing shifts centralized data processing from the cloud data center to the edge, thereby supporting faster response due to reduced communication latencies. Its distributed architecture raises security and privacy issues; some are inherited from the cloud, IoT, and network whereas others are unique. Securing fog computing is equally important as securing cloud computing and IoT infrastructure. Security solutions used for cloud computing and IoT are similar but are not directly applicable in fog scenarios. Machine learning techniques are useful in security such as anomaly detection, intrusion detection, etc. So, to provide a systematic study, the chapter will cover fog computing architecture, parallel technologies, security requirements attacks, and security solutions with a special focus on machine learning techniques.

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