scholarly journals IMPLEMENTASI KOMPUTASI AWAN MENGGUNAKAN TEKNOLOGI GOOGLE APP ENGINE (GAE) DAN AMAZON WEB SERVICES (AWS)

2012 ◽  
Vol 1 (1) ◽  
Author(s):  
Adi Nugroho ◽  
Techn Khabib Mustofa
Keyword(s):  
Cloud Computing ◽  
Web Services ◽  
Web Service ◽  
Amazon Web Services

Interoperabilitas, dalam arti cara bagaimana suatu sistem yang memiliki platform perangkat keras dan perangkat lunak tertentu dapat berkomunikasi dengan sistem-sistem yang memiliki platform yang berbeda, mungkin merupakan bagian dari ‘masa lalu’. Di masa-masa yang akan datang, interoperabilitas yang selama ini ditangani secara manual oleh organisasi-organisasi/perusahaan-perusahaan akan ditangani langsung oleh vendor-vendor penyedia komputasi awan (cloud computing) yang memang memiliki sumberdaya-sumberdaya manusia (analis sistem, pemrogram, pakar jaringan), perangkat keras (komputer-komputer server yang berjumlah sangat banyak dan berkemampuan raksasa), serta perangkat lunak (sistem operasi, server aplikasi, server Web) yang memang memenuhi syarat untuk itu. Di masa yang akan datang, untuk mendapatkan layanan-layanan (service) dan tempat penyimpanan tertentu, organisasi-organisasi/perusahaan-perusahaan tidak perlu berinvestasi terlalu tinggi untuk menyediakannya sendiri; mereka bisa saja menyewanya dari vendor-vendor komputasi awan yang saat ini mulai bermunculan. Google dan Amazon adalah para pendahulu dari teknologi komputasi awan (cloud computing) ini. Melalui tulisan ini, kita tidak akan membahas struktur internal keduanya secara rinci, melainkan kita akan mencoba membahas kelebihan serta kekurangan kedua vendor komputasi awan ini dari sudutpandang para manajer di bidang Teknologi Informasi yang akan melakukan investasi yang bermanfaat bagi organisasi/perusahaannya.Kata kunci : Cloud Computing, Google App Engine, Amazon Web 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.

Web Service Composition in Cloud: A Fuzzy Rule Model

2020 ◽  
Vol 13 (3) ◽  
pp. 446-453
Author(s):  
Hussien Alhadithy ◽  
Bassam Al-Shargabi
Keyword(s):  
Cloud Computing ◽  
Web Services ◽  
Web Service ◽  
Fuzzy Rule ◽  
Fuzzy Rules ◽  
Experimental Result ◽  
User Requirements ◽  
Cloud Environment ◽  
Proposed Model ◽  
Rule Approach

Background: Cloud Computing has drawn much attention in the industry due to its costefficient schema along with more prospects, such as elasticity and scalability nature of Cloud Computing. One of the main service models of a Cloud is software as a service, where many web services are published and hosted in the Cloud environment. Many web services offered in a Cloud have similar functionality, with different of characteristics non-functional requirements such as Quality of Service (QoS). In addition, as individual web services are limited in their capability. Therefore, there is a need for composing existing services to create new functionality in the form of composite service to fulfill the requirements of Cloud user for certain processes. Methods: This paper introduces a fuzzy rule approach to compose web service based on QoS from different Cloud Computing providers. The fuzzy rule is generated based on QoS of discovered web service from Cloud in order to compose web services that only match user requirements. The proposed model is based on an agent that is responsible for discovering and composing web service that only stratified user requirements. Results: the experimental result shows that the proposed model is efficient in terms of time and the use of fuzzy rules to compose web services from different Cloud providers under different specifications and configurations of Cloud Computing environment. Conclusion: In this paper, an agent-based model was proposed to compose web services based on fuzzy rule in Cloud environment. The agent is responsible for discovering web services and generating composition plans based on offered QoS for each web service. The agent employs a set of fuzzy rules to carry out an intelligent selection to select the best composition plan that fulfills the requirements of the end user. The model was implemented on CloudSim to ensure the validity of the proposed model and performance time analysis was performed that showed good result in terms of time with regard to the Cloud Computing configuration.

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.

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