scholarly journals Insights from Learning Analytics for Hands-On Cloud Computing Labs in AWS

2020 ◽  
Vol 10 (24) ◽  
pp. 9148
Author(s):  
Germán Moltó ◽  
Diana M. Naranjo ◽  
J. Damian Segrelles
Keyword(s):  
Cloud Computing ◽  
Learning Analytics ◽  
Cloud Provider ◽  
Online Students ◽  
Computing Services ◽  
Hands On ◽  
Data Processing Tool ◽  
Amazon Web Services ◽  
Course Length ◽  
And Performance

Cloud computing instruction requires hands-on experience with a myriad of distributed computing services from a public cloud provider. Tracking the progress of the students, especially for online courses, requires one to automatically gather evidence and produce learning analytics in order to further determine the behavior and performance of students. With this aim, this paper describes the experience from an online course in cloud computing with Amazon Web Services on the creation of an open-source data processing tool to systematically obtain learning analytics related to the hands-on activities carried out throughout the course. These data, combined with the data obtained from the learning management system, have allowed the better characterization of the behavior of students in the course. Insights from a population of more than 420 online students through three academic years have been assessed, the dataset has been released for increased reproducibility. The results corroborate that course length has an impact on online students dropout. In addition, a gender analysis pointed out that there are no statistically significant differences in the final marks between genders, but women show an increased degree of commitment with the activities planned in the course.

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 A Visual Dashboard to Track Learning Analytics for Educational Cloud Computing

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2952 ◽  
Author(s):  
Diana M. Naranjo ◽  
José R. Prieto ◽  
Germán Moltó ◽  
Amanda Calatrava
Keyword(s):  
Cloud Computing ◽  
Real World ◽  
Learning Analytics ◽  
Self Regulation ◽  
Time Frame ◽  
Self Awareness ◽  
Face To Face ◽  
Student Activity ◽  
Amazon Web Services ◽  
High Level

Cloud providers such as Amazon Web Services (AWS) stand out as useful platforms to teach distributed computing concepts as well as the development of Cloud-native scalable application architectures on real-world infrastructures. Instructors can benefit from high-level tools to track the progress of students during their learning paths on the Cloud, and this information can be disclosed via educational dashboards for students to understand their progress through the practical activities. To this aim, this paper introduces CloudTrail-Tracker, an open-source platform to obtain enhanced usage analytics from a shared AWS account. The tool provides the instructor with a visual dashboard that depicts the aggregated usage of resources by all the students during a certain time frame and the specific use of AWS for a specific student. To facilitate self-regulation of students, the dashboard also depicts the percentage of progress for each lab session and the pending actions by the student. The dashboard has been integrated in four Cloud subjects that use different learning methodologies (from face-to-face to online learning) and the students positively highlight the usefulness of the tool for Cloud instruction in AWS. This automated procurement of evidences of student activity on the Cloud results in close to real-time learning analytics useful both for semi-automated assessment and student self-awareness of their own training progress.

scholarly journals Benchmarking undedicated cloud computing providers for analysis of genomic datasets.

2014 ◽  
Author(s):  
Seyhan Yazar ◽  
George EC Gooden ◽  
David A Mackey ◽  
Alex Hewitt
Keyword(s):  
Cloud Computing ◽  
Cost Effective ◽  
Clock Time ◽  
Male Genome ◽  
Computing Services ◽  
Amazon Web Services ◽  
Major Bottleneck ◽  
Cloud Computing Services ◽  
Biological Discovery ◽  
The Cost

A major bottleneck in biological discovery is now emerging at the computational level. Cloud computing offers a dynamic means whereby small and medium-sized laboratories can rapidly adjust their computational capacity. We benchmarked two established cloud computing services, Amazon Web Services Elastic MapReduce (EMR) on Amazon EC2 instances and Google Compute Engine (GCE), using publicly available genomic datasets (E.coli CC102 strain and a Han Chinese male genome) and a standard bioinformatic pipeline on a Hadoop-based platform. Wall-clock time for complete assembly differed by 52.9% (95%CI: 27.5-78.2) for E.coli and 53.5% (95%CI: 34.4-72.6) for human genome, with GCE being more efficient than EMR. The cost of running this experiment on EMR and GCE differed significantly, with the costs on EMR being 257.3% (95%CI: 211.5-303.1) and 173.9% (95%CI: 134.6-213.1) more expensive for E.coli and human assemblies respectively. Thus, GCE was found to outperform EMR both in terms of cost and wall-clock time. Our findings confirm that cloud computing is an efficient and potentially cost-effective alternative for analysis of large genomic datasets. In addition to releasing our cost-effectiveness comparison, we present available ready-to-use scripts for establishing Hadoop instances with Ganglia monitoring on EC2 or GCE.

Comparative Study of Amazon Web Services (AWS) and Online Computer Library Services (OCLC) Web Players

2014 ◽  
pp. 212-229
Author(s):  
Ravikant M. Deshpande ◽  
Suvarna H. Paunikar ◽  
Nilima D. Likhar
Keyword(s):  
Cloud Computing ◽  
Web Services ◽  
Cloud Service ◽  
It Services ◽  
Configurable Computing ◽  
Computing Services ◽  
Generic Term ◽  
Amazon Web Services ◽  
Computer Library ◽  
Technology Benefits

Cloud computing is a model to provide on-demand access of configurable computing services and resources to the network users without direct service provider interaction. Cloud computing is one of the new buzzwords in the business world. It is a generic term for computing solution where software and services are provided over the Internet. Also the cloud computing delivered and managed IT services in several different forms such as Platform, Infrastructure, and to publish Web services for the patrons. In this chapter we discuss technology, benefits, and initiatives and mainly compare about the Amazon Web Services (AWS) and Online Computer Library Centre (OCLC) cloud service players.

Forensics as a Service

2013 ◽  
pp. 266-290 ◽  
Author(s):  
Jon Rav Gagan Shende
Keyword(s):  
Cloud Computing ◽  
Criminal Activity ◽  
Cloud Service ◽  
Cloud Provider ◽  
Technology System ◽  
Cloud Forensics ◽  
Computing Services ◽  
Cloud Computing Services ◽  
New Challenges ◽  
Information Technology System

In today’s dynamic information technology system, one area of tremendous focus and recent growth has been that of the cloud-computing model in its various offerings. With this growth, however, come new challenges within the realms of e-discovery and digital forensics, as we traditionally know it. The rapid growth of cloud-computing services and the rate of acceptance and use by consumers are on the rise. Conversely, both legitimate and illegitimate activates can leverage the resources of the cloud to execute their operations. With the challenges growing to combat computer crime that utilizes the cloud ecosystem and the ease of which a criminal activity may be hidden using a cloud service, it is imperative that a cloud provider dedicate time, training, budget, and other resources to provide the facility for forensic investigators as well as law enforcement to combat this threat. The Cloud-Forensics-as-a-Service (FRaaS) model introduced later in this chapter can provide a comprehensive cloud forensics solution for creating a repeatable system. Such a system could be implemented as a standard forensics operational model for deployment within the cloud ecosystem regardless of environments and client service lines.

scholarly journals Current Development on Cloud Computing Service Models for End Users Data Analysis

2019 ◽  
Vol 9 (2) ◽  
pp. 14
Author(s):  
Nur Hamezah Abdul Malic Abdul Malik ◽  
Tengku Adil Tengku Izhar ◽  
Mohd Razilan Abdul Kadir
Keyword(s):  
Cloud Computing ◽  
Critical Factors ◽  
The Past ◽  
Computing Services ◽  
Technology Advancement ◽  
Cloud Computing Service ◽  
Cloud Computing Services ◽  
And Performance ◽  
Framework Development ◽  
Relevant Factors

The emerging cloud computing has lead society to accept this technology advancement in their daily life. Referring to the past studies, it is found that there are few factors that effecting the usage satisfaction in using cloud computing services. Many cloud computing providers are always competing to give users the best feature out of their offered services. However, it should consider knowing the factors that contribute to the usage satisfaction. This paper reviewed the previous study on cloud computing services. We compare previous models on cloud computing to analyse current gaps on critical factors toward future framework development. The reviews contain relevant factors such as security, efficiency and performance, cost, organisation and technology developed based from the literature.

scholarly journals Configuration Method of AWS Security Architecture That Is Applicable to the Cloud Lifecycle for Sustainable Social Network

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Se-Joon Park ◽  
Yong-Joon Lee ◽  
Won-Hyung Park
Keyword(s):  
Cloud Computing ◽  
Social Network ◽  
Personal Information ◽  
Cloud Service ◽  
Cloud Services ◽  
Security Architecture ◽  
Computing Services ◽  
The World ◽  
Amazon Web Services ◽  
Cloud Computing Services

Recently, due to the many features and advantages of cloud computing, “cloud service” is being introduced to countless industries around the world at an unbelievably rapid pace. However, with the rapid increase in the introduction of cloud computing services, security vulnerabilities are increasing and the risk of technology leakage from cloud computing services is also expected to increase in social network service. Therefore, this study will propose an AWS-based (Amazon Web Services) security architecture configuration method that can be applied for the entire life cycle (planning, establishment, and operation) of cloud services for better security in AWS Cloud Services, which is the most used cloud service in the world. The proposed AWS security guide consists of five different areas, Security Solution Selection Guide, Personal Information Safeguard Guide, Security Architecture Design Guide, Security Configuration Guide, and Operational Security Checklist, for a safe social network. The AWS Security Architecture has been designed with three reference models: Standard Security Architecture, Basic Security Architecture, and Essential Security Architecture. The AWS Security Guide and AWS Security Architecture proposed in this paper are expected to help many businesses and institutions that are hoping to establish and operate a safe and reliable AWS cloud system in the social network environment.

scholarly journals Galaxy-Kubernetes integration: scaling bioinformatics workflows in the cloud

2018 ◽  
Author(s):  
Pablo Moreno ◽  
Luca Pireddu ◽  
Pierrick Roger ◽  
Nuwan Goonasekera ◽  
Enis Afgan ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Molecular Biology ◽  
Cloud Provider ◽  
Link Type ◽  
The Galaxy ◽  
Amazon Web Services ◽  
Important Challenge ◽  
Container Orchestration ◽  
Biology Laboratory ◽  
Bioinformatics Workflows

SummaryMaking reproducible, auditable and scalable data-processing analysis workflows is an important challenge in the field of bioinformatics. Recently, software containers and cloud computing introduced a novel solution to address these challenges. They simplify software installation, management and reproducibility by packaging tools and their dependencies. In this work we implemented a cloud provider agnostic and scalable container orchestration setup for the popular Galaxy workflow environment. This solution enables Galaxy to run on and offload jobs to most cloud providers (e.g. Amazon Web Services, Google Cloud or OpenStack, among others) through the Kubernetes container orchestrator.AvailabilityAll code has been contributed to the Galaxy Project and is available (since Galaxy 17.05) at https://github.com/galaxyproject/ in the galaxy and galaxy-kubernetes repositories. https://public.phenomenal-h2020.eu/ is an example deployment.Suppl. InformationSupplementary Files are available [email protected], European Molecular Biology Laboratory, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK, Tel: +44-1223-494267, Fax: +44-1223-484696.

scholarly journals A Comparative Analysis of Honeypots on Different Cloud Platforms

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2433
Author(s):  
Christopher Kelly ◽  
Nikolaos Pitropakis ◽  
Alexios Mylonas ◽  
Sean McKeown ◽  
William J. Buchanan
Keyword(s):  
Cloud Computing ◽  
Situational Awareness ◽  
Personal Data ◽  
The United States ◽  
Cloud Provider ◽  
Cloud Data ◽  
Cloud Computing Service ◽  
Cloud Providers ◽  
Multiple Regions ◽  
Amazon Web Services

In 2019, the majority of companies used at least one cloud computing service and it is expected that by the end of 2021, cloud data centres will process 94% of workloads. The financial and operational advantages of moving IT infrastructure to specialised cloud providers are clearly compelling. However, with such volumes of private and personal data being stored in cloud computing infrastructures, security concerns have risen. Motivated to monitor and analyze adversarial activities, we deploy multiple honeypots on the popular cloud providers, namely Amazon Web Services (AWS), Google Cloud Platform (GCP) and Microsoft Azure, and operate them in multiple regions. Logs were collected over a period of three weeks in May 2020 and then comparatively analysed, evaluated and visualised. Our work revealed heterogeneous attackers’ activity on each cloud provider, both when one considers the volume and origin of attacks, as well as the targeted services and vulnerabilities. Our results highlight the attempt of threat actors to abuse popular services, which were widely used during the COVID-19 pandemic for remote working, such as remote desktop sharing. Furthermore, the attacks seem to exit not only from countries that are commonly found to be the source of attacks, such as China, Russia and the United States, but also from uncommon ones such as Vietnam, India and Venezuela. Our results provide insights on the adversarial activity during our experiments, which can be used to inform the Situational Awareness operations of an organisation.

scholarly journals Comparative Analysis of Various Cloud Technologies

2011 ◽  
Vol 1 (1) ◽  
pp. 23-27
Author(s):  
Akanksha Akanksha ◽  
Dr. Gurdev Singh
Keyword(s):  
Cloud Computing ◽  
Comparative Analysis ◽  
Large Scale ◽  
Storage Capacity ◽  
Computing Services ◽  
Static Data ◽  
Cloud Computing Services ◽  
And Performance ◽  
Cloud Technologies ◽  
Memory Utilization

With the increasing prevalence and demand of large scale cloudcomputing environment, a researcher has to draw more attentiontowards the services provided by the CLOUD. As the access tothe server is increasing, centralized and distributed computingarchitecture will produce bottlenecks data which affect thequality of cloud computing services and bring the huge supportto users. In this paper we are going to propose certain vitalaspects such as memory utilization, storage capacity to check theefficiency and performance of various clouds in cloudcomputing environment. This is based upon the static data. Theproposed mechanism enables users to access memories invarious systems depending on the predefined criteria. Selectionmethod for accessing the memory of a resource is properlyintroduced in this paper. Our evaluation results show that theaggregation of various clouds is effective in indicating the betterefficiency and also to reduce network traffic sent over cloudnetworks.

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