An educational toolkit for teaching cloud computing

2021 ◽  
Vol 51 (4) ◽  
pp. 36-46
Author(s):  
Cosimo Anglano ◽  
Massimo Canonico ◽  
Marco Guazzone
Keyword(s):  
Cloud Computing ◽  
Educational Context ◽  
Computing Platform ◽  
The Core ◽  
Cloud Providers ◽  
Core Concepts ◽  
Cloud Computing Platform ◽  
Effective Use

In an educational context, experimenting with a real cloud computing platform is very important to let students understand the core concepts, methodologies and technologies of cloud computing. However, API heterogeneity of cloud providers complicates the experimentation by forcing students to focus on the use of different APIs, and by hindering the jointly use of different platforms. In this paper, we present EasyCloud, a toolkit enabling the easy and effective use of different cloud platforms. In particular, we describe its features, architecture, scalability, and use in our cloud computing courses, as well as the pedagogical insights we learnt over the years.

A P2P Resource Management Model Based on Cloud Computing

2014 ◽  
Vol 989-994 ◽  
pp. 1930-1933
Author(s):  
Yang Lu ◽  
Guang Feng Liu
Keyword(s):  
Cloud Computing ◽  
Resource Management ◽  
Cloud Layer ◽  
Management Model ◽  
Cloud Systems ◽  
Computing Platform ◽  
The Core ◽  
Model Based ◽  
Cloud Computing Platform ◽  
And Storage

The technology of cloud computing has become a hot issue of research in the service of network in recent years. Cloud computing platform provide computing and storage services to customers. And it has been widely applied in e-business, e-education and etc.. While Cloud systems are usually hosted in large datacenters which may become a bottleneck to the system. In this paper we describe the design of a double-layer P2P model based on cloud computing. In the model, user nodes grouped into clusters form the Extended Cloud layer, and transfer file to each other without participation of the Core Cloud layer. The new model has better scalability and efficiency.

Performance Test and Fault Prediction in Cloud Computing Platform

2013 ◽  
Vol 321-324 ◽  
pp. 2524-2527
Author(s):  
Li Hao Wei ◽  
Jie Qing Ai ◽  
Tian Wang ◽  
Hong Zou ◽  
Kai Dong Zhou
Keyword(s):  
Cloud Computing ◽  
Performance Test ◽  
Fault Prediction ◽  
Support Vector ◽  
Prediction Problem ◽  
Learning Problem ◽  
Software Faults ◽  
Computing Platform ◽  
The Core ◽  
Cloud Computing Platform

Performance test and fault prediction is the core challenge in building robust cloud computing platform. This paper converted fault prediction problem into a machine learning problem. Based on extracted software feature, software faults were predicted using support vector regression machine. Experimental results show that new method can improve the precision of fault prediction.

Power Management of Virtualized Cloud Computing Platform

2012 ◽  
Vol 35 (6) ◽  
pp. 1262 ◽  
Author(s):  
Ke-Jiang YE ◽  
Zhao-Hui WU ◽  
Xiao-Hong JIANG ◽  
Qin-Ming HE
Keyword(s):  
Cloud Computing ◽  
Power Management ◽  
Computing Platform ◽  
Cloud Computing Platform

Predicting Node Failures in an Ultra-Large-Scale Cloud Computing Platform

2020 ◽  
Vol 29 (2) ◽  
pp. 1-24
Author(s):  
Yangguang Li ◽  
Zhen Ming (Jack) Jiang ◽  
Heng Li ◽  
Ahmed E. Hassan ◽  
Cheng He ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Computing Platform ◽  
Cloud Computing Platform ◽  
Node Failures

In defense of decentralized research data management

Neuroforum ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Michael Hanke ◽  
Franco Pestilli ◽  
Adina S. Wagner ◽  
Christopher J. Markiewicz ◽  
Jean-Baptiste Poline ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Management ◽  
Research Data ◽  
Data Archive ◽  
Individual Scientist ◽  
Computing Platform ◽  
Research Data Management ◽  
Cloud Computing Platform ◽  
Research Objects ◽  
Central Services

Abstract Decentralized research data management (dRDM) systems handle digital research objects across participating nodes without critically relying on central services. We present four perspectives in defense of dRDM, illustrating that, in contrast to centralized or federated research data management solutions, a dRDM system based on heterogeneous but interoperable components can offer a sustainable, resilient, inclusive, and adaptive infrastructure for scientific stakeholders: An individual scientist or laboratory, a research institute, a domain data archive or cloud computing platform, and a collaborative multisite consortium. All perspectives share the use of a common, self-contained, portable data structure as an abstraction from current technology and service choices. In conjunction, the four perspectives review how varying requirements of independent scientific stakeholders can be addressed by a scalable, uniform dRDM solution and present a working system as an exemplary implementation.

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