scholarly journals Minimizing Technical Complexities in Emerging Cloud Computing Platforms

2011 ◽  
pp. 603-610
Author(s):  
Andreas Menychtas ◽  
George Kousiouris ◽  
Dimosthenis Kyriazis ◽  
Theodora Varvarigou
Keyword(s):  
Cloud Computing ◽  
Computing Platforms

Econometrics Pedagogy and Cloud Computing: Training the Next Generation of Economists and Data Scientists

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Danielle V. Handel ◽  
Anson T. Y. Ho ◽  
Kim P. Huynh ◽  
David T. Jacho-Chávez ◽  
Carson H. Rea
Keyword(s):  
Cloud Computing ◽  
Data Science ◽  
Internet Access ◽  
Next Generation ◽  
Web Browser ◽  
Computer Laboratory ◽  
Economics Students ◽  
Computing Platforms

AbstractThis paper describes how cloud computing tools widely used in the instruction of data scientists can be introduced and taught to economics students as part of their curriculum. The demonstration centers around a workflow where the instructor creates a virtual server and the students only need Internet access and a web browser to complete in-class tutorials, assignments, or exams. Given how prevalent cloud computing platforms are becoming for data science, introducing these techniques into students’ econometrics training would prepare them to be more competitive when job hunting, while making instructors and administrators re-think what a computer laboratory means on campus.

scholarly journals A Parallel Unmixing-Based Content Retrieval System for Distributed Hyperspectral Imagery Repository on Cloud Computing Platforms

2021 ◽  
Vol 13 (2) ◽  
pp. 176
Author(s):  
Peng Zheng ◽  
Zebin Wu ◽  
Jin Sun ◽  
Yi Zhang ◽  
Yaoqin Zhu ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Retrieval System ◽  
Hyperspectral Image ◽  
Parallel Implementation ◽  
Remotely Sensed Data ◽  
Web Interfaces ◽  
Content Retrieval ◽  
Service Mode ◽  
Computing Platforms

As the volume of remotely sensed data grows significantly, content-based image retrieval (CBIR) becomes increasingly important, especially for cloud computing platforms that facilitate processing and storing big data in a parallel and distributed way. This paper proposes a novel parallel CBIR system for hyperspectral image (HSI) repository on cloud computing platforms under the guide of unmixed spectral information, i.e., endmembers and their associated fractional abundances, to retrieve hyperspectral scenes. However, existing unmixing methods would suffer extremely high computational burden when extracting meta-data from large-scale HSI data. To address this limitation, we implement a distributed and parallel unmixing method that operates on cloud computing platforms in parallel for accelerating the unmixing processing flow. In addition, we implement a global standard distributed HSI repository equipped with a large spectral library in a software-as-a-service mode, providing users with HSI storage, management, and retrieval services through web interfaces. Furthermore, the parallel implementation of unmixing processing is incorporated into the CBIR system to establish the parallel unmixing-based content retrieval system. The performance of our proposed parallel CBIR system was verified in terms of both unmixing efficiency and accuracy.

scholarly journals Predictability of IP Address Allocations for Cloud Computing Platforms

2020 ◽  
Vol 15 ◽  
pp. 500-511 ◽  
Author(s):  
Hussain M. J. Almohri ◽  
Layne T. Watson ◽  
David Evans
Keyword(s):  
Cloud Computing ◽  
Ip Address ◽  
Computing Platforms

Strong Tenant Separation in Cloud Computing Platforms

2019 ◽  
Author(s):  
Michael Pfeiffer ◽  
Michael Rossberg ◽  
Simon Buttgereit ◽  
Guenter Schaefer
Keyword(s):  
Cloud Computing ◽  
Computing Platforms

Cloud Computing Based Cognitive Radio Networking

2013 ◽  
pp. 153-164 ◽  
Author(s):  
Sachin Shetty ◽  
Danda B. Rawat
Keyword(s):  
Cloud Computing ◽  
Cognitive Radio ◽  
Adaptive Learning ◽  
Dynamic Environments ◽  
Spectrum Management ◽  
Computational Capacity ◽  
Fast Processing ◽  
Limited Power ◽  
Adaptation And Learning ◽  
Computing Platforms

This chapter describes state-of-the art techniques to improve performance of spectrum sensing and spectrum management in Cognitive Radio Networks (CRN) by leveraging services available in cloud computing platforms. CRNs are capable of adaptive learning and reconfiguration to provide consistent communications in dynamic environments. However, ensuring adaptation and learning in CRN will require availability of large volume of data and fast processing. However, the performance and security of CRN is considerably constrained by its limited power, memory and computational capacity, it may not be able to achieve its full capability. Fortunately, the advent of cloud computing has the potential to mitigate these constraints due its vast storage and computational capacity.

Cloud Security

2015 ◽  
pp. 236-250
Author(s):  
Natasha Csicsmann ◽  
Victoria McIntyre ◽  
Patrick Shea ◽  
Syed S. Rizvi
Keyword(s):  
Cloud Computing ◽  
Performance Analysis ◽  
Large Scale ◽  
Research Issues ◽  
Biometric Technology ◽  
Cloud Computing Security ◽  
Security Technologies ◽  
Encryption Schemes ◽  
Computing Platforms ◽  
Cloud Auditing

Strong authentication and encryption schemes help cloud stakeholders in performing the robust and accurate cloud auditing of a potential service provider. All security-related issues and challenges, therefore, need to be addressed before a ubiquitous adoption of cloud computing. In this chapter, the authors provide an overview of existing biometrics-based security technologies and discuss some of the open research issues that need to be addressed for making biometric technology an effective tool for cloud computing security. Finally, this chapter provides a performance analysis on the use of large-scale biometrics-based authentication systems for different cloud computing platforms.

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