scholarly journals Enabling Immediate Access to Earth Science Models through Cloud Computing: Application to the GEOS-Chem Model

2019 ◽  
Vol 100 (10) ◽  
pp. 1943-1960 ◽  
Author(s):  
Jiawei Zhuang ◽  
Daniel J. Jacob ◽  
Judith Flo Gaya ◽  
Robert M. Yantosca ◽  
Elizabeth W. Lundgren ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Atmospheric Chemistry ◽  
Earth Science ◽  
Easy Access ◽  
Computing Environment ◽  
Software Environment ◽  
Science Models ◽  
Amazon Web Services ◽  
Computing Platforms ◽  
General Guidance

AbstractCloud computing platforms can provide fast and easy access to complex Earth science models and large datasets. This article presents a mature capability for running the GEOS-Chem global 3D model of atmospheric chemistry on the Amazon Web Services (AWS) cloud. GEOS-Chem users at any experience level can get immediate access to the latest, standard version of the model in a preconfigured software environment with all needed meteorological and other input data, and they can analyze model output data easily within the cloud using Python tools in Jupyter notebooks. Users with no prior knowledge of cloud computing are provided with easy-to-follow, step-by-step instructions. They can learn how to complete a demo project in less than one hour, and from there they can configure and submit their own simulations. The cloud is particularly attractive for beginning and occasional users who otherwise may need to spend substantial time configuring a local computing environment. Heavy users with their own local clusters can also benefit from the cloud to access the latest standard model and datasets, share simulation configurations and results, benchmark local simulations, and respond to surges in computing demand. Software containers allow GEOS-Chem and its software environment to be moved smoothly between cloud platforms and local clusters, so that the exact same simulation can be reproduced everywhere. Because the software requirements and workflows tend to be similar across Earth science models, the work presented here provides general guidance for porting models to cloud computing platforms in a user-accessible way.

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.

Cloud Computing

2012 ◽  
pp. 191-207 ◽  
Author(s):  
Shantanu Pal
Keyword(s):  
Cloud Computing ◽  
Service Providers ◽  
Effective Means ◽  
Cloud Service ◽  
Security And Privacy ◽  
Cloud Infrastructure ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Security Issues ◽  
Computing Platforms

In a cloud ecosystem, most of the data and software that users use reside on the remote server(s), which brings some new challenges for the system, especially security and privacy. At present, these security threats and attacks are the greatest concern for the service providers towards delivering a more secure cloud infrastructure. One of the major concerns is data security, implemented by the most effective means possible and the protection of stored data from unauthorized users and hackers. When considering these security issues, trust is one of the most important means to improve the system’s security and enable interoperability of current heterogeneous cloud computing platforms. The objective of this chapter is to discuss and understand the basic security and privacy challenges of a cloud computing environment as the security of cloud computing is the greatest challenge for delivering a safer cloud environment for both the service providers and the service customers. With this in mind, this chapter will introduce the risks and possible attacks in a cloud computing environment. The major goal is to specify the security risks and attacks and consider trust of cloud service users for delivering a safer and innovation business model.

Software Development Methodology for Cloud Computing and Its Impact

2017 ◽  
pp. 286-307
Author(s):  
Chhabi Rani Panigrahi ◽  
Rajib Mall ◽  
Bibudhendu Pati
Keyword(s):  
Cloud Computing ◽  
Software Development ◽  
Agile Development ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Computing Platform ◽  
Development Methodology ◽  
Development Processes ◽  
Software Development Methodology ◽  
Computing Platforms

This chapter emphasizes mainly on the software development methodology basically agile methods of software development in cloud computing platforms and its impact on software development processes. This chapter also covers the benefits of agile development methodology in cloud computing platform. Along with this all traditional software development phases are analyzed to discuss the differences between the traditional software development processes and software development in cloud computing environment. This chapter also includes a brief description of programming models such as MapReduce, BSPCloud, and Dryad etc. available in the literature to handle big data in SaaS cloud. Finally, we highlight the challenges and future scope of software development process in cloud computing environment.

Information Evaporation: The Migration Of Information To Cloud Computing Platforms

2012 ◽  
Vol 16 (4) ◽  
pp. 291
Author(s):  
David Reavis
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Data Access ◽  
Time Lapse ◽  
Early Years ◽  
Computing Environment ◽  
Central System ◽  
Cloud Computing Environment ◽  
Transmission Distance ◽  
Computing Platforms

The physical location for data used in every organization ebbs and flows as technology improves. In the early years of computing, data were stored on the central system because that was the only choice. As communication technology advanced, a decentralized model became popular and data were stored nearer to the place it would be used. Another leap in telecommunications prompted a move back to centralized data storage, mostly because access speeds allowed the data to be used remotely with minimal time lapse due to transmission distance. The most recent transition for housing data is to move data from various databases, some centralized and some localized, into the cloud. The benefits of moving information to a cloud computing environment have made it attractive to organizations recently. Converting data from one platform to another is done regularly by IT professionals. In each of the transitions described above, data had to be converted in some way and transitions to updated computing platforms are not uncommon. In this paper, the term information evaporation will be used to distinguish the move of information to the cloud from other conversion activities, such as system upgrades or platform transitions. Converting data from a traditional database environment to an Internet-based cloud computing environment requires a different approach to security, attention to avoiding creating information silos, and development of data tags, such as eXtensible Markup Language (XML), to facilitate cross platform data access.

scholarly journals IT obstacles using cloud computer facility that serves European users referring to access shared computing resources using cloud computing using shared computing resources

2016 ◽  
Vol 2 (1) ◽  
pp. 01
Author(s):  
Rajeev Saini ◽  
Bhubneshwar Sharma
Keyword(s):  
Cloud Computing ◽  
Communication Technology ◽  
Data Transmission ◽  
Easy Access ◽  
Computing Environment ◽  
New Era ◽  
Mobile Computing Environment ◽  
Different Types ◽  
Computer Facility ◽  
Shared Computing

cloud computing is the result of the evolution and adoption of existing technologies and paradigms. The goal of cloud computing is to allow users to take bene?t from all of these technologies, without the need for deep knowledge about or expertise with each one of them. The cloud aims to cut costs, and helps the users focus on their core business instead of being impeded by IT obstacles it is a new era of referring to access shared computing resources. On the other hand, wireless sensor networks have been seen as one of the most essential technologies for the 21st century where distributed spatially connected sensor node automatically forms a network for data transmission and receive among them is popularly known as Sensor Network. For security and easy access of data, cloud computing is widely used in distributed/mobile computing environment. This is possible due to miniaturization of communication technology. Many researchers have cited different types of technology in this context.

scholarly journals A Blockchain-Based Trusted Edge Platform in Edge Computing Environment

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2126
Author(s):  
Jinnan Zhang ◽  
Changqi Lu ◽  
Gang Cheng ◽  
Teng Guo ◽  
Jian Kang ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Privacy ◽  
Heterogeneous Data ◽  
Edge Computing ◽  
Security And Privacy ◽  
Computing Environment ◽  
Security Technology ◽  
Communication Time ◽  
Computing Platforms ◽  
Multiple Network

Edge computing is a product of the evolution of IoT and the development of cloud computing technology, providing computing, storage, network, and other infrastructure close to users. Compared with the centralized deployment model of traditional cloud computing, edge computing solves the problems of extended communication time and high convergence traffic, providing better support for low latency and high bandwidth services. With the increasing amount of data generated by users and devices in IoT, security and privacy issues in the edge computing environment have become concerns. Blockchain, a security technology developed rapidly in recent years, has been adopted by many industries, such as finance and insurance. With the edge computing capability, deploying blockchain platforms/applications on edge computing platforms can provide security services for network edge environments. Although there are already solutions for integrating edge computing with blockchain in many IoT application scenarios, they slightly lack scalability, portability, and heterogeneous data processing. In this paper, we propose a trusted edge platform to integrate the edge computing framework and blockchain network for building an edge security environment. The proposed platform aims to preserve the data privacy of the edge computing client. The design based on the microservice architecture makes the platform lighter. To improve the portability of the platform, we introduce the Edgex Foundry framework and design an edge application module on the platform to improve the business capability of Edgex. Simultaneously, we designed a series of well-defined security authentication microservices. These microservices use the Hyperledger Fabric blockchain network to build a reliable security mechanism in the edge environment. Finally, we build an edge computing network using different hardware devices and deploy the trusted edge platform on multiple network nodes. The usability of the proposed platform is demonstrated by testing the round-trip time (RTT) of several important workflows. The experimental results demonstrate that the platform can meet the availability requirements in real-world usage scenarios.

scholarly journals ADRCN: A Framework to Detect and Mitigate Malicious Insider Attacks in Cloud-Based Environment on IaaS

2019 ◽  
Vol 4 (3) ◽  
pp. 654-670
Author(s):  
Priya Oberoi ◽  
Sumit Mittal ◽  
Rajneesh Kumar Gujral
Keyword(s):  
Cloud Computing ◽  
Data Center ◽  
Critical Factor ◽  
Secure Routing ◽  
Symmetric Cryptography ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Security Issues ◽  
Basic Technology ◽  
Computing Platforms

Security is a critical factor for any of the computing platforms. Cloud computing is a new computing environment but still, its basic technology is the Internet. Thus, Cloud computing environment not only has the threats of its own but it is also prone to security issues of its underlying technology i.e. Internet. In this paper, the authors are proposing a secure routing framework viz. Authenticated Dynamic Routing in Cloud Networks (ADRCN) to mitigate the malicious insider attacks while maintaining the path integrity in the Clouds. Symmetric cryptography with hashing is used to maintain the integrity of the path between the source and destination. The purpose of ADRCN is to maintain the integrity of the path between the client and data center. If malicious insider tries to perform an attack between the client and the data center then it will be detected. This work aims to give a solution for detection and prevention of malicious insider attacks in Cloud-based environments.

Software Development Methodology for Cloud Computing and Its Impact

2021 ◽  
pp. 151-172
Author(s):  
Chhabi Rani Panigrahi ◽  
Rajib Mall ◽  
Bibudhendu Pati
Keyword(s):  
Cloud Computing ◽  
Software Development ◽  
Agile Development ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Computing Platform ◽  
Development Methodology ◽  
Development Processes ◽  
Software Development Methodology ◽  
Computing Platforms

This chapter emphasizes mainly on the software development methodology basically agile methods of software development in cloud computing platforms and its impact on software development processes. This chapter also covers the benefits of agile development methodology in cloud computing platform. Along with this all traditional software development phases are analyzed to discuss the differences between the traditional software development processes and software development in cloud computing environment. This chapter also includes a brief description of programming models such as MapReduce, BSPCloud, and Dryad etc. available in the literature to handle big data in SaaS cloud. Finally, we highlight the challenges and future scope of software development process in cloud computing environment.

scholarly journals Successes and Struggles with Computational Reproducibility: Lessons from the Fragile Families Challenge

2019 ◽  
Vol 5 ◽  
pp. 237802311984980 ◽  
Author(s):  
David M. Liu ◽  
Matthew J. Salganik
Keyword(s):  
Cloud Computing ◽  
Web Services ◽  
Fragile Families ◽  
Special Issue ◽  
Computing Environment ◽  
Raw Data ◽  
Academic Researchers ◽  
The Future ◽  
Amazon Web Services ◽  
Software Engineers

Reproducibility is fundamental to science, and an important component of reproducibility is computational reproducibility: the ability of a researcher to recreate the results of a published study 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 article, the authors describe their approach to enabling computational reproducibility for the 12 articles in this special issue of Socius about the Fragile Families Challenge. The 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 made it possible to standardize the computing environment around each submission, which will ease computational reproducibility both today and in the future. Drawing on their successes and struggles, the authors conclude with recommendations to researchers and journals.

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