Application of Cloud-Based Simulation in Scientific Research

2014 ◽  
pp. 281-307
Author(s):  
Mihailo Marinković ◽  
Sava Čavoški ◽  
Aleksandar Marković
Keyword(s):  
Cloud Computing ◽  
Computer Simulations ◽  
Scientific Research ◽  
Simulation Models ◽  
Cloud Environment ◽  
Review Of The Literature ◽  
Simulation Experiments ◽  
Software Infrastructure ◽  
Computing Platforms ◽  
Computationally Intensive

This chapter is a review of the literature related to the use of cloud-based computer simulations in scientific research. The authors examine the types and good examples of cloud-based computer simulations, offering suggestions for the architecture, frameworks, and runtime infrastructures that support running simulations in cloud environment. Cloud computing has become the standard for providing hardware and software infrastructure. Using the possibilities offered by cloud computing platforms, researchers can efficiently use the already existing IT resources in solving computationally intensive scientific problems. Further on, the authors emphasize the possibilities of using the existing and already known simulation models and tools in the cloud computing environment. The cloud environment provides possibilities to execute all kinds of simulation experiments as in traditional environments. This way, models are accessible to a wider range of researchers and the analysis of data resulting from simulation experiments is significantly improved.

Application of Cloud-Based Simulation in Scientific Research

2015 ◽  
pp. 490-516
Author(s):  
Mihailo Marinković ◽  
Sava Čavoški ◽  
Aleksandar Marković
Keyword(s):  
Cloud Computing ◽  
Computer Simulations ◽  
Scientific Research ◽  
Simulation Models ◽  
Cloud Environment ◽  
Review Of The Literature ◽  
Simulation Experiments ◽  
Software Infrastructure ◽  
Computing Platforms ◽  
Computationally Intensive

This chapter is a review of the literature related to the use of cloud-based computer simulations in scientific research. The authors examine the types and good examples of cloud-based computer simulations, offering suggestions for the architecture, frameworks, and runtime infrastructures that support running simulations in cloud environment. Cloud computing has become the standard for providing hardware and software infrastructure. Using the possibilities offered by cloud computing platforms, researchers can efficiently use the already existing IT resources in solving computationally intensive scientific problems. Further on, the authors emphasize the possibilities of using the existing and already known simulation models and tools in the cloud computing environment. The cloud environment provides possibilities to execute all kinds of simulation experiments as in traditional environments. This way, models are accessible to a wider range of researchers and the analysis of data resulting from simulation experiments is significantly improved.

Towards cloud-native simulations – lessons learned from the front-line of cloud computing

2020 ◽  
pp. 154851291989532 ◽  
Author(s):  
Nane Kratzke ◽  
Robert Siegfried
Keyword(s):  
Cloud Computing ◽  
Cost Model ◽  
Simulation Models ◽  
Lessons Learned ◽  
Fine Grained ◽  
Architectural Styles ◽  
Research Activities ◽  
Simulation Domain ◽  
Software Prototyping ◽  
Computationally Intensive

Cloud computing can be a game-changer for computationally intensive tasks like simulations. The computational power of Amazon, Google, or Microsoft is even available to a single researcher. However, the pay-as-you-go cost model of cloud computing influences how cloud-native systems are being built. We transfer these insights to the simulation domain. The major contributions of this paper are twofold: (A) we propose a cloud-native simulation stack and (B) derive expectable software engineering trends for cloud-native simulation services. Our insights are based on systematic mapping studies on cloud-native applications, a review of cloud standards, action research activities with cloud engineering practitioners, and corresponding software prototyping activities. Two major trends have dominated cloud computing over the last 10 years. The size of deployment units has been minimized and corresponding architectural styles prefer more fine-grained service decompositions of independently deployable and horizontally scalable services. We forecast similar trends for cloud-native simulation architectures. These similar trends should make cloud-native simulation services more microservice-like, which are composable but just “simulate one thing well.” However, merely transferring existing simulation models to the cloud can result in significantly higher costs. One critical insight of our (and other) research is that cloud-native systems should follow cloud-native architecture principles to leverage the most out of the pay-as-you-go cost model.

scholarly journals AN OVERVIEW OF CLOUD COMPUTING: PLATFORMS AND SECURITY ISSUES

2018 ◽  
Vol 5 (43) ◽  
Author(s):  
Paramjeet Kaur
Keyword(s):  
Cloud Computing ◽  
Capital Investment ◽  
Scale Up ◽  
Cloud Provider ◽  
It Services ◽  
Cloud Environment ◽  
Large Organization ◽  
Security Issues ◽  
User Data ◽  
Computing Platforms

Cloud computing is a new computing model which is widely emerging technology in the recent years is adopted by most of the IT companies and other organizations. Cloud computing enables individuals and organizations to gain access to huge computing resources without capital investment. Cloud computing is a set of IT services that are provided to a customer over a network on a leased basis and with the ability to scale up or down their service requirements. Cloud computing is the internet depend technology which is providing the services to user, small and large organization on demand. Cloud computing stored the user data and maintain in the data canter of cloud provider like Amazon, Oracle, Google, Microsoft etc. However, the cloud environment is considered untrusted as it is accessed through Internet. Therefore people have security concerns on data stored in cloud environment. The major concern of cloud environment is security during upload the data on cloud server.

scholarly journals Trusted Supply Chains: Surveying Competitive Value Of The Cloud

2015 ◽  
Vol 19 (1) ◽  
pp. 43
Author(s):  
Rajni Goel
Keyword(s):  
Cloud Computing ◽  
Supply Chain ◽  
Supply Chain Network ◽  
Cloud Environment ◽  
Strategic Integration ◽  
Supply Chain Networks ◽  
Innovation Potential ◽  
Cloud Computing Service ◽  
Computing Platforms ◽  
Shared Computing

Cloud computing has become a force multiplier for organizations as they realize the benefit from the shared computing platforms and services offered by cloud computing. Providers market shared computing platforms and services because of their convenience, dynamism, elasticity, and scalability to meet the growing demands of organizations, specifically in widespread supply chain networks. Yet, the issuance of trust has become a concern in the cloud web as cloud computing service technologies advance faster than measures to secure it. This research presents a framework to determine which specific supply chain functions can derive the most value from cloud capabilities and to understand how to leverage these technologies strategically to develop a competitive advantage. It proposes a strategic integration of cloud functionalities to create profitable supply chain network partnerships and to improve the processes, quality and innovation potential in the overall Supply Chain Management (SCM), while maintaining a trusted cloud environment.

Agent-Based Simulation Modeling

2016 ◽  
pp. 128-139
Author(s):  
Marco Valente
Keyword(s):  
Computer Simulations ◽  
Methodological Approach ◽  
Scientific Research ◽  
Simulation Models ◽  
Basic Unit ◽  
Full Potential ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Investigative Tool ◽  
Simulation Results

Computer simulations are a powerful tool for scientific research, but lack an accepted methodology for their use, and consequently their results are generally received with skepticisms. This chapter proposes a methodological approach allowing to formally unify the treatment of “traditional” quantitative phenomena with that of phenomena from economics or biology that prevent a universal adoption of data-centered methods. We propose to adopt the explanation as the basic unit of knowledge, which is able to cover all possible cases. From this assumption, we can derive the conclusion that simulation models fail to deliver their full potential as scientific investigative tool because their implementations lack crucial details on the intermediate steps producing simulation results.

A Review on Load Balancing Model Using Best Partition Technique

2017 ◽  
Vol 7 (8) ◽  
pp. 284
Author(s):  
M. Chaitanya ◽  
K. Durga Charan
Keyword(s):  
Cloud Computing ◽  
Fault Tolerance ◽  
Load Balancing ◽  
Load Balance ◽  
Large Impact ◽  
Cloud Environment ◽  
Public Cloud ◽  
The Public ◽  
Partition Technique ◽  
Textual Content

Load balancing makes cloud computing greater knowledgeable and could increase client pleasure. At reward cloud computing is among the all most systems which offer garage of expertise in very lowers charge and available all the time over the net. However, it has extra vital hassle like security, load administration and fault tolerance. Load balancing inside the cloud computing surroundings has a large impact at the presentation. The set of regulations relates the sport idea to the load balancing manner to amplify the abilties in the public cloud environment. This textual content pronounces an extended load balance mannequin for the majority cloud concentrated on the cloud segregating proposal with a swap mechanism to select specific strategies for great occasions.

Workflow Scheduling for Scientific Application in Homogeneous Cloud Environment

2017 ◽  
Vol 7 (7) ◽  
pp. 137
Author(s):  
. Monika ◽  
Pardeep Kumar ◽  
Sanjay Tyagi
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Large Data ◽  
Workflow Scheduling ◽  
Cloud Environment ◽  
Computing Environment ◽  
Scientific Application ◽  
Cloud Computing Environment ◽  
Flow Of Information

In Cloud computing environment QoS i.e. Quality-of-Service and cost is the key element that to be take care of. As, today in the era of big data, the data must be handled properly while satisfying the request. In such case, while handling request of large data or for scientific applications request, flow of information must be sustained. In this paper, a brief introduction of workflow scheduling is given and also a detailed survey of various scheduling algorithms is performed using various parameter.

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
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