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Large-scale analysis of neuroimaging data on commercial clouds with content-aware resource allocation strategies

Massimo Minervini, Cristian Rusu, Mario Damiano, Valter Tucci, Angelo Bifone, Alessandro Gozzi, Sotirios A Tsaftaris
2014 The international journal of high performance computing applications  
Naturally, performing such analyses on the cloud entails a monetary cost, and it is worthwhile identifying strategies that can allocate resources intelligently.  ...  In this paper we use a commercial cloud platform for brain neuroimaging and analysis.  ...  ACKNOWLEDGEMENTS The authors would like to thank Alberto Galbusera for assistance with data collection and PiCloud Inc., particularly Ken Elkabany, for providing technical assistance.  ... 
doi:10.1177/1094342013519483 fatcat:qtchpzss2fhjtikjjfebg5kjmu

Heads in the Cloud: A Primer on Neuroimaging Applications of High Performance Computing

Anwar S. Shatil, Sohail Younas, Hossein Pourreza, Chase R. Figley
2015 Magnetic Resonance Insights  
users can start analyzing their neuroimaging data using cloud resources.  ...  , etc.), a practical overview of the benefits and drawbacks, and a specific focus on how cloud resources can be used for various neuroimaging applications.  ...  Wrote the first draft of the manuscript: ASS, SY, HP, and CRF. Contributed to the writing of the manuscript: ASS, SY, HP, and CRF. Agree with manuscript results and conclusions: ASS, SY, HP, and CRF.  ... 
doi:10.4137/mri.s23558 pmid:27279746 pmcid:PMC4896536 fatcat:6nwtpgdvuncodpa5xs5x2qqzdu

Science in the cloud

Dennis Gannon, Dan Fay, Daron Green, Kenji Takeda, Wenming Yi
2014 Proceedings of the 5th ACM workshop on Scientific cloud computing - ScienceCloud '14  
Microsoft Research is now in its fourth year of awarding Windows Azure cloud resources to the academic community. As of April 2014, over 200 research projects have started.  ...  We also discuss many of the barriers to successfully using commercial cloud platforms in research and ways these problems can be overcome.  ...  Radu Tudoran, Gabriel Antoniu, Bertrand Thirion and Alexandru Costan from INRIA investigated the use of the cloud for joint genetic and neuroimaging data analysis on large cohorts of subjects.  ... 
doi:10.1145/2608029.2608030 dblp:conf/hpdc/GannonFGTY14 fatcat:oqyh3g66njamvdbwltdgf3lxim

Running Neuroimaging Applications on Amazon Web Services: How, When, and at What Cost?

Tara M. Madhyastha, Natalie Koh, Trevor K. M. Day, Moises Hernández-Fernández, Austin Kelley, Daniel J. Peterson, Sabreena Rajan, Karl A. Woelfer, Jonathan Wolf, Thomas J. Grabowski
2017 Frontiers in Neuroinformatics  
Large-scale analysis of neuroimaging data on commercial clouds with content-aware resource allocation strategies. Int. J. High Perform. Comput.  ...  computing clusters, which are similar to platforms commonly used to run large scale neuroimaging applications.  ... 
doi:10.3389/fninf.2017.00063 pmid:29163119 pmcid:PMC5675877 fatcat:bui6h6yzavbujplxwzv23e5lmy

The multi-modal Australian ScienceS Imaging and Visualization Environment (MASSIVE) high performance computing infrastructure: applications in neuroscience and neuroinformatics research

Wojtek J. Goscinski, Paul McIntosh, Ulrich Felzmann, Anton Maksimenko, Christopher J. Hall, Timur Gureyev, Darren Thompson, Andrew Janke, Graham Galloway, Neil E. B. Killeen, Parnesh Raniga, Owen Kaluza (+6 others)
2014 Frontiers in Neuroinformatics  
MASSIVE is a unique Australian facility with a focus on fast data processing, including processing data "in-experiment," large-scale visualization, and analysis of large-cohort and longitudinal research  ...  These endeavors each produce immense volumes of data and are totally reliant on large-scale data processing to uncover new knowledge.  ... 
doi:10.3389/fninf.2014.00030 pmid:24734019 pmcid:PMC3973921 fatcat:liypgnyefnehxmmg52f3cwqw5q

Big data and extreme-scale computing

M Asch, T Moore, R Badia, M Beck, P Beckman, T Bidot, F Bodin, F Cappello, A Choudhary, B de Supinski, E Deelman, J Dongarra (+27 others)
2018 The international journal of high performance computing applications  
and other devices at the networks edge, and the centralized resources of commercial clouds and HPC centers.  ...  Third, we focus on some opportunities for software ecosystem convergence in big, logically centralized facilities that execute large-scale simulations and models and/or perform large-scale data analytics  ...  Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.  ... 
doi:10.1177/1094342018778123 fatcat:vwrrxmad4rhtppq6ioaz4h5q7a

OverFlow: Multi-Site Aware Big Data Management for Scientific Workflows on Clouds

Radu Tudoran, Alexandru Costan, Gabriel Antoniu
2016 IEEE Transactions on Cloud Computing  
The global deployment of cloud datacenters is enabling large scale scientific workflows to improve performance and deliver fast responses.  ...  This unprecedented geographical distribution of the computation is doubled by an increase in the scale of the data handled by such applications, bringing new challenges related to the efficient data management  ...  The Need of site-aware file management for cloud based workflows As we move to the world of Big Data, single-site processing becomes insufficient: large scale scientific workflows can no longer be accommodated  ... 
doi:10.1109/tcc.2015.2440254 fatcat:eobiky7wbnax7ej7aacp5y3qfe

All One Needs to Know about Fog Computing and Related Edge Computing Paradigms

Ashkan Yousefpour, Caleb Fung, Tam Nguyen, Krishna Kadiyala, Fatemeh Jalali, Amirreza Niakanlahiji, Jian Kong, Jason P. Jue
2019 Journal of systems architecture  
With this growth, fog computing, along with its related edge computing paradigms, such as multi-access edge computing (MEC) and cloudlet, are seen as promising solutions for handling the large volume of  ...  With the Internet of Things (IoT) becoming part of our daily life and our environment, we expect rapid growth in the number of connected devices.  ...  SDN is mostly commercially viable inside large data centers or campus networks [9] .  ... 
doi:10.1016/j.sysarc.2019.02.009 fatcat:udonbl6rerfwdap2psex7ryloa

Federated Learning for Smart Healthcare: A Survey [article]

Dinh C. Nguyen, Quoc-Viet Pham, Pubudu N. Pathirana, Ming Ding, Aruna Seneviratne, Zihuai Lin, Octavia A. Dobre, Won-Joo Hwang
2021 arXiv   pre-print
The recent FL designs for smart healthcare are then discussed, ranging from resource-aware FL, secure and privacy-aware FL to incentive FL and personalized FL.  ...  Subsequently, we provide a state-of-the-art review on the emerging applications of FL in key healthcare domains, including health data management, remote health monitoring, medical imaging, and COVID-19  ...  Simulation results show that the proposed model can boost neuroimage analysis performances and find reliable disease-related biomarkers by using multi-site data without data sharing.  ... 
arXiv:2111.08834v1 fatcat:jmex4e25rbgy3bk67iolrj4uee

Jupyter meets the Earth: Enabling discovery in geoscience through interactive computing at scale

Fernando Pérez, Joseph Hamman, Laurel Larsen, Kevin Paul, Lindsey Heagy, Christopher Holdgraf, Yuvi Panda
2019 Zenodo  
The proposal narrative for NSF Award 1928406 which is a part of the "EarthCube: Developing a Community-Driven Data and Knowledge Environment for the Geosciences" program.  ...  Pérez is also co-PI on TRIPODS: Berkeley Institute on the Foundations of Data Analysis, NSF 1740855, a currently active project with a focus on methodological development for data science.  ...  The culmination of the project will be a robust new software toolkit for climate science at scale.  ... 
doi:10.5281/zenodo.3369938 fatcat:nzblhpru3vhmboxd4zq5hc74uu

Manifesto for an international digital mental health network

Eduard Maron, David S. Baldwin, Roman Balõtšev, Chiara Fabbri, Vikas Gaur, Diego Hidalgo-Mazzei, Sean Hood, Martti Juhola, Olli Kampman, Siegfried Kasper, Hilkka Kärkkäinen, Klára Látalová (+22 others)
2019 Digital Psychiatry  
This consensus statement summarizes the consortium's vision and strategy for further development of digital mental health. ARTICLE HISTORY  ...  Current mental health services across the world remain expert-centric and are based on traditional workflows, mostly using impractical and ineffective electronic record systems or even paper-based documentation  ...  We will establish dialog between actors involved in official agreements and resource allocation and proceed with legalization processes to enable the integration of i-PROACH with healthcare databases and  ... 
doi:10.1080/2575517x.2019.1617575 fatcat:jvkgmcc37bet3cf5wptbdziw6u

Mobile Edge Computing: A Survey

Nasir Abbas, Yan Zhang, Amir Taherkordi, Tor Skeie
2018 IEEE Internet of Things Journal  
Impact of MEC integration with the traditional mobile and cloud network appears in this paper. A survey has been presented that contributes in general understanding of mobile edge computing (MEC).  ...  To address these challenges, first off need to understand the network infrastructure of MEC, cloud and cellular network.  ...  LTE bottleneck occurs due to large number of IoT devices memory allocation to the backend cloud servers.  ... 
doi:10.1109/jiot.2017.2750180 fatcat:j3llgr6fjjbjfhsne2pfst4v74

Image Steganography Using HBC and RDH Technique

Hemalatha M, Prasanna A, Dinesh Kumar R, Vinothkumar D
2014 International Journal of Computer Applications Technology and Research  
The receiver should be aware of the keys that are used at the corners while encrypting the image.  ...  With these methods the performance of the stegnographic technique is improved in terms of PSNR value.  ...  ACKNOWLEDGEMENTS Our thanks to the colleague Lecturers of Computer Science department Federal College of Education (Technical) Potiskum for their contributions towards development of the paper.  ... 
doi:10.7753/ijcatr0303.1001 fatcat:4i6tujs4oje2tnxf5c25eh26x4

Data sharing in the sciences

Stacy Kowalczyk, Kalpana Shankar
2011 Annual Review of Information Science and Technology  
Acknowledgments We would like to thank Heather Piwowar for sharing some of her bibliographic resources with us, Shannon Oltmann for her careful editing and thoughtful comments, the anonymous reviewers  ...  protection issues, and resource allocation.  ...  Securing large-scale funding can often mean sharing among a large number of partners, with each receiving a small amount.  ... 
doi:10.1002/aris.2011.1440450113 fatcat:z62ftfrsezhhjdzrqo373mu7oy

2018-2020 Index Proceedings of the IEEE Vol. 106-108

2020 Proceedings of the IEEE  
., +, JPROC Jan. 2020 30-50 Deep-Learning-Based Wireless Resource Allocation With Application to Vehicular Networks.  ...  ., +, JPROC Feb. 2019 350-363 Toward Cloud-Assisted Industrial IoT Platform for Large-Scale Continuous Condition Monitoring.  ...  ., JPROC Jan. 2018 201-208 Analog Computer Development in Germany With a Focus on Telefunken [Scanning our Past] . Ulmann, B., 1763 -1771 Allerhand, A.,  ... 
doi:10.1109/jproc.2020.3040096 fatcat:35vqtzlkgjhzdhds5dbqyccesy
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