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Towards a Scalable and Distributed Infrastructure for Deep Learning Applications [article]

Bita Hasheminezhad, Shahrzad Shirzad, Nanmiao Wu, Patrick Diehl, Hannes Schulz, Hartmut Kaiser
2020 arXiv   pre-print
Parallelization approaches and distribution requirements are not considered in the primary designs of most available distributed deep learning frameworks and most of them still are not able to perform  ...  require deep learning frameworks to utilize scaling out techniques.  ...  Acknowledgements The authors are grateful for the support of this work by the LSU Center for Computation & Technology and by the DTIC project: Phylanx Engine Enhancement and Visualizations Development  ... 
arXiv:2010.03012v1 fatcat:2hy7evtvdra2dotv35dvbhv7mu

Table of Contents

2021 2021 IEEE 46th Conference on Local Computer Networks (LCN)  
Radio Networks in the 5G and IoT Era 403 A Vehicle Message Scheduling Scheme for Vehicle Trust Management 407 Distributed Task Migration Optimization in MEC by Deep Reinforcement Learning Strategy  ...  Soil Sensing Through Low-Cost WiFi Sensing Networks 549 Robust Authentication and Data Flow Integrity for P2P SCADA Infrastructures 557 Application Agnostic Container Migration and Failover 565 A Networked  ... 
doi:10.1109/lcn52139.2021.9524933 fatcat:bopsc4l2qrc7bobzfyb6343iou

2021 Index IEEE Transactions on Parallel and Distributed Systems Vol. 32

2022 IEEE Transactions on Parallel and Distributed Systems  
., +, TPDS April 2021 830-841 Distributed and Collective Deep Reinforcement Learning for Computation Offloading: A Practical Perspective.  ...  ., +, TPDS April 2021 830-841 Distributed and Collective Deep Reinforcement Learning for Computation Offloading: A Practical Perspective.  ...  Graph coloring Feluca: A Two-Stage Graph Coloring Algorithm With Color-Centric Paradigm on GPU. Zheng, Z., +,  ... 
doi:10.1109/tpds.2021.3107121 fatcat:e7bh2xssazdrjcpgn64mqh4hb4

Custom Hardware Versus Cloud Computing in Big Data [chapter]

Gaye Lightbody, Fiona Browne, Valeriia Haberland
2017 Advanced Information and Knowledge Processing  
The chapter concludes by looking at some of the state of the art solutions for deep learning platforms in which custom hardware such as FPGAs and Application Specific Integrated Circuits (ASICs) are used  ...  pertinent to creating a market edge for a range of applications.  ...  CNNLab , is another parallel framework for deep learning neural networks that distributes computation to both GPUs and FPGAs.  ... 
doi:10.1007/978-3-319-59090-5_9 fatcat:p74b7a42xvajvbghyugmfpwdga

Orchestrating SDN Control Plane towards Enhanced IoT Security

Tooba Hasan, Akhunzada Adnan, Thanassis Giannetsos, Jahanzaib Malik
2020 2020 6th IEEE Conference on Network Softwarization (NetSoft)  
It is a first step towards a new line of security mechanisms that enables the provision of scalable AI-based intrusion detection focusing on the operational assurance of only those specific, critical infrastructure  ...  In this paper, we propose an SDN-enabled control plane based orchestration that leverages emerging Long Short-Term Memory (LSTM) classification models; a Deep Learning (DL) based architecture to combat  ...  Towards this direction, Deep Learning techniques are used based on LSTMs, RNNs, The employed approach provides a detection rate of 90%.  ... 
doi:10.1109/netsoft48620.2020.9165424 dblp:conf/netsoft/ToobaAGM20 fatcat:dn7iovxdgre47oukvx6rj3rb5y

Towards A Methodology and Framework for Workflow-Driven Team Science [article]

Ilkay Altintas, Shweta Purawat, Daniel Crawl, Alok Singh, Kyle Marcus
2019 arXiv   pre-print
Scientific workflows are powerful tools for management of scalable experiments, often composed of complex tasks running on distributed resources.  ...  We also present a conceptual design towards the development of methodologies and tools for effective workflow-driven collaborations, namely the PPoDS methodology and the SmartFlows Toolkit for smart utilization  ...  and Software Ecosystem Community Infrastructure (CHASE-CI), and NIH P41 GM103426 for National Biomedical Computation Resource (NBCR).  ... 
arXiv:1903.01403v1 fatcat:ubbe6qk5kfb77erhetvp25jele

Tree-based overlay networks for scalable applications

D.C. Arnold, G.D. Pack, B.P. Miller
2006 Proceedings 20th IEEE International Parallel & Distributed Processing Symposium  
We describe many interesting applications and commonly-used algorithms for which TBŌNs are well-suited and provide a new (non-tool) case study, a distributed implementation of the mean-shift algorithm  ...  Tree-based Overlay Networks (TBŌNs) have proven to provide such a model for distributed tools like performance profilers, parallel debuggers, system monitors and system administration tools.  ...  Acknowledgments We are grateful to Sean Murphy and Miron Livny of the Condor Project at the University of Wisconsin who helped tremendously in procuring and setting up our experimental environment, sponsored  ... 
doi:10.1109/ipdps.2006.1639493 dblp:conf/ipps/ArnoldPM06 fatcat:jqubay3d2fcuvcvmok3vq2hxky

CALLISTO: Copernicus Artificial Intelligence Services and data fusion with other distributed data sources and processing at the edge to support DIAS and HPC infrastructures

Stelios Andreadis, Ilias Gialampoukidis, Vasileios Sitokonstantinou, Beatrice Coloru, Han Vervaeren, Eva Lopez, Vasileios Kalogirou, Panagiota Syropoulou, Elias B. Kosmatopoulos, Stefanos Vrochidis, Eliana Li Santi, Guido Vingione (+1 others)
2021 Zenodo  
CALLISTO is a project funded by the European Union's Horizon 2020 Research and Innovation Programme under the topic "Big data technologies and Artificial Intelligence for Copernicus" in 2020 and has started  ...  In this work we introduce the CALLISTO project and describe its concept and approach, the use cases of application and the expected impact.  ...  Machine Learning techniques are also applied for water quality estimation and air quality forecasting, while algorithms are optimised to be executed on HPC infrastructures, when necessary to boost scalability  ... 
doi:10.5281/zenodo.4696649 fatcat:tjrsf3i3yvaihh5or4bzcldgj4

Distributed Training of Deep Neural Networks with Spark: The MareNostrum Experience

Leonel Cruz, Ruben Tous, Beatriz Otero
2019 Pattern Recognition Letters  
Deployment of a distributed deep learning technology stack on a large parallel system is a very complex process, involving the integration and configuration of several layers of both, general-purpose and  ...  This is followed by a discussion about the impact of different configurations including parallelism, storage and networking alternatives, and other aspects related to the execution of deep learning workloads  ...  Acknowledgements This work is partially supported by the Spanish Ministry of Economy and Competitivity under contract TIN2015-65316-P and by the SGR programme (2014-SGR-1051) of the Catalan Government.  ... 
doi:10.1016/j.patrec.2019.01.020 fatcat:47iumueflfdvbdgnml6wux3qyi

Distributed Intelligence on the Edge-to-Cloud Continuum: A Systematic Literature Review

Daniel Rosendo, Alexandru Costan, Patrick Valduriez, Gabriel Antoniu
2022 Journal of Parallel and Distributed Computing  
The explosion of data volumes generated by an increasing number of applications is strongly impacting the evolution of distributed digital infrastructures for data analytics and machine learning (ML).  ...  review aims at providing a comprehensive vision of the main state-of-the-art libraries and frameworks for machine learning and data analytics available today.  ...  Acknowledgments This work was funded by Inria through the HPC-BigData Inria Challenge (IPL) and by French ANR OverFlow project (ANR-15-CE25-0003).  ... 
doi:10.1016/j.jpdc.2022.04.004 fatcat:mopdegh4vrgt5k47vrmc7xum24

ALLIANCE Project: Architecting a Knowledge-Defined 5G-Enabled Network Infrastructure

D. Careglio, S. Spadaro, A. Cabellos, J.A. Lazaro, J. Perello, P. Barlet, J.M. Gene, J. Paillisse
2018 2018 20th International Conference on Transparent Optical Networks (ICTON)  
intra-datacentre network and computational resources into a single converged 5G network infrastructure.  ...  ALLIANCE relies on a Knowledge-Defined Networking (KDN) orchestrator which take advantage of state-of-the-art machine learning techniques to deploy, operate, monitor and troubleshoot networks automatically  ...  ACKNOWLEDGEMENTS This work has been partially funded by the Spanish Ministry of Economy and Competitiveness under contract FEDER TEC2017-90034-C2-R (ALLIANCE project) and by the Generalitat de Catalunya  ... 
doi:10.1109/icton.2018.8473626 fatcat:br7iuvkrn5hgxcpfzdekyc7rii

Big data and cloud computing

Divyakant Agrawal, Sudipto Das, Amr El Abbadi
2011 Proceedings of the 14th International Conference on Extending Database Technology - EDBT/ICDT '11  
Scalable database management systems (DBMS)-both for update intensive application workloads as well as decision support systems for descriptive and deep analytics-are a critical part of the cloud infrastructure  ...  Though scalable data management has been a vision for more than three decades and much research has focussed on large scale data management in traditional enterprise setting, cloud computing brings its  ...  GOALS OF THE TUTORIAL Learning Outcomes Following are the learning outcomes from this tutorial: • State-of-the-art in scalable data management for traditional and cloud computing infrastructures for  ... 
doi:10.1145/1951365.1951432 dblp:conf/edbt/AgrawalDA11 fatcat:q3rhtmgrvjhyrlrw3qpxfwa6da

BigDataGrapes D4.3 - Models and Tools for Predictive Analytics over Extremely Large Datasets

Nicola Tonellotto, Vinicius Monteiro de Lira, Franco Maria Nardini, Raffaele Perego, Cristina Muntean, Ida Mele, Salvatore Trani
2018 Zenodo  
The BDG software stack employs efficient and fault-tolerant tools for distributed processing, aimed at providing scalability and reliability for the target applications.  ...  provided by the Persistence and Processing Layers of the BDG architecture contributed in Deliverable 4.1 "Methods and Tools for Scalable Distributed Processing".  ...  The BDG software stack employs efficient and fault-tolerant tools for distributed processing, aimed at providing scalability and reliability for the target applications.  ... 
doi:10.5281/zenodo.1481800 fatcat:rlqwgvajzre6pfxuiiclmk2r34

S2CE: A Hybrid Cloud and Edge Orchestrator for Mining Exascale Distributed Streams [article]

Nicolas Kourtellis and Herodotos Herodotou and Maciej Grzenda and Piotr Wawrzyniak and Albert Bifet
2020 arXiv   pre-print
S2CE will enable machine and deep learning over voluminous and heterogeneous data streams running on hybrid cloud and edge settings, while offering the necessary functionalities for practical and scalable  ...  To address this need, this paper proposes Stream to Cloud & Edge (S2CE), a first of its kind, optimized, multi-cloud and edge orchestrator, easily configurable, scalable, and extensible.  ...  (O2) Computing at edge for faster, more scalable, energy efficient processing Data/AI/predictive/prescriptive analytics (O3) Using distributed deep and machine learning Stream analytics frameworks  ... 
arXiv:2007.01260v1 fatcat:hfavtqtpmnd2xo5uh7tzcomm4u

Guest Editors' Introduction: Machine Intelligence at the Edge

Luca Benini, Deming Chen, Jinjun Xiong, Zhiru Zhang
2021 IEEE design & test  
• " • EdgeAI: A Vision for Deep Learning in the IoT Era" by Bhardwaj et al.: This article discusses current directions in computation-aware deep learning and describes two new challenges in the IoT era  ...  : 1) data-independent deployment of learning and 2) communication-aware distributed inference.  ... 
doi:10.1109/mdat.2020.3016589 fatcat:6qleywxdg5hjfko6fufwgqbi2y
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