Topic - Cloud Computing with Big Data
Big Data and Cloud Computing
About
This workshop exposes students to both the theory and practice of cloud computing. The learning objectives include understanding of key principles of cloud computing concepts, models, technologies and its application for big data. For projects, students will learn to develop web-based big data cloud applications.
Topics include:
Cloud-based Big Data Projects - The learning outcome
To design a big data application and to develop its implementation on a public cloud. A hackathon-like approach will be adopted to allow students to suggest ideas and form teams based on individual interests and skills. Two 3-hr lectures cover programming PaaS and SaaS IBM cloud services and pattern-based approach to design and implement big data applications. Students learn by examples with hands-on laboratories. For data, students will tap on the rich Singapore Smart Nation Open Government Data repositories.
Prerequisites
Knowledge of programming is compulsory; web programming experience will be useful.
Topics include:
- principles of cloud computing
- key business drivers
- basic concepts and terminology
- technical and non-technical challenges; fundamental concepts and models
- reference architecture
- cloud deployment models; technologies behind cloud computing
- main components in a datacenter
- virtualization
- multitenancy; cloud architecture
- how to operate/manage resources to meet certain objectives
- cloud bursting; cloud applications and paradigms
- challenges in developing applications
- application development models – IaaS (Infrastructure as a Service)
- PaaS (Platform as a Service) and SaaS (Software as a Service)
- MapReduce programming model.
Cloud-based Big Data Projects - The learning outcome
To design a big data application and to develop its implementation on a public cloud. A hackathon-like approach will be adopted to allow students to suggest ideas and form teams based on individual interests and skills. Two 3-hr lectures cover programming PaaS and SaaS IBM cloud services and pattern-based approach to design and implement big data applications. Students learn by examples with hands-on laboratories. For data, students will tap on the rich Singapore Smart Nation Open Government Data repositories.
Prerequisites
Knowledge of programming is compulsory; web programming experience will be useful.
Professor TEO Yong Meng
Department of Computer Science, School of Computing and an Affiliate Professor at the NUS Business Analytics Centre, NUS
Department of Computer Science, School of Computing and an Affiliate Professor at the NUS Business Analytics Centre, NUS
Introduction
Professor Teo was a Visiting Professor at the Chinese Academy of Science in China from 2010-2014. He received his PhD and MSc in Computer Science from the University of Manchester in UK.
Professor Teo's recent research focuses on the performance of heterogeneous systems and formalizing emergence in complex systems. Among the awards he won the Wintersim Best Applied Paper Award in 2015, Best Paper Award at the 10th International Conference on Algorithms and Architectures for Parallel Processing in 2010.
At the School of Computing, he is the leader for Systems and Network Research. He has received various research grants including European Commission, Fujitsu Computers (Singapore) Pte Ltd, Fujitsu Laboratories Ltd (Japan), Sun Microsystems/Oracle (USA), NVIDIA and PSA Corporation.
Professor Teo's recent research focuses on the performance of heterogeneous systems and formalizing emergence in complex systems. Among the awards he won the Wintersim Best Applied Paper Award in 2015, Best Paper Award at the 10th International Conference on Algorithms and Architectures for Parallel Processing in 2010.
At the School of Computing, he is the leader for Systems and Network Research. He has received various research grants including European Commission, Fujitsu Computers (Singapore) Pte Ltd, Fujitsu Laboratories Ltd (Japan), Sun Microsystems/Oracle (USA), NVIDIA and PSA Corporation.
CONTACT
Want to know more ? You can contact us.
Email: sws@comp.nus.edu.sg
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