(EDU203) Instructing on the Cloud: Using AWS to Aid Professors and Teach Students | AWS re:Invent 2014
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In the past, academic institutions and departments-primarily those focused on computer science, information systems, or other technology instruction-have made significant use of on-premises servers for labs, projects, and research efforts. Many of these institutions are now migrating from their on-premises environments to the cloud. They are providing computational support for coursework and back-end support for capstone projects, all while enhancing their cloud curriculum to create the next generation of IT innovators. In this session, learn how faculty can implement multi-user environments in the classroom; access AWS assets such as AWS credits, training resources, content, and labs; and collaborate on content creation and shared Amazon Machine Images (AMIs). Hear about best practices and case studies for implementing AWS in the classroom and in academic research. This session is an opportunity for representatives from academia to learn how they can leverage cloud computing to aid them in coursework development and research.
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(EDU203) Instructing on the Cloud: Using AWS to Aid Professors and Teach Students | AWS re:Invent 2014
- 1. November 14, 2014 | Las Vegas, NV Majd Sakr, Carnegie Mellon University, and Ken Eisner, AWS
- 3. Used by over 3,000 educational institutions worldwide
- 8. Whitepapers Videos Blogs •Setting up multi-userenvironments •Developmentand test on AWS •Overview of security processes •AWS security bestpractices •Getting Startedwith AWS •Dev & Test in the AWS Cloud •Building ScalableWeb Apps •Storage and Backup •Case studies on Novartis, Conde Nast, NotreDame, PBS, etc. •Much more … •AWS Big Data Blog •AWS Security Blog •AWS Mobile Development Blog •AWS Application Management Blog
- 10. Certification aws.amazon.com/certification Validate your proven technical expertise with the AWS platform and gain recognition for your skills Videos and Labs aws.amazon.com/training/ self-paced-labs Start working with an AWS service in minutes with free online instructional videos and labs aws.amazon.com/training Instructor-Led Courses Learn how to design, deploy, and operate highly available, cost-effective, and secure applications on AWS
- 11. msakr@cs.cmu.edu
- 12. Location In-Class Online Scale 10s students >100 students Campus Local Global Infrastructure Local Outsourced
- 13. Undergrad(10%) Graduate90% CMU-SV CMU-P CMU-R CMU-A CMU-Q Systems & Infrastructures Services & Applications Course Content 50 170 213 330 Spring 2013 Fall 2013 Spring 2014 Fall 2014
- 14. Piazza Open Learning Initiative Amazon Web Services Video Conf. Cloud platform to implement all projects Access learning content, quizzes and checkpoints Feedback & questions Weekly, in-class recitationsvideotape & video conference
- 15. Overview of Cloud Computing Enabling technologies Service and deployment models Economic models Data Centers Technologies, Design considerations, Power, Cooling, PUE Cloud Resource Sharing CPU Memory I/O Virtualization Cloud Storage Distributed File Systems NoSQL Databases Analytics Engines for the Cloud Hadoop MapReduce Pregel GraphLab
- 16. •Amazon EC2, Amazon S3, SSH, Authentication, Billing, Security Groups… 0. AWS Account Setup & Tool Primer •Amazon EC2, Amazon EMR 1. Big Data Analytics •Amazon EC2, Amazon ELB, Amazon CloudWatch, Amazon SNS 2. Scaling and Elasticity •Amazon EBS, MySQL, HBase, Amazon RDS, Amazon DynamoDB 3. Cloud Storage •Amazon EMR, HBase 4. Analytics Engines for the Cloud •No restrictions on tools or AWS services •Evaluated based on cost and performance 5. A Complete Web Service (Team project)
- 17. DC1 LG ……… DC2 DCn ? Amazon EC2, Elastic Load Balancing, Auto Scaling, Amazon CloudWatch, & Amazon SNS
- 18. Load Generator Phases Duration QueryType Phase1 2weeks Q1,Q2 Phase2 2weeks Q1,Q2,Q3,Q4 Phase2LiveTest 6hours Q1,Q2,Q3,Q4,mix-Q1Q2Q3Q4 Phase3 2weeks Q1,Q2,Q3,Q4,Q5,Q6 Phase3LiveTest 6hours Q1,Q2,Q3,Q4,Q5,Q6,MIX-Q1Q2Q3Q4Q5Q6
- 19. •Develop project learning outcomes •Understand capability of AWS services Develop idea •Identify data set •Design project steps •Develop project •Compare results with insight Design & Development •Data set size •Estimate student effort •Types of resources •Number of resources Tradeoff scale with cost Testing •V1.0 triple tested •V2.0 released Bugs •Release New AMI •Auto Patching AMI
- 20. Eval BashLogs Qs onOLI Codeon S3 AWSCost •Evaluation –Unique questions –Bash logs –AWS expenditure –Code submission •Cheat Checking –Code similarity checkusing MOSS –Log Correlation •Monitor Expenditure –Daily –Per project module (tagging)
- 21. Cheating should be more work than learning
- 22. –10% penalties: 43 in Week 1 0 in Week 8 –100% penalties: 6 in Week 1 0 in Week 8 –A 10% penalty for double the number of attempts is acceptable Full Score = Correctness + Performance + Budget
- 23. –F14: Estimated $150-$200/student Amazon’s Educational Grants + Out of Pocket
- 24. 50 170 213 330 80% 83% 80% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0 50 100 150 200 250 300 350 Spring 2013 Fall 2013 Spring 2014 Fall 2014 Retention % Students ~80% Retention Rate, ~97% Success Rate