1. CEG7380 Cloud Computing Lecture 1
Keke Chen

2. Outline Syllabus Introduction Scope of this course Tentative schedule
Prerequisites
Resources
Assignments
Introduction

3. Scope of this course Understand the basic ideas of cloud computing
Get familiar with
Tools
Systems
Expose to some research topics

4. Two major parts: Processing large data with the cloud
Scaling up/down web applications with the cloud
Note: some programming parts need self-study

5. Prerequisites Some programming skills Sufficient knowledge about
Java, python, shell
Comfortable with learning new programming frameworks
Sufficient knowledge about
Data structure and databases
Operating systems
Distributed systems

6. Assignments and Grading
Reading papers (~3) (10%)
Some miniprojects (4~5) (60%)
Help you master the concepts
Learn to use tools and systems
Self-motivated research projects are strongly encouraged!
Final exam (20%)
Class attendance and discussion (10%)

7. Resources updated reference list Inhouse hadoop cluster AWS access
coupon code for each student
Pilot
Submitting reading assignments and projects

8. Tentative Schedule Parallel data processing Cloud infrastructures
Distributed file systems (GFS, HDFS)
MapReduce
High-level distributed data management
Cloud infrastructures
Virtualization
AWS and Eucalyptus
Interactive front-end – Google App Engine
Cloud security and privacy
Research topics

9. In projects, we will learn to use
Hadoop
Mapreduce, Pig Latin
AWS
google app engine

10. Cloud Computing lecture 1-2
Some slides are borrowed from UC Berkeley RAD Lab
Keke Chen

11. Outline What is cloud computing? Why now? Cloud killer applications
Cloud economics
Challenges and opportunities
“above the cloud”
“Clairemont Report”

12. What is Cloud Computing?
Old idea: Software as a Service (SaaS)
Def: delivering applications over the Internet
Recently: “[Hardware, Infrastrucuture, Platform] as a service”
Utility Computing: pay-as-you-use computing
Illusion of infinite resources
No up-front cost
Fine-grained billing (e.g. hourly)

13. Cloud computing vs. grid computing
Cloud computing = virtualization+ grid + services + utility computing
Grid computing: resource provisioning, load balancing, parallel processing
Views of different users
System admin/hadoop users: grid
Application owners/service users: service, utility

14. Users and cloud providers

15. Why Now?
Experience with very large datacenters – profitable for cloud providers
economics of scale
Pervasive broadband Internet
Fast x86 virtualization
Pay-as-you-go billing model
Large user base
Online payment
Online Ads
Content distribution
 Web 2.0 lowers the entry point to e-business  more small e-business owners
 Large user base of clouds

16. Spectrum of Clouds Instruction Set VM (Amazon EC2, 3Tera)
Bytecode VM (Microsoft Azure)
Framework VM
Google AppEngine, Force.com
Lower-level,
Less management
Higher-level,
More management
EC2
Azure
AppEngine
Force.com

17. Cloud Killer Apps Mobile and web applications
Batch processing / MapReduce
Data analytics (big data)
E.g., OLAP, data mining, machine learning
Extensions of desktop software
Matlab, Mathematica

18. Data center in the cloud
Cloud Economics
Pay by use instead of provisioning for peak
Demand
Capacity
Time
Resources
Demand
Capacity
Time
Resources
Unused resources
Static data center
Data center in the cloud

19. Economics of Cloud Users
Risk of over-provisioning: underutilization
Demand
Capacity
Time
Resources
Unused resources
Static data center

20. Economics of Cloud Users
Heavy penalty for under-provisioning
Resources
Demand
Capacity
Time (days) 1 2 3
Resources
Demand
Capacity
Time (days) 1 2 3
Lost revenue
Resources
Demand
Capacity
Time (days) 1 2 3
Lost users

21. Economics of Cloud Providers
5-7x economies of scale [Hamilton 2008]
Extra benefits
Amazon: utilize off-peak capacity
Microsoft: sell .NET tools
Google: reuse existing infrastructure
Resource
Cost in
Medium DC
Very Large DC
Ratio
Network
$95 / Mbps / month
$13 / Mbps / month
7.1x
Storage
$2.20 / GB / month
$0.40 / GB / month
5.7x
Administration
≈140 servers/admin
>1000 servers/admin

22. Adoption Challenges Challenge Opportunity Availability
Multiple providers & DCs
Data lock-in
Standardization
Data Confidentiality, Auditability, and privacy
Encryption, VLANs, Firewalls; Geographical Data Storage; Privacy preserving data outsourcing

23. Growth Challenges Challenge Opportunity Data transfer bottlenecks
FedEx-ing disks, Data Backup/Archival
Performance unpredictability
Improved VM support, flash memory, scheduling VMs
Scalable storage
Invent scalable store
Bugs in large distributed systems
Invent Debugger that relies on Distributed VMs
Scaling quickly
Invent Auto-Scaler that relies on ML; Snapshots

24. Policy and Business Challenges
Opportunity
Reputation Fate Sharing
Offer reputation-guarding services like those for
Software Licensing
Pay-for-use licenses; Bulk use sales

25. Research Challenges Mentioned by Database Community (Claremont Report)

26. Functionality and operational cost
Background: compare massive-scale data intensive computing systems with today’s DBMS
Limited functionality
Simple APIs (e.g. mapreduce)
Pushes more burden on developers
Benefits
Easier to manage
Lower operational cost
Service Level Agreement (SLA) that is hard to provide for a SQL DBMS
P.S. DB Systems are notorious for their expenses in installation and maintenance.

27. Manageability Features of cloud systems
Limited human intervention
High variance workloads
A variety of shared infrastructures
No DBAs or Administrators to assist developers
Systems need to do work automatically
Self-managing
Adaptive (autonomous) computing

28. Data security and privacy
Users sharing physical resources in a cloud
Protect from each other (security)
Protect from curious cloud providers (privacy)
Successes may depend on specific target usage scenarios
Examples
Query based services
Mining based services

29. Datasets over multiple clouds
Interesting datasets might be available in different clouds
Different cloud providers
Private or public clouds
Services mashing up datasets
Inevitably crossing clouds
Federated cloud architectures

30. Algorithms on Big data Working on “Big Data”
Data mining
Machine learning
Visualization
Traditionally assume data is in
flat files or relational databases
Distributed data organization puts new challenges
Redesign algorithms
Redesign frameworks