1. Cloud Computing
CS4262 Distributed Systems
Dilum Bandara
Dilum.Bandara@uom.lk

2. Cloud Computing
2
Clients
Other
Cloud Services
Govt.
Cloud Services
Private
Cloud
Cloud
Manager
Public Cloud
Source: Green Cloud Computing by Dr. Rajkumar Buyya

3. Cloud Computing (Cont.)
 Variety of services available over Internet that
deliver compute functionality on service
provider’s infrastructure
 Umbrella term
 Computing as a utility
 Pay as you go model
3
Source: www.free-power-point-templates.com/articles/best-
cloud-computing-powerpoint-templates/

4. Cloud Computing Characteristics
 Massive scale
 Rapid elasticity
 Resource pool
 Virtualization
 On demand
 Resilient computing
 Broad network access
 Service orientation
 Geographic distribution
 Homogeneity 4
Hardware
OS
App App App
Hypervisor
OS OS
Virtualized Stack

5. Cloud Computing – Pros & Cons
5

6. Cloud Computing – Pros & Cons (Cont.)
 Reduced cost
 5.7 times reduction in storage costs
 7.1 times reduction in administrative costs
 7.3 times reduction in networking costs
 No upfront investment
 Better performance
 Rapid scalability
 Access to latest version
 Global distribution
 Device independent
 More secure than having your own server rack
6Source – Green Cloud Computing by Dr. Rajkumar Buyya

7. Cloud Computing – Pros & Cons (Cont.)
 Cons
 Need high-bandwidth links
 Lower control & security concerns
 Low performance
 Web-based applications aren’t the fastest
 Interoperability
 Deployment specific software
7

8. Cloud Computing – Levels
Cloud Computing =
Software as a Service
+ Platform as a Service
+ Infrastructure as a Service
+ Data as a Service
8

9. Software as a Service (SaaS)
 Examples
 Google apps, O365, Salesforce.com (CRM)
 Pros
 Availability
 When & where you need them
 Cost reduction
 No up front costs
 Access to the latest version
 Cons
 Lack of control
 Lower customizability
9

10. Platform as a Service (PaaS)
 Examples
 Google app engine, Windows Azure, Heroku
 Pros
 Rapid development
 Better control
 Cost reduction
 Access to latest version
 Cons
 Relatively lower customizability
10

11. Infrastructure as a Service (IaaS)
 Examples
 Amazon, Rackspace, Akamai, SLT
 Pros
 Better control
 High customizability
 Cons
 Administration overhead
 High upfront cost, if application is built using
commercial software/OS
11

12. Delivered Using Warehouse-Scale
Computers (WSC)
12
www.laserfocusworld.com/articles/print/volume-48/issue-
12/features/optical-technologies-scale-the-datacenter.html http://www.slashgear.com/google-data-center-hd-photos-
hit-where-the-internet-lives-gallery-17252451/

13. WSC (Cont.)
13

14. Design Factors for WSC
 Cost-performance
 Small savings add up
 Energy efficiency
 Affects power distribution & cooling
 Work per joule
 Operational costs count
 Power consumption is a primary, constraint when
designing a system
 Dependability via redundancy
 Many low-cost components
14

15. Design Factors (Cont.)
 Network I/O
 Interactive & batch processing workloads
 Web search – interactive
 Web indexing – batch
 Ample computational parallelism isn’t important
 Most jobs are totally independent, “Request-level
parallelism”
 Scale – Its opportunities & problems
 Can afford to build customized systems as WSC
require volume purchase
 Frequent failures
15

16. Programming Models & Workloads
 Batch processing framework
– MapReduce
 Map
 Applies a programmer-
supplied function to each
logical input record
 Runs on thousands of
computers
 Provides new set of (key,
value) pairs as intermediate
values
 Reduce
 Collapses values using
another function 16
Source:
www.cbsolution.net/techniques/ontarget/
mapreduce_vs_data_warehouse

17. Divide & Conquer
17
“Work”
w1 w2 w3
r1 r2 r3
“Result”
“worker” “worker” “worker”
Partition
Combine
Source – “What is Cloud Computing? (and an intro to parallel/distributed
processing) “by Jimmy Lin, The iSchool, University of Maryland

18. Map-Reduce (Contd.)
 Map-reduce support is provided by a function
like following
 Y map-reduce(mapfn, reducefn, List<X>)
 Map reduce implementation takes list of inputs
(list) & does following:
 Apply map function to each entry in the list, which
emit (key, value) pairs
 Collect results, group them by keys, & then pass them
to reduce function as an array
18

19. Map-Reduce (Contd.)
19
Source: www.datasciencecentral.com/profiles/blogs/practical-
illustration-of-map-reduce-hadoop-style-on-real-data

20. Applications of Map-Reduce
 Frequency distribution of word occurrences
 Building inverted index of a search engine
 Sorting
 Stitch Imagery
 Google maps
 Data clustering
 Data analytics & business intelligence
20

21. Map-Reduce for Word Counting
21
Source: http://xiaochongzhang.me/blog/?p=338
How to do this for a large dataset using a distributed system?

22. Example – Word Count
Map(docId, text):
for all terms t in text
emit(t, 1);
Reduce(t, values[])
int sum = 0;
for all values v
sum += v;
emit(t, sum);
22

23. In Class Activity
1. Identify missing card(s)
2. Card sorting
3. Card sorting with 2 rounds
23
Inspired by Marcio Silva's “The MapReduce Card Game” at
http://blog.marciosilva.com/2012/10/the-mapreduce-card-game.html

24. Why Map-Reduce?
 Implementing same pattern in a distributed
system isn’t that easy
 Need to worry about communication, failures,
initialization, etc.
 MapReduce frameworks worry about all those
 You write map & reduce functions & call
framework
 It forces you to think parallel in design time
 It gives you a higher-level of abstraction to think in
 It’s very generic, & covers lot of usecases
 See http://wiki.apache.org/hadoop/PoweredBy
24

25. MapReduce Execution
25
Source: Dean et. al.,
“MapReduce, OSDI, 2004

26. Amazon Web Services
 Virtual machines – XEN
 Very low cost
 $ 0.10 per hour per instance
 Primary rely on open source software
 No (initial) service guarantees
 No contract required
 Amazon EC2
 Elastic Computer Cloud
 Amazon S3
 Simple Storage Service
26

27. Amazon Web Services – Example
27www.ryhug.com/free-art-available-on-amazon-amazon-web-services-that-is/

28. Cloud Computing Middleware
28
Openstack basic architecture

29. OpenStack Architecture
29
11 components
1229 parameters

30. CloudStack Architecture
30

31. CloudStack Architecture (Cont.)
31
Source: https://cwiki.apache.org/confluence/display/CLOUDSTACK/Original+Feature+Spec

32. Source: www.suse.com/documentation/sles-
12/book_virt/data/sec_kvm_intro_arch.html
Hypervisors
32
Source: http://wiki.xen.org/wiki/Xen_Project_Software_Overview

33. Xen vs. KVM
33
Source: http://dtrace.org/blogs/brendan/2013/01/11/virtualization-performance-zones-kvm-xen/

34. Challenges
 Getting large volume of data in/out
 Bandwidth aggregation
 Lack/lower QoS
 SLAs are too simplistic
 Deployment times are in 10s of seconds to
minutes
 Distributed cloud
 Lack of control
 Security, privacy, & ownership concerns
 Policy issues
34