The document provides an overview of cloud computing, covering its services, characteristics, benefits, and drawbacks. It explains different models like SaaS, PaaS, and IaaS along with their examples and compares their pros and cons. Additionally, it discusses programming models like MapReduce used for processing large datasets in distributed systems.

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