The document discusses cloud computing, defining it as a scalable model that offers various service models like IaaS, PaaS, and SaaS, along with deployment options such as private, community, public, and hybrid clouds. It highlights benefits like cost reduction, scalability, and efficiency in scenarios such as mobile backends and financial services, while detailing Amazon Web Services' capabilities including Elastic Compute Cloud, relational databases, and auto-scaling features. The discussion also emphasizes the importance of stateless development and the continuous evolution of cloud platforms.

1. Cloud Computing
Challenges and Opportunities of
Moving to Cloud Based Computing
Arron Clague Owen Cutajar
Bsc(Hons) MBCS
Intelligence Ltd
Synapse Consulting
Email : owen@cutajar.net
Email :
arron@synapse-consulting.com Twitter : OwenC
Twitter : arroncx Skype: owencutajar
http://www.synapsecn.com http://intelligence.im

2. Defining Cloud Computing

3. Defining Cloud Computing
Cloud computing is a model for building out
loosely coupled systems which run at scale,
with elastic resourcing properties.

4. Defining Cloud Computing
Service Models
•Infrastructure as a Service (IaaS)
•Platform as a Service (PaaS)
•Software as a Service (SaaS)
Deployment Models
•Private Cloud
•Community Cloud
•Public Cloud
•Hybrid Cloud

5. Elasticity and Scalability
Allocated Load
Not Enough IT-capacities Forecast
Power
IT CAPACITY
Too Much
Power
Actual Load
TIME

6. How the Cloud can help
• Reduce upfront cost (no CapEx)
• Reduce ongoing expenses
• JIT infrastructure if things go well (or not)
• Efficiently test multiple scenarios
• Leverage repeatable “web scale” patterns

7. Some Use Cases
• Mobile backends
• Campaign Support Apps
• Geographically diverse apps
• Examples
1. The New York Times used 100 Amazon EC2 instances and a Hadoop
application to process 4 TB of raw image TIFF data (stored in S3) into 11
million finished PDFs in the space of 24 hours at a computation cost of
about $240 (not including bandwidth).
2. Scale to demand (next slide)

8. Scale to demand-Financial Services

9. Current playing field
• Microsoft Azure
• Amazon Web Services
• Google Cloud Platform
• VMware vCloud/vFabric/Cloud Foundry

10. Azure Deep Dive

11. Windows Azure provides a ‘pay-as-you-go’ scale out application platform
Intelligent Network Load Balancer
Stateless Web and/or Application Servers
Stateless ‘Worker’ Machines
State Key/Value Partitioned Shared
Queues
Tier Datastores RDBMS Filesystem

12. Representational State Transfer

14. Windows Azure
Enterprise

18. Service Bus
Client
Service

20. VMware vFabric

22. AWS Service Overview

23. AWS Global Infrastructure

24. Simple Storage Service
• Cloud based file storage
• Simple API to CRUD
• Unlimited capacity
• Excellent for static web content
• Files are URL-accessible
• Move BLOBS from RDS

25. Elastic Compute Cloud (EC2)
• Hardware virtualisation in the cloud
• Various sized servers
• All major operating systems
• Uses Elastic Block Store (EBS) service
• Provision, configure, start and stop
instances through browser or API
• Full operating system access

26. Relational Database Service (RDS)
• Specialised RDBMS hosting
• Supports MySQL, Oracle, SQL Server
• Automated backups
• Live replication for fail-over support
• Transparent to your apps
• DB provisioning from your
browser or through API

27. SimpleDB & DynamoDB
• NOSQL databases
• Simple DB
– Excellent for small amount of structured data
– 10GB limit per table
• DyanamoDB
– Hosted on SSD
– No size or request limitation

28. CloudWatch
• Scalable unified resource monitoring
• Monitor EC2, RDS and any other AWS
resource
• Also monitor your own application
• Create your own alarm conditions
• Flexible notification system via Simple
Notification Service (SNS)

29. Auto Scaling
• Works with CloudWatch
• Automatically provision additional instances
when load increases
• Shutdown instances when load decreases
• Pre-emptive scaling
• Monitoring health of EC2
instances

30. Elastic Load Balancing (ELB)
• Scaled fault-tolerant load balancing
• Works with Auto Scaling and CloudWatch
• Load balances request over a set of EC2 instances
• Can monitor health of EC2 instances and
shutdown non-performance instances
• Can trigger scale-up and
scale-down events

31. Elastic Beanstalk (EBS)
• Platform as a Service (PaaS)
• Bundles up services provided by EC2, S3,
CloudWatch, Auto Scaling and ELB
• Browser based provisioning of production
ready applications
• Supports live hot-swapping
for applications
• Java, .Net, Python, PHP only

32. Simple Notification Service (SNS)
• Scalable fault tolerant messaging
• Public-Subscribe model (push)
• Messages are persisted and can survive
common outage scenarios
• Notifications via
– HTTP
– SMTP
– SMS
– SQS

33. Simple Queue Service (SQS)
• Compliments SNS functionality
• Queue model (FIFO)
• Message publishers and consumers
inside and outside of AWS
• Using SNS and SQS paramount for
applications to ensure
– Scalability
– Reliability
– Resilience

34. Simple Workflow Service (SWF)
• Coordinated synchronous and asynchronous
work in distributed application
• Supports automated and manual (human-
based) tasks
• Like SNS and SQS, using SWF as part of your
application can help build
“web-scale” applications

35. Elastic MapReduce (EMR)
• “Big Data”
• Hosted Apache Hadoop environment
• Uses EC2, S3 and DynamoDB service
• Specify the number and type of EC2 instances
used
• Full control via API
• Examples: Scientific Analysis,
Financial Analysis, Content indexing

37. Discussion Points
• Stateless development is key ?
• Can you abstract away from the
underlying technology – Cloud Foundry ?
• Remember that platforms are still
evolving…
• Rate of change of platforms is high
• Need to factor in existing staff Skill Base