Post on 14-Jun-2020
transcript
En oversikt, likheter og forskjellerEn oversikt, likheter og forskjeller
Rune ZakariassenMicrosoftMicrosoft
Historic Computing Transformations
We are all excited about the cloudWe are all excited about the cloud IDC Sees Cloud Market Maturing QuicklyIDC Sees Cloud Market Maturing Quickly
I 2009 i l $17 billi l d l d• In 2009, approximately $17 billion was spent on cloud-related technologies, hardware and software.
• By 2013, that spending is expected to grow to $45 billion.y , p g p g $• Frank Gens, senior vice president and chief analyst for the IDC,
declare that the chasm has been crossed and the cloud is well on its way to becoming mainstreamway to becoming mainstream.
Kilde: IDChttp://itmanagement.earthweb.com/features/article.php/3870016/IDC-Sees-Cloud-Market-Maturing-Quickly.htm
In House or Hosted ServersIn House or Hosted Servers
Allocated L dAllocated IT-capacities
“Under-supply“ of capacities
Load Forecast
AC
ITY “Waste“ of
capacitiesFixed cost of IT-capacities
IT C
APA
I
Barrier forActual Load
Barrier forinnovations
TIME
Cloud ComputingCloud Computing
LoadAllocated IT capacities
Load Forecast
No “under-supply“
AC
ITY
Reduction of “over-supply“
Possible reduction of IT
CA
PA
Reduction of initial
investments
IT-capacities in case of
reduced load
I
Actual Load
investments
Time
What Is A Cloud Platform?
“software as a service”
“infrastructure as a service”
“platform as a service”
“information as a service”“everything as a service”
Gartner’s View of Clouds
Cloud computing is cheaper when the economic return is high
CloudCo-location
E i
computingservices
Economiesof scale
Traditionaloutsourcing
On-premiseinstallation outsourcinginstallation
Economies of skill
Data Center Evolution
Leased COLO
Quincy Class
Container Class
Generation 4Modular Data Center
Data CenterData CenterDesignDesign
Deployment Deployment Scale UnitScale UnitScale UnitScale Unit
ContainerS
Rack
Server
The Microsoft CloudThe Microsoft Cloud~100 Globally Distributed Data Centers
Quincy, WA Chicago, IL San Antonio, TX Dublin, Ireland Generation 4 DCs
Workload Patterns Optimal For Cloud
Usage
Compu
te
Ti
Average
Inactivity
Period Average UsageCo
mpu
te
TiTime
On & off workloads (e.g. batch job)On & off workloads (e.g. batch job)Over provisioned capacity is wasted Over provisioned capacity is wasted Time to market can be cumbersomeTime to market can be cumbersome
Time
Successful services needs to grow/scale Successful services needs to grow/scale Keeping up w/ growth is big IT challenge Keeping up w/ growth is big IT challenge Complex lead time for deploymentComplex lead time for deploymentTime to market can be cumbersome Time to market can be cumbersome Complex lead time for deploymentComplex lead time for deployment
Compu
te
Average Usage
Compu
te
Average Usage
Time
Average Usage
Unexpected/unplanned peak in demand Unexpected/unplanned peak in demand S dd ik i t fS dd ik i t f
Time
Average Usage
Services with micro seasonality trends Services with micro seasonality trends Sudden spike impacts performance Sudden spike impacts performance Can’t over provision for extreme cases Can’t over provision for extreme cases
Peaks due to periodic increased demandPeaks due to periodic increased demandIT complexity and wasted capacity IT complexity and wasted capacity
Types of Clouds
PrivatePrivate
Types of Clouds
PlatformPlatformI f t tI f t tPrivate(On-Premise)
Private(On-Premise)
Applications
Platform(as a Service)Platform
(as a Service)
Applicationsnage
Infrastructure(as a Service)
Infrastructure(as a Service)
Applications
Runtimes
Applications
Security & Integration
Runtimes
Applications
Security & Integration
You
ma
Runtimes
Applications
Security & Integrationman
age
Databases
Security & Integration
age Databases
Security & Integration
Manage
Databases
Security & Integration
You
m
Servers
Virtualization
You
man Servers
Virtualization
ed by vend
Virtualization
Managed
Servers
Storage
Server HW
Storage
Server HW
dor
Storage
Server HW
d by vendo
Networking NetworkingNetworking
or
Azure & Amazon ComparisonAzure & Amazon Comparison
Y A li i Deplo ment
Frameworks
Your Application Deployment
Web ServerDeployment
Operating System
OS Services Provided byWindows Azure
p g y
ProvidedBy
A
Virtualized Instance
AmazonEC2 Hardware
Azure and Google (AppEngine)
Your ApplicationDeployment Deployment
Frameworks
Web Server
OS ServicesProvided byProvided by
Operating System
OS ServicesGoogle
AppEngine
Provided byWindows Azure
Virtualized Instance
HardwareHardware
Azure & SalesForce.com
Your Application Deployment
Frameworks
Your Application Deployment
Web Server
OS S iProvided by
Operating System
OS ServicesProvided bySalesForce.com Provided by
Windows Azure
Virtualized Instance
Hardware
Data Storage
K t t t i bl b d bl kKey concepts account, container, blob and blocks
BlockBlobContainerAccount
IMG001 JPG
Pictures
IMG001.JPG
IMG002.JPG
AccountBlock AAAA
Movies MOV1.AVI Block AAAB
Block AAACBlock AAAC
Semi‐Structured data
Tables contain entities Tables contain entities
Entities contain properties
May be partitioned across May be partitioned across thousands of servers.
Support ACID transactions ppover single entities
Queries over entire table
.NET and REST interfaces
GetMessageGetMessage (Timeout)(Timeout)RemoveMessageRemoveMessage
MsgMsg 11Worker RoleWorker Role
PutMessagePutMessageMsgMsg 11
MsgMsg 22Web RoleWeb Role MsgMsg 22MsgMsg 11
MsgMsg 33
MsgMsg 44 Worker RoleWorker RoleWorker RoleWorker Role
QueueQueue MsgMsg 22
SQL Azure
Database Replicas
Single Database Multiple Replicas
Single PrimaryReplica 1
g y
Replica 2DB
Replica 3
Scenarios vs. Platform Capabilities
Create Create Very Scalable Web
Moderately Scalable Web
Apps
Create Very Scalable Web
Apps
Create Parallel Processing Apps
Apps with Background Processing
Run On‐Premises Apps
Scale‐out web
VM with standard OS
x x x
x
app platform
Scale‐out batch app platform
x x
x x
x
Scale‐out
Relational storage x
x x
x
storage
Blob storage
x
x x
x
Queues x
GoGrid, Mosso, Flexiscale, OthersTypical scenariosTypical scenarios
Create Very Scalable Web Create
Run On‐Premises Apps
Create Very Scalable Web
Apps
Apps with Background Processing
Create Parallel Processing Apps
Moderately Scalable Web
Apps
Scale‐out web
VM with standard OS VMs
VMsapp platform
Scale‐out batch app platform
VMs
Scale‐out
Relational storage
VMs (w/RDBMS) VMs (w/RDBMS)
storage
Blob storage
Queues
Amazon Web ServicesTypical scenariosTypical scenarios
Create Very Scalable Web Create
Run On‐Premises Apps
Create Very Scalable Web
Apps
Apps with Background Processing
Create Parallel Processing Apps
Moderately Scalable Web
Apps
Scale‐out web
VM with standard OS EC2 VMs
EC2 VMs EC2 VMs EC2 VMsapp platform
Scale‐out batch app platform
EC2 VMs EC2 VMs
EC2 VMs , Elastic MapReduce
EC2 VMs
EC2 VMs
Scale‐out
Relational storage
EC2 VMs (w/RDBMS)
EC2 VMs (w/RDBMS)
SimpleDB SimpleDBstorage
Blob storage
SimpleDB
Simple Storage Service (S3) S3
SimpleDB
QueuesSimple Queue Service (SQS)
Windows AzureTypical scenariosTypical scenarios
Create Very Scalable Web Create
Run On‐Premises Apps
Create Very Scalable Web
Apps
Apps with Background Processing
Create Parallel Processing Apps
Moderately Scalable Web
Apps
Scale‐out web
VM with standard OS
Web role Web role Web roleapp platform
Scale‐out batch app platform
Web role Web role
Worker role
Web role
Worker role
Scale‐out
Relational storage SQL Azure
Tables Tablesstorage
Blob storage
Tables
Blobs Blobs
Tables
Queues Queues
Google AppEngineTypical scenariosTypical scenarios
Create Very Scalable Web Create
Run On‐Premises Apps
Create Very Scalable Web
Apps
Apps with Background Processing
Create Parallel Processing Apps
Moderately Scalable Web
Apps
Scale‐out web
VM with standard OS
Java/Python app platform
Scale‐out batch app platform
runtime
Scale‐out
Relational storage
Datastorestorage
Blob storage
Datastore
Queues
Salesforce.com Force.comTypical scenariosTypical scenarios
Create Very Scalable Web Create
Run On‐Premises Apps
Create Very Scalable Web
Apps
Apps with Background Processing
Create Parallel Processing Apps
Moderately Scalable Web
Apps
Scale‐out web
VM with standard OS
Force.comapp platform
Scale‐out batch app platform
runtime
Scale‐out
Relational storage
Force.comstorage
Blob storage
storage
Queues
Comparing Cloud PlatformsSummarizing typical scenariosSummarizing typical scenarios
Run On PremisesCreate Very Scalable Web
Create Very Scalable Web Apps with Background
Create Parallel Processing Apps
Create Moderately Scalable WebRun On‐Premises
AppsScalable Web
Apps
gProcessing
Processing AppsScalable Web Apps
GoGrid, Mosso, Flexiscale etc xxFlexiscale, etc. xxAmazon Web
Services x x x x x
Windows Azure x x x xGoogle
AppEngine xAppEngine
Salesforce.com Force.com x
© 2008 Microsoft Corporation. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS,
IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.