En oversikt En, oversikt likheter, og forskjeller Rune Zakariassen Microsoft Micr

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1 En oversikt, likheter og forskjeller Rune Zakariassen Microsoft

2 Historic Computing Transformations

3 We are all excited about the cloud

Frank Gens, senior vice president and chief analyst for the IDC, declare that the chasm has been crossed and the cloud

4 IDC Sees Cloud Market Maturing Quickly In 2009, approximately $17 billion was spent on cloud-related l d technologies, hardware and software. By 2013, that spending is expected to grow to $45 billion. 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 mainstream. Kilde: IDC

5 In House or Hosted Servers Under-supply of capacities Allocated Load IT-capacities Forecast IT CAPA ACITY Waste of capacities Fixed cost of IT-capacities Barrier for innovations Actual Load TIME

6 Cloud Computing Allocated IT capacities Load Forecast No under-supply IT CAPA ACITY Reduction of initial investments Reduction of over-supply Actual Load Possible reduction of IT-capacities in case of reduced load Time

7 What Is A Cloud Platform? software as a service infrastructure as a service platform as a service everything as a service information as a service

8 Gartner s View of Clouds

9 Cloud computing is cheaper when the economic return is high Co-location services Cloud computing Economies of scale On-premise installation Traditional outsourcing Economies of skill

10 Data Center Evolution Leased COLO Data Center Design Quincy Class Container Class Generation 4 Modular Data Center Deployment Scale Unit Server Rack Container

11 The Microsoft Cloud ~100 Globally Distributed Data Centers Quincy, WA Chicago, IL San Antonio, TX Dublin, Ireland Generation 4 DCs

12 Workload Patterns Optimal For Cloud Compute Average Inactivity Period Usage Compute Average Usage Time On & off workloads (e.g. batch job) Over provisioned capacity is wasted Time to market can be cumbersome Time Successful services needs to grow/scale Keeping up w/ growth is big IT challenge Complex lead time for deployment Compute Average Usage Compute Average Usage Time Time Unexpected/unplanned peak in demand Sudden spike impacts performance Can t over provision for extreme cases Services with micro seasonality trends Peaks due to periodic increased demand IT complexity and wasted capacity

Types of Clouds age You man Private (On-Premise) Applications

Virtualization Server HW Storage Networking You manage

Server HW Storage Networking You ma nage Managed by vendo or

HW Storage Networking Manage ed by vend dor

13 Types of Clouds age You man Private (On-Premise) Applications Runtimes Security & Integration Databases Servers Virtualization Server HW Storage Networking You manage Infrastructure t (as a Service) Applications Runtimes Security & Integration Databases Servers Virtualization Server HW Storage Networking You ma nage Managed by vendo or Platform (as a Service) Applications Runtimes Security & Integration Databases Servers Virtualization Server HW Storage Networking Manage ed by vend dor

14 Azure & Amazon Comparison Your Application i Deployment Frameworks Deployment Web Server OS Services Operating System Provided by Windows Azure Provided By Amazon EC2 Virtualized Instance Hardware

15 Azure and Google (AppEngine) Deployment Your Application Frameworks Deployment Provided by Google AppEngine Web Server OS Services Operating System Virtualized Instance Provided by Windows Azure Hardware

16 Azure & SalesForce.com Your Application Deployment Frameworks Provided by SalesForce.com Web Server OS Services Operating System Virtualized Instance Provided by Windows Azure Hardware

17 Data Storage

19 Key concepts account, container, blob b and blocks Account Container Blob Block IMG001.JPG Pictures IMG002.JPG Account Block AAAA Movies MOV1.AVI Block AAAB Block AAAC

20 Semi Structured data Tables contain entities Entities contain properties May be partitioned across thousands of servers. Support ACID transactions over single entities Queries over entire table.net and REST interfaces

21 GetMessage RemoveMessage (Timeout) PutMessage Web Role Msg 1 Msg 2 Msg 3 Msg 4 Worker Role Msg 12 Worker Role Queue Msg 2

22 SQL Azure

24 Database Replicas Single Database Multiple Replicas Replica 1 Single Primary DB Replica 2 Replica 3

25 Scenarios vs. Platform Capabilities Run On Premises Create Moderately Create Very Create Parallel Processing Create Very with Background Processing VM with standard OS Scale out web app platform Scale out batch app platform Relational Scale out Blob Queues x x x x x x x x x x x x x

26 GoGrid, Mosso, Flexiscale, Others Typical scenarios Run On Premises Create Moderately Create Very Create Parallel Processing Create Very with Background Processing VM with standard OS VMs Scale out web app platform VMs Scale out batch app platform Relational Scale out Blob VMs (w/rdbms) VMs (w/rdbms) Queues

27 Amazon Web Services Typical scenarios Run On Premises Create Moderately Create Very Create Parallel Processing Create Very with Background Processing VM with standard OS EC2 VMs Scale out web app platform EC2 VMs EC2 VMs EC2 VMs Scale out batch app platform EC2 VMs, Elastic MapReduce EC2 VMs Relational EC2 VMs (w/rdbms) EC2 VMs (w/rdbms) Scale out SimpleDB SimpleDB Blob Queues Simple Storage Service (S3) S3 Simple Queue Service (SQS)

28 Windows Azure Typical scenarios Run On Premises Create Moderately Create Very Create Parallel Processing Create Very with Background Processing VM with standard OS Scale out web app platform Web role Web role Web role Scale out batch app platform Worker role Worker role Relational SQL Azure Scale out Tables Tables Blob Queues Blobs Blobs Queues

29 Google AppEngine Typical scenarios Run On Premises Create Moderately Create Very Create Parallel Processing Create Very with Background Processing VM with standard OS Scale out web app platform Java/Python runtime Scale out batch app platform Relational Scale out Datastore Blob Queues

30 Salesforce.com Force.com Typical scenarios Run On Premises Create Moderately Create Very Create Parallel Processing Create Very with Background Processing VM with standard OS Scale out web app platform Force.com runtime Scale out batch app platform Relational Scale out Force.com Blob Queues

31 Comparing Cloud Platforms Summarizing typical scenarios Run On Premises Create Moderately Create Very Create Parallel Processing Create Very with Background Processing GoGrid, Mosso, Flexiscale, etc. x x Amazon Web Services x x x x x Windows Azure x x x x Google AppEngine Salesforce.com Force.com x x

33 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.

Finished Application e.g. SalesForce.com. Software as a Service. Application Code Publish your app to the cloud. Platform as a Service

Software as a Service Finished Application e.g. SalesForce.com Platform as a Service Application Code Publish your app to the cloud Infrastructure as a Service Virtual Machine Some might also consider