Historic Computing Transformations. Rune Zakariassen
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1 Historic Computing Transformations En oversikt, likheter og forskjeller Rune Zakariassen Microsoft We are all ecited about the cloud IDC Sees Cloud Market Maturing Quickly In 2009, approimately $17 billion was spent on cloud-related l d technologies, hardware and software. By 2013, that spending is epected 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
2 In House or Hosted Servers Cloud Computing Under-supply of capacities Allocated Load IT-capacities Forecast Allocated IT capacities Load Forecast IT CAPA ACITY Barrier for innovations Waste of capacities Actual Load Fied cost of IT-capacities IT CAPA ACITY Reduction of initial investments No under-supply Reduction of over-supply Actual Load Possible reduction of IT-capacities in case of reduced load TIME What Is A Cloud Platform? Gartner s View of Clouds software as a service infrastructure as a service platform as a service everything as a service information as a service
3 Cloud computing is cheaper when the economic return is high Data Center Evolution Economies of scale Co-location services Cloud computing Leased COLO Data Center Design Quincy Class Container Class Generation 4 Modular Data Center On-premise installation Traditional outsourcing Scale Unit Server Rack Container Economies of skill The Microsoft Cloud ~100 Globally Distributed Data Centers Workload Patterns Optimal For Cloud Compute Average Inactivity Period Usage Compute Average Usage Quincy, WA Chicago, IL San Antonio, TX Dublin, Ireland Generation 4 DCs On & off workloads (e.g. batch job) Over provisioned capacity is wasted to market can be cumbersome Successful services needs to grow/scale Keeping up w/ growth is big IT challenge Comple lead time for deployment Compute Average Usage Compute Average Usage Unepected/unplanned peak in demand Sudden spike impacts performance Can t over provision for etreme cases Services with micro seasonality trends Peaks due to periodic increased demand IT compleity and wasted capacity
4 Types of Clouds Azure & Amazon Comparison 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 Provided By Amazon EC2 Your Application i Frameworks Web Server Services Operating System Virtualized Instance Hardware Windows Azure Azure and Google (AppEngine) Azure & SalesForce.com Your Application Your Application Frameworks Frameworks Google AppEngine Web Server Services Operating System Windows Azure SalesForce.com Web Server Services Operating System Windows Azure Virtualized Instance Virtualized Instance Hardware Hardware
5 Data Storage Key concepts account, container, blob b and blocks Account Account Container Pictures IMG001.JPG IMG002.JPG Block Block AAAA 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 Movies MOV1.AVI Block AAAB Block AAAC
6 GetMessage RemoveMessage (out) SQL Azure PutMessage Web Role Msg 1 Msg 2 Msg 3 Msg 4 Worker Role Msg 12 Worker Role Queue Msg 2 Database Replicas Single Database DB Multiple Replicas Replica 1 Replica 2 Single Primary Replica 3
7 Scenarios vs. Platform Capabilities GoGrid, Mosso, Fleiscale, Others Parallel with Parallel with VM with standard VMs web web VMs batch batch VMs (w/rdbms) VMs (w/rdbms) Amazon Web Services Windows Azure Parallel with Parallel with web web Web role Web role Web role batch, Elastic MapReduce batch Worker role Worker role (w/rdbms) (w/rdbms) SQL Azure SimpleDB SimpleDB Tables Tables Simple Storage Service (S3) S3 s s Simple Queue Service (SQS)
8 Google AppEngine Salesforce.com Force.com Parallel with Parallel with web Java/Python runtime web Force.com runtime batch batch Datastore Force.com Comparing Cloud Platforms Summarizing typical scenarios Parallel with GoGrid, Mosso, Fleiscale, etc. Amazon Web Services Windows Azure Google AppEngine Salesforce.com Force.com
9 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. MICROFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.
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