Ulrich (Uli) Homann Chief Architect, WW Enterprise Services Microsoft Corporation SESSION CODE: ARC305

Congress Passed Public Law Dec 2006 EPA to study and promote IT Efficiency EPA provided response August Outcome: EPA Energy Star Program Energy Star for Servers May 2009 release Energy Star for DCs Jan 2010 release 3

In the US: Capacity Growth = 0.8%/yr Usage Growth = 1.1%/yr DC Growth = 15%/yr US Projections: 2020  10% 2030  30-40% US Projections: 2020  10% 2030  30-40% 4 Today in US 1.8 to 2.0% Today in US 1.8 to 2.0%

In the US: Capacity Growth = 0.8%/yr Usage Growth = 1.1%/yr DC Growth = 15%/yr US Projections: 2020  10% 2030  30-40% US Projections: 2020  10% 2030  30-40% 5 Today in US 1.8 to 2.0% Today in US 1.8 to 2.0% 5 times perf/W

Belady, C., “In the Data Center, Power and Cooling Costs More than IT Equipment it Supports” Electronics Cooling Magazine (Feb 2007) DC Energy 6

Source: EYP Mission Critical Facilities Inc., New York Microsoft is focusing on all the pieces of the pie Utility Load Utility Load PUE = = 2 IT Load IT Load Utility Load Utility Load PUE = = 2 IT Load IT Load 7

what… why… Business applications delivered as a service that are always available, scale as required, provisioned just-in-time, and run on shared industry-standard hardware Accelerates speed to solution and lowers the cost of IT

Microsoft Confidential Capacity Density and Deployment Time to Market Lower TCO Scalable Datacenter Scalability and Sustainability Servers Rack ITPAC Containers

‘Traditional’New SpaceWatts Per layer thinkingIntegrated systems MonolithicModular Network-centric CustomStandard ‘Free-form’ variabilityManaged variability (product line approach)

Aim: Avoid component failure Availability through Redundancy Redundancy at all layers Fewer failures, but greater impact “The Nines” fallacy Measured by Mean-Time- Between-Failures Aim: Avoid service failure Availability through Resiliency Automated detect-and-respond More failures, but less impact Measured by Mean-Time-to- Restore-Service

Usage Compute Time Average Inactivity Period Compute Time Average Usage Compute Time Compute Time Average Usage

GOAL: Massive Scalability Scale out – not up – by replicating worker instances as needed. Allow applications to scale user and compute processing independently. Two role types: Web Role & Worker Role Windows Azure applications are built with web roles, worker roles, or a combination of both deployed to a number of instances. Each instance runs on its own VM (virtual machine), replicated as needed Cloud Compute Services (example Windows Azure)

Windows Azure storage is an application managed by the Fabric Controller Windows Azure applications can use native storage, MySQL within a VM, or SQL Azure Application state is kept in storage services, so worker roles can replicate as needed Blobs can be stored in global Windows Azure Content Delivery Network (CDN) Storage Services (example Windows Azure) GOAL: Scalable, durable utility-based storage

Service Model Simple topology view

Initial Size 2 SharePoint App Servers 1 SQL Server Growth Unit A Capacity Driver: # of users +1 SharePoint Application Server Growth Unit B Capacity driver: content db size +1 SQL Server Max Growth 4 SharePoint App Servers 2 SQL Server Pete’s SharePoint order (representing max growth): - 50,000 users - 20,000 team sites - 150MB/site - Responses per second: 100 Farm configurationRPS 2 by 199 Farm configurationRPS 4 by 2120 Farm configurationRPS 3 by 1115 Monitoring counters in the operational configuration and monitoring environment (SC OM 2007) trigger growth (or shrink) provisioning once the specific capacity driver hits 80% of specified value: - Growth based upon RPS (growth type A): initial size – 99 RPS; counter is set to 80 RPS - Growth based upon content db size (growth type B): initial size – 0.8 TB; counter is set to 0.7 TB

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