1. AZR308

3. Building distributed systems on an abstraction against commodity hardware at Internet scale, composed of multiple services. Distributed System. Cloud applications are inherently distributed and must balance consistency and availability Abstraction. Cloud applications are developed against various levels of abstraction; virtual machines (IaaS), platform services (storage, compute – PaaS), and software services (media, mobile, messaging, data – SaaS). Commodity hardware at Internet scale. Clouds deploy commodity hardware to balance cost-efficiency and performance in 1000+ machines chunks. Composed of multiple services. Platform services (stuff you get from the cloud), external services (stuff you get from others) and application services (stuff you write)

4. Drive cost efficiency at design time and at runtime while still using a robust platform with good uptime. Drive cost efficiency. The overall cost of an application includes the development effort, the running costs and the people time to operate and manage. Need to optimize them all Design time. Pre-built cloud services reduce dev effort, optimized deployment models allow faster iteration. Runtime. Granular purchasing in small increments. High level abstractions remove much of the traditional operations overhead. Robust platform. Still not the cheapest, but comparing like for like in quality it is hard to compete with the economies of scale

9. All of the abstractions use the same underlying storage sub-system

11. Design Goals

12. Index Partition Layer

13. Blobs

15. Azure CDN http:// xxx.vo.msecnd.net

16. Tables Go to AZR412: WAS Tables, What Are They Good For? 1610hr today in Epsom

17. Drives & Disks Azure PaaS uses Drives Azure IaaS uses Disks

18. Drives & Disks So for apps with chunky file IO we’ll see some cost savings. Do Disks and Drives deliver different performance on account of the different paths?

19. Caching Most OLTP database scenarios best with caching off (high random IO requirements)

22. Windows Azure SQL Database Single Logical Database Multiple Physical Replicas Replica 1 Replica 2 Replica 3 Firewall TDS Scott Klein has two sessions on Azure SQL Database AZR314: Query Performance Tuning straight after this session in Epsom AZR311: Azure SQL Database for the DBA 1510hr today in NZ3

24. SQL Server in an IaaS Virtual Machine

25. See TechEd US 2013 sessions that deep dive on HA & Perf Tuning DBI311 Performance Tuning Microsoft SQL Server in Windows Azure Virtual Machines MDC406 SQL Server High Availability and Disaster Recovery on Windows Azure VMs

27. The CAP Theorum (2000ish) Choosing among Cloud Computing Data Stores is usually about trading off CAP

29. W+R <= N will always be eventually consistent E.g. N=2, W=1, R=1 will have a period where the single read could return a value from a node other than that which was written to by the client W=1 optimizes for write availability R=1 optimizes for read availability Can still be strongly consistent if we want e.g. N=4, W=1, R=N=4 can write to a single node but must read the same value from all 4 nodes ensuring consistency

33. Help!?! Can we use Azure?

47. Head to... aka.ms/te