1. Lecture 9: More Cloud Applications Xiaowei Yang (Duke University)

2. News: Buffalo as Data Center Mecca $1.9 billion, at least 200 employees Low-cost electric power, tax incentives, plenty of shovel-ready sites, cool climate

3. Review Cloud Computing – Elasticity – Pay-as-you-go Challenges – Security: co-residence, inference – Performance Coarse-grained sharing Lack of virtualized interface for specialized hardware

4. Today Cloud Applications – Execution augmentation for mobile devices – Energy saving for mobile – Energy saving for desktops – Disaster recovery

5. The Case for Energy-Oriented Partial Desktop Migration Nilton Bila†, Eyal de Lara†, Matti Hiltunen, Kaustubh Joshi, H. Andr´es Lagar-Cavillaand M. Satyanarayanan

6. Motivations Offices and homes have many PCs But, they areoften left running idle – PCs idle on average 12 hours a day “Skilled in the art of being idle” by Nedevschi et al. in NSDI 2009 – 60% of desktops remain powered overnight “After-hours power status of office equipment in the USA” by Webber, in Energy 2006

7. Why is it important? Dell Optiplex 745 Desktop Peak power: 280W Idle power: 102.1W Sleep power: 1.2W If we put one to sleep when it is idle, the saving is (102.1-1.2)W.

8. Why do we leave desktops on? Applications with always on semantics – Skype, IM, email, personal media sharing Interspersed activities with idle periods – Lunch break – Chatting with colleagues

9. Related work Full VM migration – LiteGreen, USENIX 2010 best paper – Encapsulate user session in VM – When idle, migrate VM to consolidation server and power down PC – When busy, migrate back to user’s PC Xen Dom0 User0 User1

10. Partial VM migration Idle VM only access partial memory and disk state (working set) Migrate only the working set to a server – Potentially a cloud server – Cloud provider can further aggregate

11. Advantages Small migration footprint Client – Fast migration – Low energy cost Network – Reduce bandwidth demand Server – More VMs per server

12. Feasibility Study Can its desktop save energy by sleeping when an VM runs on the cloud? Does the entire domain save energy by migrating idle sessions by sleeping?

13. Methodology Prototyped simple on-demand migration approach with SnowFlock – Prepared a VM image, and run the VM – After five minutes, used SnowFlock to clone the VM – Monitor memory and disk page migration to cloneVM

14. Setup Dell Optiplex 745 Desktop – 4GB RAM, 2.66GHz Intel C2D – Peak power: 280W – Idle power: 102.1W – Sleep power: 1.2W VM Image: – Debian Linux 5 – 1GB RAM – 12 GB disk

15. Workloads

16. Memory Request Pattern Spatial locality – Pre-fetching

17. Page Request Interval 98% of request arrive in close succession

18. Potential Sleep Intervals

22. Energy Savings: an hour-long trace

23. Hourly Energy Savings: an overnight session Saves 69% of energy

24. Memory footprint A cloud node with 4GB of RAM can run ~30 VMs

25. Domain-wide Energy Savings

26. Annual Energy Savings No partial migration

27. Annual Energy Savings V = 23

28. Annual Savings

29. Open issues Can it save cost? – Network – Cloud Rental Frequent power cycling reduces hw life expectancy and limits power savings – Reduce number of sleep cycles and increase sleep duration – Predict page access patterns and prefetch – Leverage content addressable memory Fast reintegration – Big Q: Can it be fast enough so that a user does not suffer a long delay? Policies – When to migrate/re-integrate? – When does the desktop go to sleep? – On re-integration, should state be maintained in the cloud? For how long?

30. Disaster Recovery as a Cloud Service: Economic Benefits & Deployment Challenges Timothy Wood and Emmanuel Cecchet, University of Massachusetts Amherst; K.K. Ramakrishnan, AT&T Labs—Research; Prashant Shenoy, University of Massachusetts Amherst; Jacobus van der Merwe, AT&T Labs—Research; Arun Venkataramani, University of Massachusetts Amherst

32. Datacenter Disasters Disasters cause expensive application downtime Truck crash shuts down Amazon EC2 site center (May 2010) Lightning strikes EC2 data (May 2009) Comcast Down: Hunter shoots cable (2008) Squirrels bring down NASDAQ exchange (1987 and 1994)

33. DR Fits in the Cloud Customer: pay-as-you-go and elasticity – Normal is cheap (fewer resources for backup than normal operations) – Rapidly scale up resources after disaster is detected Provider: high degree of multiplexing – Customers will not fail at once – Can offer extra services like disaster detection

34. What is disaster recovery Use DR services to prevent lengthy service disruptions Data backups + failover mechanism – Periodically replicate state – Switch to backup site after disaster

35. DR Metrics Recovery Point Objective (RPO): the most recent backup time prior to any failure Recovery Time Objective (RTO): how long it can take for an application to come back online after a failure occurs – Time to detect failure – Provision servers – Initialize applications – Configure networks to connect

36. Performance – Have a minimal impact on the performance of each application being protected under failure-free operation – How can DR impact performance? Consistency – The application can be restored to a consistent state Geographic separation – Challenge: increasing network latency

37. DR Mechanisms Hot Backup Site – Provides a set of mirrored stand-by servers that are always available – Minimal RTO and RPO – Use synchronous replication to prevent any data loss

38. Warm backup Site Cheaply synchronize state during normal operations Obtain resources on demand after failure Short delay to resource provision and applications

39. Cost analysis study Compare DR in Colocation center to Cloud Colocation – pays for servers and space at all times Cloud DR – Pays for resources as they are used

40. Case Study 1 RUBiS: an ebay-like multi-tier web application – Three front ends – One database server – Only database state is replicated

41. Cost analysis 99% Uptime cost (3 days of disaster per year)

42. Case 2: Data Warehouse Post-disaster expensive due to high powered VM instance Overall cheaper because 99% Uptime

43. RPO vs Cost Tradeoff Flexible Colo has a fixed cost regardless of RPO requirements

44. Cost Analysis Summary Cloud DR’s benefits depend on – Type of resources to run application – Variation between normal and post-disaster costs – RPO and RTO requirements – Uptime Cloud is better if post-disaster cost much higher than normal mode

45. Provider Challenges How to maximize revenue? – Makes money from storage in normal case – But must pay for servers and keep them available for DR – Possible solutions Spot instances (EC2 uses them) Higher prices for higher priority resources Correlated failures – Large disasters may affect many – Possible solutions Decide provision using a risk model Spread out customers

46. Mechanisms Needed for Cloud DR Network reconfiguration – Application must be brought up online after moved to a backup site – May require setting up a private business network Security and Isolation VM migration and cloning – Restore an application after a disaster is handled – Cloud provider does not support VM migration in and out cloud yet

47. Summary Cloud based disaster recovery – Can reduce cost Up to 85% from a case study – Flexible tradeoff between cost and RPO

48. Forecast Next lecture – Another cloud application for group collaboration Monday is in fall break Next Wednesday – Midterm – http://www.cs.duke.edu/courses/fall10/cps 296.2/syllabus.html