lecture 9: more cloud applications xiaowei yang (duke university)
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Lecture 9: More Cloud Applications
Xiaowei Yang (Duke University)
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
Review
• Cloud Computing– Elasticity– Pay-as-you-go
• Challenges– Security: co-residence, inference – Performance• Coarse-grained sharing• Lack of virtualized interface for specialized
hardware
Today
• Cloud Applications– Execution augmentation for mobile
devices– Energy saving for mobile – Energy saving for desktops– Disaster recovery
The Case for Energy-Oriented Partial Desktop Migration
Nilton Bila†, Eyal de Lara†, Matti Hiltunen, Kaustubh Joshi,
H. Andr´es Lagar-Cavillaand M. Satyanarayanan
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
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.
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
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
Dom0User0 User1
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
Advantages
• Small migration footprint
• Client – Fast migration – Low energy cost
• Network – Reduce bandwidth demand
• Server – More VMs per server
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?
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
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
Workloads
Memory Request Pattern
• Spatial locality– Pre-fetching
Page Request Interval
• 98% of request arrive in close succession
Potential Sleep Intervals
Potential Sleep Intervals
Potential Sleep Intervals
Potential Sleep Intervals
Energy Savings: an hour-long trace
Hourly Energy Savings: an overnight session
• Saves 69% of energy
Memory footprint
• A cloud node with 4GB of RAM can run ~30 VMs
Domain-wide Energy Savings
Annual Energy Savings
• No partial migration
Annual Energy Savings
• V = 23
Annual Savings
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?
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
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)
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
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
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
• 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
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
Warm backup Site
• Cheaply synchronize state during normal operations
• Obtain resources on demand after failure• Short delay to resource provision and
applications
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
Case Study 1
• RUBiS: an ebay-like multi-tier web application– Three front ends– One database server– Only database state is
replicated
Cost analysis
• 99% Uptime cost (3 days of disaster per year)
Case 2: Data Warehouse
• Post-disaster expensive due to high powered VM instance
• Overall cheaper because 99% Uptime
RPO vs Cost Tradeoff
• Flexible• Colo has a fixed cost regardless of
RPO requirements
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
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
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
Summary
• Cloud based disaster recovery– Can reduce cost• Up to 85% from a case study
– Flexible tradeoff between cost and RPO
Forecast
• Next lecture– Another cloud application for group
collaboration
• Monday is in fall break
• Next Wednesday–Midterm– http://www.cs.duke.edu/courses/fall10/
cps296.2/syllabus.html
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