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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

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Lecture 9 more cloud applications l.jpg

Lecture 9: More Cloud Applications

Xiaowei Yang (Duke University)

News buffalo as data center mecca l.jpg
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 l.jpg

  • Cloud Computing

    • Elasticity

    • Pay-as-you-go

  • Challenges

    • Security: co-residence, inference

    • Performance

      • Coarse-grained sharing

      • Lack of virtualized interface for specialized hardware

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  • 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 l.jpg

The Case for Energy-Oriented Partial Desktop Migration

NiltonBila†, Eyal de Lara†, MattiHiltunen, Kaustubh Joshi,

H. Andr´esLagar-Cavillaand M. Satyanarayanan

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  • 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

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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.

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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

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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





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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

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  • Small migration footprint

  • Client

    • Fast migration

    • Low energy cost

  • Network

    • Reduce bandwidth demand

  •   Server

    • More VMs per server

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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?

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  • 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 l.jpg

  • 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

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Memory Request Pattern

  • Spatial locality

    • Pre-fetching

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Page Request Interval

  • 98% of request arrive in close succession

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Memory footprint

  • A cloud node with 4GB of RAM can run ~30 VMs

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Annual Energy Savings

  • No partial migration

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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?

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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; PrashantShenoy, University of Massachusetts Amherst; Jacobus van derMerwe, AT&T Labs—Research; ArunVenkataramani, University of Massachusetts Amherst

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Datacenter Disasters Deployment Challenges

  • 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)

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DR Fits in the Cloud Deployment Challenges

  • 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

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What is disaster recovery Deployment Challenges

  • Use DR services to prevent lengthy service disruptions

  • Data backups + failover mechanism

    • Periodically replicate state

    • Switch to backup site after disaster

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DR Metrics Deployment Challenges

  • 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

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  • Performance Deployment Challenges

    • 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

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DR Mechanisms Deployment Challenges

  • 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

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Warm backup Site Deployment Challenges

  • Cheaply synchronize state during normal operations

  • Obtain resources on demand after failure

  • Short delay to resource provision and applications

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Cost analysis study Deployment Challenges

  • 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

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Case Study 1 Deployment Challenges

  • RUBiS: an ebay-like multi-tier web application

    • Three front ends

    • One database server

    • Only database state is replicated

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Cost analysis Deployment Challenges

  • 99% Uptime cost (3 days of disaster per year)

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Case 2: Data Warehouse Deployment Challenges

  • Post-disaster expensive due to high powered VM instance

  • Overall cheaper because 99% Uptime

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RPO Deployment Challengesvs Cost Tradeoff

  • Flexible

  • Colo has a fixed cost regardless of RPO requirements

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Cost Analysis Summary Deployment Challenges

  • 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

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Provider Challenges Deployment 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

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Mechanisms Needed for Cloud DR Deployment Challenges

  • 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

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Summary Deployment Challenges

  • Cloud based disaster recovery

    • Can reduce cost

      • Up to 85% from a case study

    • Flexible tradeoff between cost and RPO

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Forecast Deployment Challenges

  • 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