Availability Analysis for Deployment of In-Cloud Applications

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International Symposium on Architecting Critical Systems (ISARCS) 2013 talk slides. June 19th, 2013.
Full paper at http://www.nicta.com.au/pub?doc=6431

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  • In this paper, we only show the architecture model and the recovery model due to space limitations.
  • Availability Analysis for Deployment of In-Cloud Applications

    1. 1. Availability Analysis for Deploymentof In-Cloud ApplicationsXiwei Xu, Qinghua Lu, Liming Zhu, Jim (Zhanwen) LiSherif Sakr, Hiroshi Wada, Ingo WeberSoftware Systems Research Group, NICTAISARCS13, VancouverSlides at: http://www.slideshare.net/LimingZhu/
    2. 2. NICTA Copyright 2010 From imagination to impact 2Motivation• Uncertainties in Cloud are challenging for architectingcritical applications and understanding availability– Shared resources, weak SLA guarantees and limited visibility– Rare but high consequence events– Sporadic activities: upgrade, backup, recovery…– Subjective uncertainties: impact of configuration choices• We want to explicitly model the above uncertainties inapplication availability analysis of cloud deployment.– from a cloud consumer perspective– focusing on mechanisms most relevant to criticalapplications: auto-scaling, over-provisioning, backup, recovery and maintenance.
    3. 3. NICTA Copyright 2010 From imagination to impact 3Contributions• SRN(Stochastic Reward Net)-based availability models• which allow you to specify:– Deployment architecture (application placements in VM)– Node/Aggregation level SLAs from infrastructure providers– Auto-scaling policies and recovery strategies– Rare events: availability zone or region down• which give you application availability levels of different optionsunder different scenarios• Model evaluation by analysing existing industry bestpractices in cloud application deployment– Quantifying the rule-of-thumb best practices– Comparing different (best) practices
    4. 4. NICTA Copyright 2010 From imagination to impact 4Deployment Architecture Assumption– Stateless VMs: auto-scaling groups– Stateful VMs: hot standbys– Backup at separate region for recovery
    5. 5. NICTA Copyright 2010 From imagination to impact 5Availability Analysis Overview• SRN-based Models• Architecture model and recovery model in this paper• One SRN architecture model per availability zone
    6. 6. NICTA Copyright 2010 From imagination to impact 6Availability Analysis Overview• Deployment decisions and patterns– stateless/stateful application placement within VMs– auto-scaling policies– multi-zone configurations
    7. 7. NICTA Copyright 2010 From imagination to impact 7Availability Analysis Overview• SLA from the cloud providers• Node level (Rackspace) or zone level (Amazon)
    8. 8. NICTA Copyright 2010 From imagination to impact 8Availability Analysis Overview• Recovery strategy• Auto-regeneration of stateless VMs and differentrecovery mechanisms for stateful VMs• Different Recovery-Time/Point-Objective (RTO/RPO)
    9. 9. NICTA Copyright 2010 From imagination to impact 9Availability Analysis Overview• Application-specific data– Stateless VM start-up time…– Stateful VM replication…
    10. 10. NICTA Copyright 2010 From imagination to impact 10Stochastic Reward Net• Stochastic Reward Net (SRN)– Stochastic Petri Net variant– Firing delays– Reward function• Constructs• Places: VM states(Full, Running, Stoped, Failed )• Token: VMs• Transition• Guard function• Transition rate: 1) frequency ofevents, 2) delay before thetransition fires• Reward Function:if((#Running1>0) 1 else 0
    11. 11. NICTA Copyright 2010 From imagination to impact 11SRN-based Availability Models
    12. 12. NICTA Copyright 2010 From imagination to impact 12Availability Models: Auto-scaling
    13. 13. NICTA Copyright 2010 From imagination to impact 13Availability Models: Auto-scalinggScaleSelf1:if(#Running1<=#Running2 && #Stopped1>0) 1 else 0gScaleOther1:if(#Running1>#Running2 && #Stopped2>0) 1 else 0
    14. 14. NICTA Copyright 2010 From imagination to impact 14Availability Models: Stateful VM
    15. 15. NICTA Copyright 2010 From imagination to impact 15Availability Models—Disaster Recovery• Availability zone life cycle– Interact with the bigarchitecture model• Stateless VM recovery– Backup/AMI• Stateful VM recovery– Backup– Replica– Hot standby
    16. 16. NICTA Copyright 2010 From imagination to impact 16Case 1: Multi-zone Deployment• Parameters– Amazon EC2 SLA of 99.95% availability– Zone fail rate: 0.00011, MTTR: 4.38 hours per year– Application specific measurement of transitions0.01% = 52.56 mins downtime per year0.4% diff = 35 hours0.76% diff = 66 hours
    17. 17. NICTA Copyright 2010 From imagination to impact 17Case 2: Recovery across Availability Zone• Industry rule of thumb: ―Target auto-scale 30-60% until you have50% headroom for load spikes. Lose an AZ leads to 90% utilisation.‖• Impact on overall availability?• 30-60% vs. traditional 70-90%?• over-provisioning vs. auto-scaling?0.29% diff = 25 hours
    18. 18. NICTA Copyright 2010 From imagination to impact 18Case 3: Disaster Recovery across Regions• Trade-off between RPO and RTO• RPO: Recovery Point Objective• RTO: Recovery Time ObjectiveYuruware — http://www.yuruware.com/0.2% diff = 17 hours
    19. 19. NICTA Copyright 2010 From imagination to impactConclusion and Future Work• SRN-based availability models– Application-level availability– Highly configurable for different deployment architectures– Model different uncertainties and scenarios for critical systems– Quantify and compare choices and enable what-if analysis– Evaluated using industry best practices• Future work– Better evaluation!– Integrated models on impact of upgrade, live migration, backup andsubjective uncertainties (in IEEE Cloud 13)Q. Lu, X. Xu, L. Zhu, L. Bass, et al., "Incorporating Uncertainty into in-Cloud ApplicationDeployment Decisions for Availability," in IEEE Cloud 2013Liming.Zhu@nicta.com.auSlides available at http://www.slideshare.net/LimingZhu/19

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