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Techniques for Minimizing Cloud Footprint

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  • 1. Techniques for Minimizing Cloud Footprint Arun Kejariwal March 20131 International Conference on Cloud Engineering 2013 © Arun Kejariwal
  • 2. Overview   Cloud Computing o  Ubiquitous o  IDC – Cloud Services revenue ~100 B by 2016   Infrastructure-as-a-Service (IaaS) o  Large adoption   Elasticity   Scale up and down   Eliminates cycles for hardware procurement, datacenter maintenance   Higher product development agility   Fault Tolerance   Geographically distributed availability zones (e.g., AWS in US, Ireland, Singapore, Japan, Brazil) o  Growing vendors2 International Conference on Cloud Engineering 2013 © Arun Kejariwal
  • 3. Capitalizing Cloud Elasticity   Non-trivial to manage o  Aggressive scale down   Potentially affect latency and throughput adversely   Degraded end-user experience - impacts bottomline o  Aggressive scale up   Balloon cloud footprint   Adversely impact operational efficiency - impacts bottomline o  Which metric to use?   Traffic   Incoming, Outgoing   CPU   Load3 International Conference on Cloud Engineering 2013 © Arun Kejariwal
  • 4. Capitalizing Cloud Elasticity (contd.)   Non-trivial to manage o  Scaling policy   Whether to scale up/down by fixed amount   Whether to scale up/down by percentage of current capacity   What should be the cooldown period?   Efficient exploitation of cloud elasticity: Why Bother? o  Handle high traffic   Deliver best end-user experience o  Contain overall footprint   Reserved vs. On-Demand instances4 International Conference on Cloud Engineering 2013 © Arun Kejariwal
  • 5. Key Contributions   Algorithms 1.  Scale up/down by fixed amount   Target applications: small (< 20-25 nodes) clusters   Case Study 2.  Scale up/down by percentage of current capacity   Target applications: medium/large clusters 3.  Implications of long application start up time?   Example application: Netflix Recommendation Engine   Loading of user metadata (such as users preferences)   Modified Algorithm 2 to capture effects of application start up time   Case Study5 International Conference on Cloud Engineering 2013 © Arun Kejariwal
  • 6. Autoscaling in AWS: Quick review   CloudWatch o  Real-time monitoring of EC2 instances o  Metrics   CPU utilization, latency, network traffic, application specific etc. o  Alarm   Change state when metric > threshold   Action: when metric > threshold for a number of time periods   Policy o  ScalingAdjustment   # instances by which to scale o  Adjustment type   ChangeInCapacity   PercentChangeInCapacity   Cooldown o  Allows effects of scaling activity to become visible6 International Conference on Cloud Engineering 2013 © Arun Kejariwal
  • 7. Techniques: Design Guidelines   Avoid ping-pong effect o  High latency o  SLA violation   Be proactive, not reactive o  Application start up SLA driven need to scale up Autoscaling event7 International Conference on Cloud Engineering 2013 © Arun Kejariwal
  • 8. Techniques: Design Guidelines (contd.)   Aggressive upwards, conservative downwards o  Deliver best user experience   Handle more than expected increase in traffic   Handle slower ramp down of traffic o  Aggressive scale up   Provides buffer for increase in traffic during the cooldown period o  Aggressive scale down   May result in under-provisioning   Impact user experience   Scalability Analysis o  Given an SLA, determine maximum throughput8 International Conference on Cloud Engineering 2013 © Arun Kejariwal
  • 9. Techniques: Properties   RPS -> Requests per second 1.  RPS/node after scale up > Scale down threshold 2.  RPS/node after scale down > Scale up threshold9 International Conference on Cloud Engineering 2013 © Arun Kejariwal
  • 10. Techniques: # 1   Scale up by fixed amount o  Outline   : Scale up threshold (RPS per node)   : Scale up value     Proactive   Empirically determined   Example applications o  Beaconserver, customer service o  It does add up!10 International Conference on Cloud Engineering 2013 © Arun Kejariwal
  • 11. Techniques: # 1 (contd.)   Case Study Aggressive upwards Conservative downwards11 International Conference on Cloud Engineering 2013 © Arun Kejariwal
  • 12. Techniques: # 3   Scale up/down by percentage of current capacity o  Account for application start up o  How to model it?   Time series of RPS   : Application start up time, determine rolling RPS change time series   Compute 99th percentile   Compute effective scale up threshold   Consistent with being proactive design guideline12 International Conference on Cloud Engineering 2013 © Arun Kejariwal
  • 13. Techniques: # 3 (contd.) Rolling RPS Change Time Series (Application start up: 30 mins)13 International Conference on Cloud Engineering 2013 © Arun Kejariwal
  • 14. Techniques: # 3 (contd.)   Scale up percentage of current capacity   Outline   : Scale up threshold (RPS per node)   : Scale up value     Proactive   Empirically determined   Example applications o  Merchweb, simsservice, recommendation service, API o  Savings up to 50%14 International Conference on Cloud Engineering 2013 © Arun Kejariwal
  • 15. Techniques: # 3 (contd.)   Case Study15 International Conference on Cloud Engineering 2013 © Arun Kejariwal
  • 16. Wrapping up …   Summary o  Improve operational efficiency in the cloud o  Benefits both small and large clusters o  Up to 50% reduction in operational footprint   Future work o  How to handle spikes? o  Capture interaction between different services in a SOA o  Autoscale across IaaS vendors16 International Conference on Cloud Engineering 2013 © Arun Kejariwal
  • 17. Q&A17 International Conference on Cloud Engineering 2013 © Arun Kejariwal