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Cloud Auto-Scaling with Deadline and Budget Constraints

Ming Mao, Jie Li, Marty Humphrey eScience Group CS Department, University of Virginia Grid 2010 – Oct 27, 2010. Cloud Auto-Scaling with Deadline and Budget Constraints. Cloud Computing. A fast growing computing platform

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Cloud Auto-Scaling with Deadline and Budget Constraints

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  1. Ming Mao, Jie Li, Marty Humphrey eScience Group CS Department, University of Virginia Grid 2010 – Oct 27, 2010 Cloud Auto-Scaling with Deadline and Budget Constraints

  2. Cloud Computing • A fast growing computing platform • IDC - Cloud spending increases 27.4% a year to $56 billion (compared 5% a year of traditional IT) • $16.5 billion (2009) -> $55.5 billion (2014) src: Worldwide and Regional Public IT Cloud Service 2010-2014 Forecast • Two most quoted benefits • Scalable computing and storage • Reduced cost • Concerns • Security, availability, cost management, integration interoperability, etc.

  3. Cost • Q1. Cost – the most important factor in practice? • Q2. Moving into Cloud == Reduced Cost ?

  4. Current Auto-Scaling Mechanisms • Resource utilization information based triggers (e.g. AWS auto-scaling, RightScale, enStratus, Scalr, etc)

  5. Where does the gap exist? • Multiple instance types • Current billing models • Full hour billing • Non-ignorable instance acquisition time • 7-15 min in Windows Azure • More specific performance goals • Budget awareness (e.g. dollars/month, dollars/job)

  6. Problem Statement • Deadline (Job finish time) • Cost Problem Statement – how to enable cloud applications to finish all the submitted jobs before user specified deadline with as little money as possible using auto-scaling.

  7. Cloud Application Performance Model • Workload are non-dependent jobs submitted in the job queue • FCFS manner and fairly distributed • Different classes of jobs • Same performance goal (e.g.1 hour deadline) • VM instances take time to startup

  8. Problem Formalization (1) Key variables used in the model

  9. Problem Formalization (2) • Workload • Computing Power of Instance Running Instance Pending Instance

  10. Problem Formalization (3) • Scale up • Sufficient budget • Insufficient budget • Scale down

  11. An example Workload Required Computing Power where

  12. Windows Azure Implementation

  13. Evaluation - Simulation Workload & VM simulation parameters

  14. Stable workload & changing deadline

  15. Changing workload & fixed deadline

  16. Cost

  17. Evaluation - MODIS • MODIS 200X – Year Terra & Aqua – Satellite (X - Y) – Day X to day Y 15 images / day Moderate scale test (up to 20 instances) Large Scale test (up to 90 instances) * C.H. – computing hour 1C.H. = 0.12$ in Windows Azure

  18. Evaluation - MODIS • Test: Terra & Aqua 2006(1-75) - total 1125 jobs 6min early theoretical cost - 93 C.H. or 11.16$ actual cost - 132 C.H. or 15.84$

  19. Conclusions & Future works • Conclusions • More cost-efficient than fixed-size instance choice • VM startup delay can affect hugely in practice • Future works • More general cloud application model • Multiple job classes • Consider other instance types (e.g. spot instances & reserved instances) • Data transfer performance and storage cost

  20. Thank you

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