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Ming Mao, Jie Li, Marty Humphrey eScience Group CS Department, University of Virginia Grid 2010 – Oct 27, 2010.

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Presentation on theme: "Ming Mao, Jie Li, Marty Humphrey eScience Group CS Department, University of Virginia Grid 2010 – Oct 27, 2010."— Presentation transcript:

1 Ming Mao, Jie Li, Marty Humphrey eScience Group CS Department, University of Virginia Grid 2010 – Oct 27, 2010

2 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 Forecast Two most quoted benefits Scalable computing and storage Reduced cost Concerns Security, availability, cost management, integration interoperability, etc.

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

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

5 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 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 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 Key variables used in the model

9 Workload Computing Power of Instance Running Instance Pending Instance

10 Scale up Sufficient budget Insufficient budget Scale down

11 WorkloadRequired Computing Power where

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13 Mix Avg 30 jobs/hour STD 5 jobs/hour Computing Intensive Avg 30 jobs/hour STD 5 jobs/hour IO Intensive Avg 30 jobs/hour STD 5 jobs/hour General 0.085$/hour Delay 600s Average 300s STD 50s Average 300s STD 50s Average 300s STD 50s High-CPU 0.17$/hour Delay 720s Average 210s STD 25s Average 75s STD 15s Average 300s STD 50s High-IO 0.17$/hour Delay 720s Average 210s STD 25s Average 300s STD 50s Average 75s STD 15s Workload & VM simulation parameters

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16 VM TypesTotal Cost ($) % more than optimal Choice #1General98.52$ (43%) Choice #2High-CPU128.86$ (87%) Choice #3High-IO129.71$ (88%) Choice #4General, High-CPU, High-IO78.62$ (14%) OptimalGeneral, High-CPU, High-IO68.85$

17 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 1hour deadline2hour deadline3hour deadline Terra 2004(10-12) Total 45 jobs 4 C.H.* or 0.48$ 18 min late8 min early20 min early 9 C.H.or 1.08$6 C.H or 0.72$5 C.H.or 0.6$ Aqua 2008(30-32) Total 45 jobs 4 C.H. or 0.48$ 15min late20 min early29 min early 10 C.H or 1.2$7 C.H.or 0.84$5 C.H.or 0.6$ 2 hour deadline4 hour deadline Terra & Aqua 2006(1-75) Total 1125 jobs 93 C.H. or 11.16$ 20min late 170 C.H. or 20.4$ 6 min early 132 C.H. or 15.84$ Terra & Aqua 2006(1-150) Total 2250 jobs 185 C.H. or 22.2$ Admission Denied22 min early 243 C.H. or 29.16$

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

19 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

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