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Ming Mao, Jie Li, Marty Humphrey             eScience Group   CS Department, University of Virginia        Grid 2010 – Oct...
   A fast growing computing platform     IDC - Cloud spending increases 27.4% a year to $56      billion (compared 5% a ...
        Q1. Cost – the most important factor in                       practice?                      Rate the benefits co...
   Resource utilization information based triggers (e.g.    AWS auto-scaling, RightScale, enStratus, Scalr, etc)
   Multiple instance types   Current billing models     Full hour billing   Non-ignorable instance acquisition time   ...
Cloud   Deadline               Users                                       Application    (Job finish time)              ...
   Workload are non-dependent jobs submitted    in the job queue   FCFS manner and fairly distributed   Different class...
nitjIVii,i jd          Key variables used in the model
   Workload     W  (J j , nj )   Computing Power of Instance I i                       D  nj    P  (J j ,            ...
   Scale up     Sufficient budget     Min(i ctype ( Ii ) )         P  W  P                                          ...
Workload                           Required Computing Powerj1 :  x  60 10  10   40          j1 : 10           ...
Cloud Cruise Control            notify                           Decider admin                    Min( i ctype ( Ii ) ) &...
Workload & VM simulation parameters                    Mix             Computing          IO Intensive               Avg 3...
Stable Worload & Changing DeadlineResponse (sec)                                            Utilization (%)               ...
Changing Workload & Fixed DeadlineResponse (sec)                                                Worload (job/h) 4000      ...
VM Types               Total Cost ($)                                         % more than optimalChoice #1             Gen...
   MODIS200X – Year                                   Terra & Aqua – Satellite(X - Y) – Day X to day Y                   ...
   Test: Terra & Aqua 2006(1-75) - total 1125 jobs                  6min early                  theoretical cost - 93 C.H...
   Conclusions     More cost-efficient than fixed-size instance choice     VM startup delay can affect hugely in practi...
Cloud auto-scaling with deadline and budget constraints
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Cloud auto-scaling with deadline and budget constraints

http://www.cs.virginia.edu/~mm5bw/papers/CloudAutoScaling.pdf

http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5697966&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5697966

www.mingmao.org

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Cloud auto-scaling with deadline and budget constraints

  1. 1. Ming Mao, Jie Li, Marty Humphrey eScience Group CS Department, University of Virginia Grid 2010 – Oct 27, 2010
  2. 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 2010-2014 Forecast Two most quoted benefits  Scalable computing and storage  Reduced cost Concerns  Security, availability, cost management, integration interoperability, etc.
  3. 3.  Q1. Cost – the most important factor in practice? Rate the benefits commonly ascribed to the How important is it that Cloud service providers... cloud on-demand model Offer competitive pricing 91.60% Pay only for what you use 77.90% Offer Service Level Agreements 88.60% Easy/fast to deply to end-users Option to move cloud offerings back on premise 87.80% 77.70% Provide a complete solution 86.00% Monthly payments 75.30% Understand my business and industry 84.50% Encourages standard systems 68.50% Allow managing on-premise & cloud together 82.10% Requires less in-house IT staff, costs 67.00% Support many of my IT needes 81.00% Alwasys offers latest functionality 64.60% Offer both on-premise and public cloud services 79.20%Sharing systems with partners simpler 63.90% Are a technology and business model innovator 78.30% Seems like the way of future 54.00% Have local presence, can come to my offices 72.90% 0.00% 20.00% 40.00% 60.00% 80.00% 100.00% 0.00% 20.00% 40.00% 60.00% 80.00% 100.00% Source: IDC Enterprise Panel, 3Q09, n = 263, Sep 2009 Source: IDC Enterprise Panel, 3Q09, n = 263, Sep 2009  Q2. Moving into Cloud == Reduced Cost ?
  4. 4.  Resource utilization information based triggers (e.g. AWS auto-scaling, RightScale, enStratus, Scalr, etc)
  5. 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. 6. Cloud Deadline Users Application (Job finish time) Cloud Server Cost Job 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. 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. 8. nitjIVii,i jd Key variables used in the model
  9. 9.  Workload W  (J j , nj ) Computing Power of Instance I i D  nj P  (J j , ) Running Instance  i j t j ,type ( Ii ) n j ( D  (dtype ( Ii )  si ))  n jP  (J j , )  j t j ,type ( Ii ) n j Pending Instance i
  10. 10.  Scale up  Sufficient budget Min(i ctype ( Ii ) )  P W  P i i  Insufficient budget Max( Pi ) c i type ( Ii )  C  i ctype ( Ii ) Scale down  P  P W i i s
  11. 11. Workload Required Computing Powerj1 :  x  60 10  10   40 j1 : 10  10  10  x   45j2 :  y   60   5    20  35           j2 : n1  5   n2 20  n3 10   y   35          j3 :  z  60  20  5  35           j3 :  20 5 10  z  35           P W I1 I2 V1 V2 V3 P Min(c1n1  c2 n2  c3n3 ) where c1n1  c2 n2  c3n3  ctype( I1 )  ctype( I2 )  C
  12. 12. Cloud Cruise Control notify Decider admin Min( i ctype ( Ii ) ) &  Pj  W  P dynamic j configuration vm plan VM Monitor Repository Manager +, – Config workload update update vm infoenqueue Historical VM instances Data users dequeue
  13. 13. Workload & VM simulation parameters Mix Computing IO Intensive Avg 30 jobs/hour Intensive Avg 30 jobs/hour STD 5 jobs/hour Avg 30 jobs/hour STD 5 jobs/hour STD 5 jobs/hour General Average 300s Average 300s Average 300s0.085$/hour STD 50s STD 50s STD 50sDelay 600s High-CPU Average 210s Average 75s Average 300s0.17$/hour STD 25s STD 15s STD 50sDelay 720s High-IO Average 210s Average 300s Average 75s0.17$/hour STD 25s STD 50s STD 15sDelay 720s
  14. 14. Stable Worload & Changing DeadlineResponse (sec) Utilization (%) 100.00%7000 90.00%6000 80.00%5000 70.00% 60.00%4000 50.00%3000 40.00%2000 30.00% 20.00%1000 10.00% 0 0.00% 0 10 20 30 40 50 60 70 80 Time (hour) utilization deadline avg max min
  15. 15. Changing Workload & Fixed DeadlineResponse (sec) Worload (job/h) 4000 350 3500 300 3000 250 2500 200 2000 150 1500 100 1000 500 50 0 0 0 10 20 30 40 50 60 70 80 Time (hour) deadline avg max min workload
  16. 16. VM Types Total Cost ($) % more than optimalChoice #1 General 98.52$ (43%)Choice #2 High-CPU 128.86$ (87%)Choice #3 High-IO 129.71$ (88%)Choice #4 General, High-CPU, High-IO 78.62$ (14%) Optimal General, High-CPU, High-IO 68.85$
  17. 17.  MODIS200X – Year Terra & Aqua – Satellite(X - Y) – Day X to day Y 15 images / day Moderate scale test (up to 20 instances) 1hour deadline 2hour deadline 3hour deadline Terra 2004(10-12) 18 min late 8 min early 20 min early Total 45 jobs 9 C.H.or 1.08$ 6 C.H or 0.72$ 5 C.H.or 0.6$ 4 C.H.* or 0.48$ Aqua 2008(30-32) 15min late 20 min early 29 min early Total 45 jobs 10 C.H or 1.2$ 7 C.H.or 0.84$ 5 C.H.or 0.6$ 4 C.H. or 0.48$ Large Scale test (up to 90 instances) 2 hour deadline 4 hour deadline Terra & Aqua 2006(1-75) 20min late 6 min early Total 1125 jobs 170 C.H. or 20.4$ 132 C.H. or 15.84$ 93 C.H. or 11.16$ Terra & Aqua 2006(1-150) Admission Denied 22 min early Total 2250 jobs 243 C.H. or 29.16$ 185 C.H. or 22.2$ * C.H. – computing hour 1C.H. = 0.12$ in Windows Azure
  18. 18.  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$ Instance Acquisition and Release 40 38 36 34 32 30 28 26 Instance Number 24 22 20 18 16 14 12 10 8 6 4 2 0 0 1 2 3 4 5 Time (hour) Released Acquiring Ready
  19. 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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