cloud auto-scaling with deadline and budget constraints

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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

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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 - PowerPoint PPT Presentation

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Page 1: Cloud Auto-Scaling with Deadline and Budget Constraints

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

Page 2: Cloud Auto-Scaling with Deadline and Budget Constraints

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.

Page 3: Cloud Auto-Scaling with Deadline and Budget Constraints

Cost

Q1. Cost – the most important factor in practice?

Q2. Moving into Cloud == Reduced Cost ?

54.00%

63.90%

64.60%

67.00%

68.50%

75.30%

77.70%

77.90%

0.00% 20.00% 40.00% 60.00% 80.00% 100.00%

Seems like the way of future

Sharing systems with partners simpler

Alwasys offers latest functionality

Requires less in-house IT staff, costs

Encourages standard systems

Monthly payments

Easy/fast to deply to end-users

Pay only for what you use

Source: IDC Enterprise Panel, 3Q09, n = 263, Sep 2009

Rate the benefits commonly ascribed to the cloud on-demand model

72.90%78.30%79.20%81.00%82.10%

84.50%86.00%87.80%88.60%

91.60%

0.00% 20.00% 40.00% 60.00% 80.00% 100.00%

Have local presence, can come to my officesAre a technology and business model innovatorOffer both on-premise and public cloud services

Support many of my IT needesAllow managing on-premise & cloud together

Understand my business and industryProvide a complete solution

Option to move cloud offerings back on premiseOffer Service Level Agreements

Offer competitive pricing

Source: IDC Enterprise Panel, 3Q09, n = 263, Sep 2009

How important is it that Cloud service providers...

Page 4: Cloud Auto-Scaling with Deadline and Budget Constraints

Current Auto-Scaling Mechanisms

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

Page 5: Cloud Auto-Scaling with Deadline and Budget Constraints

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)

Page 6: Cloud Auto-Scaling with Deadline and Budget Constraints

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.

CloudApplication

Users

Job

Cloud Server

Page 7: Cloud Auto-Scaling with Deadline and Budget Constraints

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

Page 8: Cloud Auto-Scaling with Deadline and Budget Constraints

Problem Formalization (1)ijinijiViI idiV,i jt

Key variables used in the model

Page 9: Cloud Auto-Scaling with Deadline and Budget Constraints

Problem Formalization (2)

Workload

Computing Power of Instance Running Instance

Pending Instance

( , )j jW J n

, ( )

( , )i

ji j

j type I jj

D nP J

t n

( )

, ( )

( ( ))( , )i

i

type I i ji j

j type I jj

D d s nP J

t n

iI

Page 10: Cloud Auto-Scaling with Deadline and Budget Constraints

Problem Formalization (3)

Scale up Sufficient budget

Insufficient budget

Scale down

'iiP W P ( ')( )

itype IiMin c

( ')iMax P ( ') ( )i itype I type Ii ic C c

i siP P W

Page 11: Cloud Auto-Scaling with Deadline and Budget Constraints

An example

Workload Required Computing Power

1

2

3

21

: 60 10 10 40: 60 5 20 35: 60 20 5 35

'

j xj yj z

P W I I

1

2 1 2 3

3

1 2 3

: 10 10 10 45: ' 5 ' 20 ' 10 35: 20 5 10 35

'

j xj n n n yj z

V V V P

1 1 2 2 3 3( ' ' ')Min c n c n c n

1 21 1 2 2 3 3 ( ) ( )' ' ' type I type Ic n c n c n c c C where

Page 12: Cloud Auto-Scaling with Deadline and Budget Constraints

Windows Azure Implementation

Cloud Cruise Control

Decider

&

Monitor Repository VMManager

Config

VM instancesHistorical Data

workload

dequeue

enqueue

update update

+ , –

vm plan

vm info

( ')( )itype Ii

Min c 'jjP W P admin

users

dynamicconfiguration

notify

Page 13: Cloud Auto-Scaling with Deadline and Budget Constraints

Evaluation - Simulation

MixAvg 30 jobs/hourSTD 5 jobs/hour

Computing Intensive

Avg 30 jobs/hourSTD 5 jobs/hour

IO IntensiveAvg 30 jobs/hourSTD 5 jobs/hour

General0.085$/hourDelay 600s

Average 300sSTD 50s

Average 300sSTD 50s

Average 300sSTD 50s

High-CPU0.17$/hourDelay 720s

Average 210sSTD 25s

Average 75sSTD 15s

Average 300sSTD 50s

High-IO0.17$/hourDelay 720s

Average 210sSTD 25s

Average 300sSTD 50s

Average 75sSTD 15s

Workload & VM simulation parameters

Page 14: Cloud Auto-Scaling with Deadline and Budget Constraints

Stable workload & changing deadline

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%

0

1000

2000

3000

4000

5000

6000

7000

0 10 20 30 40 50 60 70 80

Utilization (%)Response (sec)

Time (hour)

Stable Worload & Changing Deadline

utilization deadline avg max min

Page 15: Cloud Auto-Scaling with Deadline and Budget Constraints

Changing workload & fixed deadline

0

50

100

150

200

250

300

350

0

500

1000

1500

2000

2500

3000

3500

4000

0 10 20 30 40 50 60 70 80

Worload (job/h)Response (sec)

Time (hour)

Changing Workload & Fixed Deadline

deadline avg max min workload

Page 16: Cloud Auto-Scaling with Deadline and Budget Constraints

CostVM 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$

Page 17: Cloud Auto-Scaling with Deadline and Budget Constraints

Evaluation - MODIS MODIS200X – 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 deadline 2hour deadline 3hour deadlineTerra 2004(10-12)

Total 45 jobs4 C.H.* or 0.48$

18 min late 8 min early 20 min early9 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 late 20 min early 29 min early10 C.H or 1.2$ 7 C.H.or 0.84$ 5 C.H.or 0.6$

2 hour deadline 4 hour deadlineTerra & Aqua 2006(1-75)

Total 1125 jobs93 C.H. or 11.16$

20min late170 C.H. or 20.4$

6 min early132 C.H. or 15.84$

Terra & Aqua 2006(1-150)Total 2250 jobs

185 C.H. or 22.2$

Admission Denied 22 min early243 C.H. or 29.16$

Page 18: Cloud Auto-Scaling with Deadline and Budget Constraints

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$

0 1 2 3 4 5

02468

10121416182022242628303234363840

Time (hour)

Inst

ance

Num

ber

Instance Acquisition and Release

Released Acquiring Ready

Page 19: Cloud Auto-Scaling with Deadline and Budget Constraints

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

Page 20: Cloud Auto-Scaling with Deadline and Budget Constraints

Thank you