Power and Performance Management of Virtualized Computing Environments via Lookahead Control

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Power and Performance Management of Virtualized Computing Environments via

Lookahead Control

Dara Kusic1, Jeffrey O. Kephart2, James E. Hanson2, Nagarajan Kandasamy1, and Guofei Jiang3

1- Drexel University, Philadelphia, PA 191042- IBM T.J. Watson Research Center, Hawthorne, NY 10532

3- NEC Labs America, Princeton, NJ 08540

Presented by Tongping Liu

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OUTLINEMotivation and problem statementDescription of the experimental testbedProblem formulation and controller designPerformance resultsConclusions

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DATA-CENTER ENERGY COSTSServer energy

consumption is growing at 9% per year

Carbon dioxide emissions as percentage of world total – industries

Carbon emissions by countries (Metric tons of CO2 per year)

0.30.6

0.8 1.0

Data centers

Airlines Shipyards Steel plants

170 142 146 178

Data centers

Argentina Nether-lands

MalaysiaMcKinsey & Co. Report: http://uptimeinstitute.org/content/view/168/57

Data centers are projected to surpass the airline industry in CO2 emissions by 2020

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SERVER UTILIZATION IN DATA CENTERS

Server utilization averages about 6%, accounting for idle servers

Average daily server utilization (%)

10

20

30

40

50

60

70

80

90

100

Peak daily server utilization (%)

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 90 100

Up to 30% of servers are idle!

McKinsey & Co. Report: http://uptimeinstitute.org/content/view/168/57

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Performance-isolated platforms, called virtual machines (VMs), allow resources (e.g., CPU, memory) to be shared on a single server

VIRTUALIZATION AS THE ANSWER

TechniqueEfficiency

Impact

• 3-5%

Deploy virtualization for existing and new demand

• 25-30%

Implement free cooling • 0-15%

Introduce greener and more power efficient servers

• 10-20%

Selectively turn off core components to increase remaining unit efficiency

McKinsey & Co. Report: http://uptimeinstitute.org/content/view/168/57

Enables consolidation of online services onto fewer servers

Increases per-server utilization and mitigates “server sprawl”

Enables on-demand computing, a provisioning model where resources are dynamically provisioned as per workload demand

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We address combined power and performance management in a virtualized computing environment

– The problem is posed as one of sequential optimization under uncertainty and solved using limited look-ahead control (LLC)

– The notion of risk is encoded explicitly in the problem formulationSummary of main results

– A server cluster managed using LLC saves 26% in power-consumption costs over a 24 hour period when compared to an uncontrolled system

– Power savings are achieved with very few SLA violations (1.6% of the total number of requests)

PROBLEM STATEMENT

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THE EXPERIMENTAL TESTBED

Gol

dW

ebS

pher

e

O S

Chronos D em eter

D ispatcher

W orkload A rrivals

Application T ier

Apollo Poseidon

O perational H osts Powered-dow nH osts

Database T ie r

S leep

Sleep

f11(k ) f21(k ) f12(k) f13(k )

LLCContro llable Param eters:

host on/o ff: N (k), VM on/off: n i(k),W ork load fraction: (k), C PU share f(k)

Eros

f2n(k )

Eros

Bacchus

Gol

dW

ebS

pher

e

O S

Gol

dW

ebS

pher

e

O S

Silv

erW

ebS

pher

e

O S

Silv

erW

ebS

pher

e

O S

Gol

dD

B2

O S

Silv

erD

B2

O S

Gol

dD

B2

O S

Silv

erD

B2

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Silv

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B2

O S

Silv

erW

ebS

pher

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Gol

dD

B2

O S

f22(k )

)(),( 21 kk

The testbed is a two-tier architecture with front-end application servers and back-end databases

It hosts two online services (Gold and Silver)

Servers are virtualized Performance goals

– Minimize power consumption– Minimize SLA violations

We target the application and the database tiers

)(),( 1}..1{21}..1{1 kk nn

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EXPERIMENTAL SYSTEM Six Dell servers (models 2950 and 1950) comprise the experimental testbed Virtualization of the CPU and memory is enabled by VMware ESX Server 3.0 Virtual machines run SUSE Enterprise Linux Server Edition 10 Control directives use the VMware API, Linux shell commands, and IPMI Silver application is Trade6 only; Gold application is Trade6 + extra CPU load

Host name CPU speed # of CPU cores MemoryApollo 2.3 GHz 8 8 GBBacchus 2.3 GHz 2 8 GBChronos 1.6 GHz 8 4 GBDemeter 1.6 GHz 8 4 GBEros 1.6 GHz 8 4 GBPoseidon 2.3 GHz 8 8 GB

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We assume a session-less workload, i.e., incoming requests are independent of each other

The transaction mixed is fixed to a constant proportion of browse/buy requests

CHARACTERISTICS OF THE INCOMING WORKLOAD

0 100 200 300 400 5000

0.5

1

1.5

2

2.5

3x 10

4

Time instance in 150 second increments

Num

ber o

f arri

vals

Arrivals per Time Instance, Workload 1

Silver

Gold

The workload to the computing system is time varying and shows significant variability over short time periods

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APPLICATION ENVIRONMENTOnline services are enabled by enterprise applications

Application server

Database

Web Clients Trade Action

Trade Servlets

Trade Services

DB2 Database

Trade Server Pages

WebSphere Application Server

Trade6 is an example–It is transaction-based stock trading application from IBM

–It can be hosted across one or more servers in a multi-tier architecture

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The power/performance management problem is posed as a dynamic resource provisioning problem under dynamic operating constraints

Objectives– Maximize the profit generated by the system (i.e., minimize SLA

violations and the power consumption cost)Decisions to be optimized

– Number of servers to turn on or off– Number of VMs to provision to each service – The CPU share given to each VM – Distribute incoming workload to different servers

PROBLEM FORMULATION

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))](())(())(),(([Maximizeu

kuSkuOkukxRk

PROBLEM FORMULATION (Contd.)

0

G old S LA

S ilver SLA

Response Time, ms

Rev

enue

, dol

lars

7e -5

3e -5

-3e -5

0 100 200 300

A stepwise pricing SLA for the online services

5e -5

1e -5

-1e -5

400

Refund

Reward

Dollars generated (Revenue)–Obtained as per a (nonlinear)

reward-refund curve specified by the SLA

Reward is defined by SLA for each service class

Violation of SLA results in a refund to client

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))](())(())(),(([Maximizeu

kuSkuOkukxRk

Key characteristics of the control problem– Some control actions have (long) dead times; e.g., switching on a server,

instantiating VMs, migrating VMs– Decisions must be optimized over a discrete domain– Optimization must be performed quickly, given the dynamics of input

We use a limited look-ahead control (LLC) concept

PROBLEM FORMULATION (Contd.)

Power consumption cost of operating servers

Switching costs– Opportunity cost lost due

to the unavailability of servers/VMs involved in provisioning decisions

– Transient power consumption costs

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THE LLC FRAMEWORK

LLC is same as model predictive control, but in a discrete domain and quickly

Advantages– Use predictions to improve control performance– Robust (iterative feedback) even in dynamic operating conditions– Inherent compensation for dead times– Multi-objective and non-linear optimization in the discrete domain under

explicit constraints

System

Predictivefilter

Systemm odel

System O ptim izer

W orkloadforecast

C ontro linput to

evaluate

Estim atedstate

W orkloadarriva l

O bservedstate

Contro linput

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THE LLC FRAMEWORK (Contd.)

],1[ :horizon Prediction

hkk

k+4k+3k+2 Use a system model to estimate future system states over a prediction horizon

k+1

Obtain an “optimal” sequence of control inputs

)1(ˆ kx

)2(ˆ kx )(ˆ hkx )3(ˆ kx

Apply the first control input in the sequence at time k +1; discard the rest

x(k)

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System

Predictivefilter

Systemm odel

System O ptim izer

W orkloadforecast

Contro linput to

eva luate

Estim atedsta te

W orkloadarriva l

O bservedstate

Contro linput

WORKLOAD ESTIMATION USING A PREDICTIVE FILTER

0 50 100 150 200 250 300 350 400 450 5000

0.5

1

1.5

2

2.5

3x 10

4 Kalman Filter Workload Estimates, Workload 1

Num

ber o

f arri

vals


Silver

Gold

Kalman estimate: dotted lineActual value: solid line

A Kalman filter is used to estimate the workload over the prediction horizon

Prediction error is about 8%

Training phase

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CONSTRUCTING THE SYSTEM MODEL

20 25 30 350

100

200

300

400

500

600

700

800Measured Average Response Time for the Gold Application

Workload in arrivals per second

Res

pons

e tim

e in

ms

3 GHz CPU Share VM

4.5 GHz CPU Share VM

6GHz CPU Share VM

Gold SLA

System model will base on observed state, control input and estimated workload to create new state.

The behavior of each application is captured using simulation-based learning and stored in an approximation structure (e.g., lookup table, neural network) – OFFLINE mode

System

Predictivefilter

Systemm odel

System O ptim izer

W orkloadforecast

C ontro linput to

eva luate

Estim atedsta te

W orkloadarrival

O bservedstate

C ontro linput

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20 25 30 350

100

200

300

400

500

600

700



Res

pons

e tim

e in

ms

3 GHz CPU Share VM


6GHz CPU Share VM

Gold SLA


Example 1: Given a 3 GHz CPU share and 1 GB of memory, how many requests can a Gold VM handle before incurring SLA violations?

Average response time is below the limit doesn’t mean that no violations.

System

Predictivefilter

Systemm odel

System O ptim izer

W orkloadforecast

C ontro linput to

eva luate

Estim atedsta te

W orkloadarrival

O bservedstate

C ontro linput

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20 25 30 350

100

200

300

400

500

600

700



Res

pons

e tim

e in

ms

3 GHz CPU Share VM


6GHz CPU Share VM

Gold SLA


Example 2: Given a 6 GHz CPU share and 1 GB of memory, how many requests can a Gold VM handle before incurring SLA violations?

System

Predictivefilter

Systemm odel

System O ptim izer

W orkloadforecast

C ontro linput to

eva luate

Estim atedsta te

W orkloadarrival

O bservedstate

C ontro linput

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Observation – non-linear behavior

20 25 30 350

100

200

300

400

500

600

700



Res

pons

e tim

e in

ms

3 GHz CPU Share VM


6GHz CPU Share VM

Gold SLA

3G: 22 requests, 6G: 29 requests, why we can’t achieve 2 speedup if CPU share is 2 times??

Memory or IO is not considered????

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CONSTRUCTING THE SYSTEM MODEL (Contd.)

Power = current * voltage Two observations:

– Power consumption of boot time is larger than idle state. – Power consumption having VMs is not greatly larger than idle state.

1 2 3 4 5 6 7 8 9 100

50

100

150

200

250

300

350

Host state

Pow

er c

onsu

med

in W

atts

Power Consumption per Host Machine State

Host states: 1 - Standby 2 - Boot, 1st 20 sec. 3 - Boot, remaining 2 min. 35 sec. 4 - Idle, 0 vms 5 - Boot 1st vm 6 - Idle, 1vm 7 - Boot 2nd vm 8 - Idle, 2 vms 9 - Workload on 1 vm 10 - Workload on 2 vms

Dell PowerEdge 1950

Dell PowerEdge 2950

System

Predictivefilter

Systemm odel

System O ptim izer

W orkloadforecast

C ontro linput to

eva luate

Estim atedsta te

W orkloadarrival

O bservedstate

C ontro linput

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CONSTRUCTING THE SYSTEM MODEL (Contd.) - Power consumptions

Power consumption is closely related to CPU usage.

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Does increase of CPU utilization increase Computer Power consumption?

The more utilization of the CPU, the more signals generated and processed by it.

Consequently, the more utilization of the CPU, the greater the energy requirement.

Power = energy x time. So we can than conclude that the greater the

CPU utilization the greater the power consumption.

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Key Observations (1) Idle machine consumes 70% or more

power of full utilization Conclusion (1): Power down machine to achieve

maximum power savings.(2) Intensity of the workload at VMs doesnot

affect power consumption and cpu utilization. Conclusion (2): Only the number of VMs can

affect power consumption.

(3) Power consumed by a server is a function of instantiated VMs on it.

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CPU scheduling modework-conservative mode (WC-mode): in order to keep the

server resources well utilized– Under WC-mode, the shares are merely guarantees, and CPU is idle if and

only if there is no runnable work.Non-work-conservative:

– With NWC-mode, the shares are caps, i.e., each client owns its fraction of the CPU.

– It means that if one VM is assigned to 3G HZ cpu, this VM cannot use more than this even if the system is 10G HZ and no other VM at all.

Assumption: – Esx server is worked on non-work-conservative mode– Cpu assignment is not larger than maximum limit of hardware.

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System

Predictivefilter

Systemm odel

System O ptim izer

W orkloadforecast

C ontro linput to

eva luate

Estim atedsta te

W orkloadarrival

O bservedstate

C ontro linput

DEVELOPING THE OPTIMIZER Issue 1: Risk-aware control

– Due to the energy and opportunity costs incurred when switching hosts and VMs on/off, excessive switching caused by workload variability may actually reduce profits

– We need to encode a notion of risk in the cost function

Cost that accumulates during the time a server is being turned on but is unavailable to perform any useful service

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Environment-input estimates will have prediction errors

We encode a notion of risk in the optimization problem– Generate a set of expected next states for lots of the predicted environment inputs

RISK-AWARE CONTROL

)()(ˆ)(ˆ)()(ˆ jjjjj iiiii

Construct an uncertainty bound for environment input of interest:

Averaged past observed error between actual and forecasted arrival rate

Estimated environment

input

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RISK-AWARE CONTROL (Contd.) A utility function encodes risk into the objective function

hk

kj iiiiiujujXU

1

2

}{)(),(ˆmax

Maximize utility over horizon and client classes

Utility model with tunable risk-preference parameter, β

Apply a mean-square variance model of utility

Formulate a utility maximization problem

β = 0 : risk-neutral

β > 0 : risk-averseβ < 0 : risk-seeking

Tunable risk-preference parameter, β

Uncertainty as variance

A > 2*Mean

ijXjXjXAR iiii

2)(ˆmean)(ˆvar)(ˆmeanU

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System

Predictivefilter

Systemm odel

System O ptim izer

W orkloadforecast

Contro linput to

eva luate

Estim atedsta te

W ork loadarrival

O bservedstate

Contro linput

DEVELOPING THE OPTIMIZER (Contd.)

We use a control hierarchy to reduce execution-time overhead– An L0 controller decides the CPU share to assign to VMs

– An L1 controller decides the number of VMs for each service and the number of servers to keep powered on

– The average execution time of the L1 controller is about 10 seconds

Issue 2: Execution-time overhead of the controller

– “Curse of dimensionality” - Problem will show an exponential increase in worst-case complexity with more control options and longer prediction horizons

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EXPERIMENTAL PARAMETERS

Parameter Value

Cost per KiloWatt hour $0.3

Time delay to power on a VM 1 min. 45 sec.

Time delay to power on a host 2 min. 55 sec.

Prediction horizon L1: 3 steps, L0: 1 step

Control sampling period L1: 150 sec, L0: 30 sec

Initial configuration for Gold service (application tier) 3 VMs

Initial configuration for Silver Service (application tier) 3 VMs

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MAIN RESULTSA risk-neutral controller conserves, on average, 26% more

energy than a system without dynamic control with very few SLA violations

Workload Energy savings % of SLA violations (Silver)

% of SLA violations (Gold)

Workload 1 18% 3.2% 2.3%

Workload 2 17% 1.2% 0.5%

Workload 3 17% 1.4% 0.4%

Workload 4 45% 1.1% 0.2%

Workload 5 32% 3.5% 1.8%

More SLA violations for Silver requests than for Gold requests.

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RESULTS (Contd.)

0 500 1000 1500 2000 2500

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

x 104

Tota

l CP

U c

ycle

s pe

r sec

ond


Total CPU Speed, Gold Application , Workload 1

0 500 1000 1500 2000 2500

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8x 10

4 Total CPU Speed, Silver Application, Workload 1


Tota

l CP

U c

ycle

s pe

r sec

ond

CPU shares assigned to the Gold and Silver applications over a 24-hour period – L0 layer

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RESULTS (Contd.)Number of virtual machines assigned to the Gold and Silver

applications over a 24-hour period – L1 layer

100 200 300 400 5000

1

2

3


Num

ber o

f virt

ual m

achi

nes

on

Switching Activity, Gold Application, Workload 1

100 200 300 400 5000

1

2

3

Num

ber o

f virt

ual m

achi

nes

on


Switching Activity, Silver Application, Workload 1

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EFFECT OF THE RISK PREFERENCE PARAMETER

A risk-averse ( = 2) controller conserves about the same amount of energy as a risk-neutral ( = 0) controller

WorkloadEnergy savings (risk-

neutral control) ( = 0)

Energy savings (risk-averse control)

( = 2)

Workload 6 20.8 % 20.9 %

Workload 7 25.3 % 25.2 %

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EFFECT OF THE RISK PREFERENCE PARAMETER (Contd.)

A risk-averse controller ( = 2) maintains a higher QoS (Less violations) than a risk-neutral ( = 0) controller by reducing switching activity

WorkloadSLA violations (risk-

neutral control) ( = 0)

SLA violations (risk-averse control)

( = 2)

% reduction in SLA violations

Workload 6 28,635 (2.3%) 15,672 (1.7%) 45%

Workload 7 34,201 (2.7%) 25,606 (2.0%) 25%

WorkloadSwitching activity (risk-neutral control)

( = 0)

Switching activity (risk-averse control)

( = 2)

% reduction in switching activity

Workload 6 30 28 7%

Workload 7 40 30 25%

Best-case risk-averse controller:

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OPTIMALITY CONSIDERATIONSThe controller cannot achieve optimal performance

– Limited by errors in workload predictions– Limited by constrained control inputs– Limited by a finite prediction horizon

Controller Total Energy Savings Total SLA violations Num. times hosts

switchedRisk neutral 25.3% 34,201 (2.7%) 40Risk averse 25.2% 25,606 (2.0%) 38

Oracle 16.3% 14,228 (1.1%) 32

To evaluate optimality, profit gains of a risk-neutral and best-case risk-averse controller were compared against an “oracle” controller with perfect knowledge of the future

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CONCLUSIONSWe have addressed power and performance management in a

virtualized computing environment within a LLC frameworkThe cost of control and the notion of risk is encoded explicitly in

the problem formulation A server cluster managed using LLC saves 26% in power-

consumption costs over a 24 hour period when compared to an uncontrolled system

Power savings are achieved with very few SLA violations (1.6% of the total number of requests)

Our recommendation is a risk-averse controller since it reduces SLA violations and switching activity

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Conclusion (1) – Why significant?

Why significant?– Using virtualization, implement a dynamic

resource provisioning model– Integrate power and performance

management, reduce energy cost (26%) while causing little SLA( service level agreement) violation (less than 3%)

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Conclusion(2) - Alternate approach?

Alternate approach?Technique

EfficiencyImpact

• 3-5%

Deploy virtualization for existing and new demand

• 25-30%

Implement free cooling • 0-15%

Introduce greener and more power efficient servers

• 10-20%

Selectively turn off core components to increase remaining unit efficiency

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Conclusion (3) – Improvement?

Simplify the control logic to reduce the exe. time

Integrate the memory usage when modify VM configuration

Provide a mechanism to decide the granularity to create VMs – One 6G VM can handle more requests than that of two 3G VMs.

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SCALABILITYExecution time of the controller can be reduced

through various techniques– Approximating control– Implementing the controller in hardware– Increasing the number of tiers in the control hierarchy– Simplifying the iterative search process to “hold” a control

input constant over the prediction horizon

System

Pred ictivefilter

Systemm odel

System O ptim izer

A neural network or regression tree can be trained to learn the decision-making behavior of the optimizer

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Scalability problem

Scalability is not good, current result is based on 5 hosts only. But there can be dozens or thousands of servers in actual data center.

» 5 hosts - < 10 sec»10 hosts - 2min. 30 sec»15 hosts – 30 min.

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Questions?

Thank you!

Documents

Power and Performance Management of Virtualized Computing Environments via Lookahead Control