© 2008 Hewlett-Packard Development Company, L.P.The information contained herein is subject to change without notice

Automated Workload Management in Virtualized Data Centers

Xiaoyun ZhuHewlett Packard Laboratories

Sigmetrics 2008 Tutorial: Introduction to Control Theory and Its Application to Computing Systems

SIGMETRICS 2008: Introduction to Control Theory. Abdelzaher, Diao, Hellerstein, Lu, and Zhu.

Outline• Background

− Next generation data center− Server consolidation and virtualization

• Case study− Shared hosting platform− Workload management goals− Problem formulation− Adaptive optimal controller design− Testbed and performance evaluations

• Summary

SIGMETRICS 2008: Introduction to Control Theory. Abdelzaher, Diao, Hellerstein, Lu, and Zhu.

Next generation data centers- the utility computing vision

switchedfabric

processingelements

storageelements

infrastructure on demand

internet

intranet

accesstier

webtier

applicationtier

databasetier

edge routers

routingswitches

authentication, DNS,intrusion detect, VPN

web cache 1st level firewall

2nd level firewall

load balancingswitches

web servers

web page storage(NAS)

databaseSQL servers

storage areanetwork(SAN)

applicationservers

files(NAS)

switches

switches

large scalevirtualized utility fabric

provides application services to millions of users

Multi-tiered applications

SIGMETRICS 2008: Introduction to Control Theory. Abdelzaher, Diao, Hellerstein, Lu, and Zhu.

Server consolidation and virtualization- key technology enablers

• Container: encapsulates a share of server resources− CPU, memory, network I/O, disk I/O− Provides performance isolation

• Actuator: APIs for dynamic resource allocation to containers

• Controller: Workload management tools (e.g., HP WLM) can dynamically size a container to maintain a target utilization

measured utilization

resource allocation

Controller

target utilization

-

error

workload

ContainerActuator

resource shares

SIGMETRICS 2008: Introduction to Control Theory. Abdelzaher, Diao, Hellerstein, Lu, and Zhu.

Case study- a shared hosting platform

workload 2

Resource

Controller

workload 1

DBS_1

Virtualized Server N

●●●

A

S

DBS_2A

S

DBS_MA

S

WS_1

Virtualized Server 1

●●●

A

S

WS_2A

S

WS_MA

S

workload M

AS_1

Virtualized Server 2

●●●

A

S

AS_2A

S

AS_MA

S

app 1

app 2

app M

QoS sensor

QoS sensor

QoS sensor

measured QoS and system metrics

resource allocationdecisions

application QoS goals,QoS differentiation policy

SIGMETRICS 2008: Introduction to Control Theory. Abdelzaher, Diao, Hellerstein, Lu, and Zhu.

Workload management goals• Meets the quality of service (QoS) goal for

every application by providing sufficient resources to each component [ACC’06, FeBID’07]− A predictive controller has been integrated into the

HP Global Workload Manager (gWLM) product

• Ensures high resource utilization (by providing “just enough” resources so that more applications can be hosted in a given server pool)

• Provides service differentiation among co-hosted applications during resource contention− Focus on one type of resource (CPU) − Desired level of differentiation should be

maintained in spite of workload changes

SIGMETRICS 2008: Introduction to Control Theory. Abdelzaher, Diao, Hellerstein, Lu, and Zhu.

Output regulation problem• M applications, each has N tiers• u(k): (M-1) x N inputs:

• y(k): (M-1) outputs:

• Reference:

jCCu

NjMiiju

M

mjjij

ij

server ofcapacity is where,

,...,1 ,1,...,1 ,n applicatio of for tier allocation resource

1

M

iii

M

mm

ii

yiq

Miiq

qy

1

1

1 and ,n applicatiofor QoS measured -

1,...,1 ,n applicatiofor ratio QoS computed ,

1,...,1 ,n applicatiofor ratio QoS desired, Miiy iref

SIGMETRICS 2008: Introduction to Control Theory. Abdelzaher, Diao, Hellerstein, Lu, and Zhu.

Challenges and our solution• Challenges

− No first principle model characterizing the relationship between u and y

− The relationship varies with the workload

• An adaptive optimal controller

Optimal Controller

Minu J(u,A,B)System

Model Estimator

Referenceyref

Resource Entitlements

u

Model parameters

(A,B)Measured QoS ratios

y

SIGMETRICS 2008: Introduction to Control Theory. Abdelzaher, Diao, Hellerstein, Lu, and Zhu.

Online model estimationLinear input-output model for local approximation:

0 1 1[ ,..., , ,... ],

( ) [ ( ),..., ( 1), ( ),..., ( 1)] .

n n

T T T T T

X B B A A

k u k u k n y k y k n

( 1) ( ) ( )y k X k e k

ˆ( 1) ( 1) ( )k y k X k k

( 1) ( ) ( 1)ˆ ˆ( 1) ( )( ) ( 1) ( )

T

T

k k P kX k X k

k P k k

1 1

2

( 1) ( )( ) ( 1) (1 ( 1) ) ( ) ( )

( ) ( )

TT

T

P k kP k P k k k

k k

Online adaptation using RLS:

SIGMETRICS 2008: Introduction to Control Theory. Abdelzaher, Diao, Hellerstein, Lu, and Zhu.

Linear quadratic optimal controller• Minimizing quadratic cost function

• Optimal solution:

2 2|| ( ( 1) ( 1)) || (|| ( ) ( 1) || )refJ W y k y k Q u k u k

~

( ) [0, ( 1), , ( 1), ( ), , ( 1)]T T T T Tk u k u k n y k y k n

^ ^ ^* 1

0 0 0

^ ~

( ) (( ) ) [( )

( ( 1) ( ) ( )) ( 1)]

T T T

Tref

u k W B W B Q Q W B W

y k X k k Q Qu k

SIGMETRICS 2008: Introduction to Control Theory. Abdelzaher, Diao, Hellerstein, Lu, and Zhu.

Experimental validation• 2 HP Proliant servers (N=2)

− 4GB RAM, Gigabit Ethernet− Virtualized using Xen 3.0.3− Credit-based CPU scheduler

• Two Applications (M=2)− RUBiS online auction

benchmark− 22 transaction types (browsing,

bidding, viewing,…)− Apache Web Server− MySQL Database Server− Use response time (RT) as QoS

metric

• C1 = 100%, C2 = 40%

• Ts = 20 sec

Virtual Node I Virtual Node II

WL1

WL2

QoS differentiation

(y = rt1 / (rt1+rt2))

Web Server I

Web Server I

Web Server II

Web Server II

DB Server I

DB Server I

DB Server II

DB Server II

DS2 share

WS1 share (u1)

WS2 share

DS1 share (u2)

SIGMETRICS 2008: Introduction to Control Theory. Abdelzaher, Diao, Hellerstein, Lu, and Zhu.

Evaluation results (I)• Performance with varying references

− Desired QoS ratio yref = 0.3 0.5 0.7− WL1 = WL2 = 500 users

Achieved application QoS

CPU allocation and consumption

Stability and Accuracy

Quick Settling

Small Overshoot

SIGMETRICS 2008: Introduction to Control Theory. Abdelzaher, Diao, Hellerstein, Lu, and Zhu.

Evaluation results (II)• Performance with varying workloads

− Desired QoS ratio yref = 0.7− WL1 = 300500 users, WL2 = 500 users

Achieved application QoS

CPU allocation and consumption

SIGMETRICS 2008: Introduction to Control Theory. Abdelzaher, Diao, Hellerstein, Lu, and Zhu.

Summary• Applied control theory to the design of

workload management solutions for virtualized data centers

• Evaluated a self-tuning optimal controller on a lab testbed

• During resource contention, our controller provides service differentiation to co-hosted applications by automated allocation of shared server resource

• The closed-loop system shows good SASO properties as the reference inputs change or as the workloads vary