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© 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