Power Aware Virtual Machine Placement

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Power Aware Virtual Machine
Placement
Yefu Wang
Introduction
• Data centers are underutilized
– Prepared for extreme workloads
– Commonly under 20% utilized
• Shutting down unused servers
– Saves more power than DVFS
– Application correctness should
be guaranteed
• Design choices
– Workload redirection
– VM live migration
– Workload redirection + VM live migration
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[Meisner’09]
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Design Choice (1) : Workload Redirection
Web requests
Example: [Heo’07]
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Design Choice (2): VM Live Migration
VM
VM
VM
VM
VM
VM
VM
VM
VM
VM
VM
Example: [Wang’09, Verma’08]
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Design Choice (3): Hybrid
• 6 servers and 12 VMs
• 2 applications: Gold
and Silver
• HTTP requests are
dispatched by a
dispatcher
[Kusic’08]
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pMapper: Power and Migration Cost
Aware Application Placement in
Virtualized Systems
Akshat Verma, Puneet Ahuja, Anindya Neogi
IBM India Research Lab, IIT Delhi
Application Placement
• Static vs. dynamic placement
– Utilization of each server shows
dynamic pattens
– Dynamic placement saves more energy
– A later paper from the save authors
advocates static placement
CPU utilization(0%-100%) are
mapped to grayscale (0-1) in this
figure.
• System administrators have more control
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Application Placement Architecture
DVFS
VM resizing +
idling
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Optimization Formulations
• Cost performance tradeoff
N
max
M
  B(x
i 1
M
i, j
)
j 1
 P(A
I
)  Mig ( Ao , A I )
j 1
Performance benefit
Power
Migration Cost
• Cost Minimization with Performance Constraint
M
min
 P(A
I
)  Mig ( A o , A I )
j 1
• Performance benefit maximization with power constraint
N
max
M
  B(x
i 1
i, j
)  Mig ( A o , A I )
j 1
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System Modeling
• Migration Cost
– Independent of the background workload
– Can be estimated a priori
• Performance modeling
– This paper does not design a performance controller
– pMapper can be integrated with other performance controllers
or models
• Power model
– It is infeasible to have an accurate power model in practice
• Server power consumnption depends on the hosted applications
• The potential server-VM mappings can be huge
– This paper only relies on the power efficiency of servers
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Optimization Formulations
• Cost performance tradeoff
N
max
M
  B(x
i 1
M
i, j
)
j 1
 P(A
I
)  Mig ( Ao , A I )
j 1
Performance benefit
Power
Migration Cost
• Cost Minimization with Performance Constraint
M
min
 P(A
I
)  Mig ( A o , A I )
j 1
• Performance benefit maximization with power constraint
N
max
M
  B(x
i 1
i, j
)  Mig ( A o , A I )
j 1
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mPP Algorithm
VM
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VM
VM
VM
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VM
Server 1
Server 2
Server 3
Server 4
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Server 5
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mPP Algorithm
Server 1
VM
VM
Server 2
VM
VM
Server 3
VM
VM
Server 4
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Server 5
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VM
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mPP Algorithm
Sort VMs by size
Server 1
Server 2
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Server 3
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Server 4
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VM
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Server 5
VM
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mPP Algorithm
Sort servers by slope (power efficiency)
Server 1
Server 2
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Server 3
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Server 4
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VM
VM
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Server 5
VM
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mPP Algorithm
Allocate the VM to servers using First Fit
VM
Server 1
Server 2
Server 3
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Server 4
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Server 5
VM
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mPP Algorithm
VM
VM
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VM
Server 1
VM
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VM
VM
VM
Server 2
Server 3
Server 4
Server 5
•mPP algorithm is oblivious of the last configuration
•May entail large-scale migrations.
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mPPH Algorithm : mPP with History
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mPP
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mPPH Algorithm : mPP with History
VM
VM
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VM
Target
Util.
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Target
Util.
Target
Util.
Receiver
Receiver
Donor
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Donor
Donor
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mPPH Algorithm : mPP with History
VM
VM
VM
VM
VM
VM
VM
VM
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VM
VM
Target
VM
Donor
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Donor
Donor
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mPPH Algorithm : mPP with History
Pick the smallest VMs that add to a migration list
VM
VM
VM
VM
VM
VM
VM
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VM
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Target
VM
Migration list
Donor
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Donor
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mPPH Algorithm : mPP with History
VM
VM
VM
VM
Receiver
Receiver
VM
VM
Donor
Donor
Donor
VM
VM
Migration list
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VM
VM
VM
VM
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mPPH Algorithm : mPP with History
VM
VM
VM
VM
Receiver
Receiver
VM
VM
Donor
Donor
Donor
VM
Migration list
Target
VM
VM
VM
VM
VM
VM
Target
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mPPH Algorithm : mPP with History
VM
VM
VM
VM
VM
Receiver
Receiver
VM
VM
Donor
Donor
Migration list
Target
Donor
VM
VM
VM
VM
VM
VM
Target
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mPPH Algorithm : mPP with History
VM
VM
VM
VM
VM
VM
VM
VM
VM
VM
VM
Receiver
Receiver
Target
Target
VM
VM
Donor
Donor
Donor
•mPPH algorithm tries to minimize migrations by
migrating as few VMs as possible
•pMaP algorithm: before each migration, consider
the benefit and migration cost
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System Implementation
• Testbed
–
–
–
–
Virtulization platform: VMware ESX 3.0
Power monitor: IBM Active Energy Manager
Performance manager: EWLM
DVFS is not implemented
• Simulator
– Replace performance manager with trace data
– Simulating 4 blades, 42 VMs
• Baselines
– Load balanced
– Static
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Power Consumption
mPP, mPPH saves 25% of power
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Algorithm Cost
mPP fails
at high
utilization
pMaP has the least overall cost most of the time
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Conclusion
• Application placement controller pMapper
– Minimize power and migration cost
– Meet performance guarantees
• Also avaiable in the paper:
– More simulation results
– Proof of the propoties of the algorithm for a given platform
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Performance-Controlled Power
Optimization for Virtualized Clusters
with Multi-tier Applications
Yefu Wang and Xiaorui Wang
University of Tennessee, Knoxville
Introduction
• Power saving techniques
– DVFS have a small overhead
– Server consolidation saves more power
• Performnce guarantee
– Multi-tier applications may span multiple VMs
– A controller must respond to a workload variation quickly
• Integrating performance control and server consolidation
is important
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System Architecture
ApplicationApplicationApplicationlevel
Response
level
levelResponse
Response
Time
Controller
Time
TimeController
Controller
ApplicationApplicationApplicationlevel
Response
level
levelResponse
Response
Time
Monitor
Time
TimeMonitor
Monitor
CPU Resource Demands
VM
VM
VM
VM
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VM
VM migration and server sleep/active commands
Power
Optimizer
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Response Time Controller
Application
HTTP requests
VM1
VM2
VM2
Response
Time
Monitor
CPU requirements
MPC Controller
2
P
J (k ) 

t ( k  i | k )  ref ( k  i | k )
i 1
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M 1

Q
Response
Time Model

i0
c(k  i | k )
R (i)
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CPU Resource Arbitrator
• CPU resource arbitrator
– Runs at server level
– Collect the CPU requirements of all VMs
– Decides CPU allocations of all VMs and DVFS level of the server
• The CPU resource a VM receives depends on:
– CPU allocation a ( k )
• Example: give 20% CPU to VM1
– DVFS level f (k )
• Example: Set the CPU frequency to 0 . 66 Fmax
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CPU Resource Arbitrator
• Two observations
– Performance depends
on a ( k ) f ( k )
– Keeping a ( k ) f ( k ) a
constant, a lower f (k )
leads to less power
consumption
• CPU resource arbitrator
– Use the lowest possible
CPU frequency to meet
the CPU requirements of
the hosted VMS
f i ( k )  min{
c
VM
jl
( k )  c 0 , Fmax }
jl  S i
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VM Consolidation for Power Optimization
• Problem formulation
– Minimize the total power consumption of all servers
– Meet all CPU resource requirements
• Power Model
p i ( k )  a i f i ( k )  bi
p i ( k )  a i min{
c
VM
jl
( k )  c 0 , Fmax }  bi
jl  S i
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Optimization Algorithm
1. Minimum slack problem for a single server
2. Power aware consolidation for a list of servers
3. Incremental Power Aware Consolidation (IPAC)
algorithm
4. Cost-aware VM migration
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Minimum slack problem for a single server
Slack=1
Minimum Slack=1
Server
VM
VM
VM
VM
VM
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VM
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VM
VM
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Minimum slack problem for a single server
Slack=0.8
Minimum Slack=0.8
VM
Server
VM
VM
VM
VM
VM
VM
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VM
VM
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Minimum slack problem for a single server
Slack=0.2
Minimum Slack=0.2
VM
VM
VM
Server
VM
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VM
VM
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Minimum slack problem for a single server
Slack=0.2
VM
VM
VM
VM
Server
VM
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VM
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VM
VM
VM
VM
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Minimum slack problem for a single server
Slack=0.5
Minimum Slack=0.2
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Server
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VM
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Minimum slack problem for a single server
Slack=0.2
Minimum Slack=0.2
VM
VM
VM
Server
VM
VM
VM
VM
VM
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VM
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Minimum slack problem for a single server
Slack=0
Minimum Slack=0.2
VM
• Algorithm stops if minimum slack <
• Sounds like exhaustive search?
• Complexity: O ( n u ) u : the maximum
number of VMs a server can host
• Fast in practice [Fleszar’02]
• Gives better solution than FFD
VM
VM
VM
Server
VM
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VM
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Consolidation Algorithm
• Power aware consolidation for a list of servers
– Begin from the most power efficient server
– Use minimum slack algorithm to fill the server with VMs
– Repeate with the next server untill all the VMs are hosted
• Incremental Power Aware Consolidation (IPAC) algorithm
– Everytime only consider these VMs for consolidation:
• Selected VMs on overloaded servers
• The VMs on the least power efficient server
– Repeate until the number of servers does not decrease
• Cost-aware VM migration
– Consider the benefit and migration cost before each migration
• Benefit: power reduction estimated by the power model, etc.
• Cost: administrator-defined based on their policies
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System Implementation
• Testbed
– 4 servers, 16 VMs
– 8 applications (RUBBoS)
– Xen 3.2 with DVFS
• Simulator
– Use CPU utilization trace file for 5415 servers to simulate 5415
VMs
– 400 physical servers with random power models
– 3 different type of CPUs
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Response Time Control
Violation of performance
requirements
Lower power consumption
resulted from DVFS
Our solution
Baseline: pMapper
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Server Consolidation
• 69.6% power is saved
• Response time is still
guaranteed after the
consolidation
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Simulation Results
• IPAC saves more power
than pMapper
– Algorithm gives better
consolidation solutions
– Even more power is saved
by DVFS
• IPAC runs even faster
than pMapper
– Only a small number of
VMs are considered in
each period
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Conclusion
• Performance-controlled power optimization solution for
virtualized server clusters
– Application-level performance guaranteed
– Power savings are provided by DVFS and server consolidation
• Compared with pMapper
– Provides performance guarantee
– Consumes less power
– Less computational overhead
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Critiques to pMapper
• Too many components are only disscussed without
implementation
• Lacks hardware experiments
• Only provides small scale simulations
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Critiques to IPAC
• Realword constraints are not shown in experiments
– Network constraint, etc.
• Centralized solution
– Incures a heavy conmunication overhead to the optimizer
• The settling time of the response time controller is too
long
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Comparisons of the Two Papers
pMapper
IPAC
Performance guarantee
No
Yes
DVFS
No
Yes
Hardware experiments
No
Yes
Based algorithms
FFD
Minimum Slack
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