Transcript Power Aware Virtual Machine Placement

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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Design Choice (1) : Workload Redirection
Web requests
Example: [Heo’07]
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Design Choice (2): VM Live Migration
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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
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mPP Algorithm
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mPP Algorithm
Sort VMs by size
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mPP Algorithm
Sort servers by slope (power efficiency)
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mPP Algorithm
Allocate the VM to servers using First Fit
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mPP Algorithm
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•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
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Target
Util.
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Target
Util.
Target
Util.
Receiver
Receiver
Donor
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mPPH Algorithm : mPP with History
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Target
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Donor
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mPPH Algorithm : mPP with History
Pick the smallest VMs that add to a migration list
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Target
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Migration list
Donor
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mPPH Algorithm : mPP with History
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Receiver
Receiver
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Donor
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Migration list
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mPPH Algorithm : mPP with History
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Receiver
Receiver
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Donor
Donor
Donor
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Migration list
Target
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Target
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mPPH Algorithm : mPP with History
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Receiver
Receiver
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Donor
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Migration list
Target
Donor
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Target
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mPPH Algorithm : mPP with History
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Receiver
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Target
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Donor
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•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
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VM migration and server sleep/active commands
Power
Optimizer
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Response Time Controller
Application
HTTP requests
VM1
VM2
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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
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Minimum slack problem for a single server
Slack=0.8
Minimum Slack=0.8
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Minimum slack problem for a single server
Slack=0.2
Minimum Slack=0.2
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Minimum slack problem for a single server
Slack=0.2
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Minimum slack problem for a single server
Slack=0.5
Minimum Slack=0.2
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Minimum slack problem for a single server
Slack=0.2
Minimum Slack=0.2
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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
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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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