iPOEM: A GPS Tool for
Integrated Management in
Virtualized Data Centers
Hui Zhang1 , Kenji Yoshihira1, Ya-Yunn Su2 ,
Guofei Jiang1, Ming Chen3, Xiaorui Wang3
1. NEC Laboratories America
2. National Taiwan University
3. University of Tennessee
Virtualized data centers: server consolidation and green IT
•
Server consolidation - virtualization facilitates consolidation of several
physical servers onto a single high end system
— Reduces management costs/overheads
— Increases overall utilization
Resource Pool
•
Green IT - computing more, consume less
— Improving infrastructure efficiency
—Increasing IT productivity
Future
Today
IT load power
DCiE =
DCpW =
Total data center
Input power
Total facility
power
DCiE: Data center infrastructure efficiency
iPOEM
Data center
useful work
DCpW: Data center performance per Watt
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Virtualized data center management
• Server utilization based performance and
power management mechanisms
– VMware DPM, NEC SSC, IBM Tivoli…
Overload
threshold
CPU utilization
CPUhigh
CPUlow
Power-saving
mode
Management Configuration
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iPOEM: a middleware for
integrated power and performance management
•• A
GPS tool declarative
is a good metaphor.
Features
management methodology
• How can I get the
driver
1. accepts higher-level managementoperator
objectives
system to 20% less
• How
can
I
go
to
NYC?
•
target system status set on individual management components
power cost?
iPOEM
2. generates low-level management configurations.
system
•
Configuration settings of individual management components
car
1. Map
2. Direction
iPOEM
GPS
device
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1. System status
2. Management
decisions
4
Research Goal
Management
challenges
Data center administrator
Human-friendly management interfaces
iPOEM
System
Complexity
Workload
Dynamics
System
Dynamics
Performance
management
t
t
t
t
t
t
Power
management
t
t
t
t
migration
t
t
t
t
t
t
t
failure
t
Application
management
remove/add
Virtualized data center
Thermal
management
iPOEM APIs
API 1 : get_position()
API 2 : put_position()
Input: Management Configuration
Input: Target Performance & Power
(Time start, Time end)
(Time start, Time end)
Workload (reshaping-scheme)
Workload (reshaping-scheme)
VM-server map, resource inventory
VM-server map, resource inventory
Output: Management Configuration
Output: System Status
System status is described in 3 metrics
Load
threshold
CPU utilization
time
Performance cost:
March
server2010
overloading
time in percentage.
iPOEM
Power cost:
KWatts, total
power consumed
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Operation cost:
VM migrations
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iPOEM architecture
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iPOEM management configuration engine
API 1 : get_position()
Management Configuration
CPU High
CPU High, Low
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System status as a function of
management configurations
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Formal description of system status functions
Assume a homogeneous system, and the workload
remains the same for different configuration settings.
Theorem 1. Performance-cost(CPUhigh) is a non-decreasing
function of CPUhigh.
Theorem 2. Power-cost(CPUlow) is a non-increasing function of
CPUlow.
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iPOEM configuration searching algorithm
• A O(logR) searching algorithm
owhere R =CPUmax-CPUmin, the allowable load range
Binary search
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iPOEM prototype implementation
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iPOEM System positioning services
• Position reporting
iPOEM
• Destination searching
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iPOEM
1
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1
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27
40
53
66
79
92
105
118
131
144
157
170
183
196
209
222
235
248
261
274
287
300
313
326
339
352
365
378
391
404
417
430
443
456
469
482
495
508
521
534
547
560
573
586
599
612
625
638
651
664
1
14
27
40
53
66
79
92
105
118
131
144
157
170
183
196
209
222
235
248
261
274
287
300
313
326
339
352
365
378
391
404
417
430
443
456
469
482
495
508
521
534
547
560
573
586
599
612
625
638
651
664
Evaluation: data center workload traces
•
Traces on 2525 servers from 10 IT
systems
– Each is regarded as a VM in the
simulations.
•
•
Monitoring data: CPU utilization.
1 week length, 15 minute monitoring
frequency
– 672 time points
100
50
0
150
100
50
0
100
50
0
150
100
50
0
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Evaluation: methodology
•
Run the iPOEM prototype as an offline engine.
–
•
It is driven by the data traces stored in the monitoring database, and emulates the integrated
management in a virtualized data center hosting the 2, 525 servers as VMs.
The system and management configuration settings
– Performance manager and power manager
•
Implementation of the simplified schemes in NEC SigmaSystemCenter middleware
– The default <CPUlow,CPUhigh> setting is <40%, 80%>.
– The physical servers are homogeneous with the same CPU specs
•
3GHZ Quadra-core (the most common CPU model in the traces).
– Performance cost: number of performance violation in a time epoch
•
A server has a performance violation at a time point when its CPU utilization is larger than a threshold
(90% in the paper).
– Power cost: we assume power consumption per server is either 0 (power-off
mode) or 200Watts (power-on mode), simplified on the power model profiled in
the local testbed.
– Operation cost: the number of VM migrations that the performance and power
managers need to execute for the server load configuration enforcement.
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iPOEM engine performance
iPOEM engine response time to service requests
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iPOEM auto-piloting service
• Sensitivity based optimization [Markovic et al. 2004]
where Pth is the upper bound of the performance cost.
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iPOEM auto-piloting evaluation
Comparison of Auto-piloting and three static configuration schemes
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iPOEM
Auto-piloting management
configuration evolution
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Conclusions & Future Work
• iPOEM, an integrated power and performance
management middleware in an virtualized
infrastructure.
– human-friendly interfaces for multi-objective
management.
• Future work
– Meta-management integrating more objectives.
• explosive growth of the system state space
– mashup services for customized tenant management
• new API designs
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Thank you.
• Questions?
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Appendix
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