PowerPerfCenter: A Power
and Performance Prediction
Tool For Multi-Tier
Applications
Varsha Apte, Bhavin Doshi
Department of Computer Science and Engineering
IIT Bombay, Mumbai, INDIA
CMG Meeting, April 4th, 2014, IIT Bombay
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The application performance
prediction problem
Application
Web
Database
Application
A multi-tier application is ready to be “rolled out”.
•How many users can it support?
•With some N simultaneous users, what will be the response
time?
•What sort of hardware and deployment architecture is
required to support a target N users?
2
2
Application performance predictionhow?
Carry out “load testing”
on a testbed
Clients running load generators
Generate
Requests,
measure
performance
Servers running
system under test
Extrapolate to “production”
environment, using
models. More specifically,
modeling tools.
3
3
PerfCenter – Multi-tier Application
Performance Modeling Tool
Takes as input, the server, host, network, deployment, request types, message flow
details, and resource usage details.
Request
Machine 1
Machine 2
Machine 3
Web Server
Web Server
WA
N
Auth Server
LAN
10ms
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PerfCenter Model
Specification Language
variable
nusr 1
end
…
server web
thread count 150
thread buffer 0
device
thread schedP fcfs
intel_xeon_cpu
task verify_credentials
raid_disk
task prepare_calendar_page
end
task verify_session
…
host server_host[2]
end
intel_xeon_cpu count 1
deploy web server_host[1]
intel_xeon_cpu buffer 99999 deploy db server_host[2]
intel_xeon_cpu schedP fcfs
raid_disk count 1
lan
raid_disk buffer 99999
lan1
raid_disk schedP fcfs
end
end
task verify_session
intel_xeon_cpu servt 0.010
end
deploy server_host[1] lan1
deploy server_host[2] lan1
scenario Login prob 0.05
user verify_credentials 100 SYNC
branch prob 0.05
verify_credentials display_error_message 100 SYNC
end
…..
end
loadparams
noofusers nusr
thinktime exp(2)
end
modelparams
method simulation
type closed
noofrequests 40000
end
for nusr = 5 to 100 incr 5
print nusr+","+respt()+","+tput()+","+
util(server_host[1]:intel_xeon_cpu)+
","+util(server_host[2]:intel_xeon_cpu)
nusr=nusr+5
end
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Power – an important resource
•Power Costs are now a significant part of
overall datacenter expenses
•Specifically, many devices in servers run
in self-tuned mode to save power
•Typically: low speed  low power
 Run at low speed when load is low
•But low power  low performance
1. Application Performance Modeling Tools
must predict performance in presence of
power-managed devices
2. Power Consumption should also be
modeled and predicted
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PowerPerfCenter
A multi-tier application performance
modeling tool which can represent “powermanaged” devices and predict power
consumption
 Extension of existing FOSS tool developed
at IIT Bombay “PerfCenter”*

(www.cse.iitb.ac.in/panda/tools/perfcenter)
*"PerfCenter: a performance modeling tool for application hosting centers" , Akhila Deshpande,
Varsha Apte and Supriya Marathe, Proceedings of the 7th international workshop on Software
and performance, June 2008.
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PowerPerfCenter

Can specify power-managed devices


Power
(watts)consumed at those
Abstraction: set of speed levels, and
power
speed levels
Current model based on linux governors of “frequency
max
scaled CPUs”:

two static
dynamic
powersave: fixed at lowest speed
 performance: fixed at highest speedidle
power
 two dynamic
 ondemand: speed jumps to highest if utilization exceeds
utilization 1.0
upthreshold
 conservative: speed jumps by one step when low/high thresholds
crossed

Power usage model within PerfCenter:
power used by device (speed, utilization) =
idlepower(speed) + utilization X max_dynamic(speed)
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PowerPerfCenter Specification
host arpa[1]
corei5 count 1
corei5 buffer 99999
corei5 schedP fcfs
corei5 power_managed
governor conservative
disk
disk
disk
disk
end
count 1
buffer 99999
schedP fcfs
speedup 1
powermanagement corei5
speed_levels 1.2 2.26 2.39
2.53 2.66 2.79 2.93 3.06 3.19
3.19 end
power_consumed 10.36 69.259
81.911 97.166 112.926 130.306
150.922 171.916 194.771
194.771 end
idlepower 65 65 65 65 65 65 65
65 65 65 end
probe_interval 0.08
governor_up_threshold 80
governor_down_threshold 20
end
power(arpa[1]:corei5) // average power consumed by
// corei5
eperr(arpa[1]:corei5)
respt(),
//energy per request
tput() // avg response time, and throughput
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PowerPerfCenter Validation:
Application

Example: “Webcalendar”
 Two

tier application
Request “scenarios”
 ViewEvent
 ViewWeek
 ViewMonth
…
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PowerPerfCenter Validation:
Testbed
Database server:
Client for Load
Generation
Load generator:
AutoPerf
Web server:
Intel Core i5 650 processor
•One active core
•Frequency scaled:
•1.2 GHz to 3.19 GHz in
steps of 133 MHz
•Idle power consumption:
65 w at all frequency levels
•Dynamic power
consumption:
Varies with frequency levels
Intel Core2 Duo E4500
2.2 GHz processor
Not frequency scaled
5000 users, 100
events each
AutoPerf generates usual
performance metrics as
well as per scenario, per
server, resource demands
e.g. CPU execution time
of ViewEvent request at
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Web server
PowerPerfCenter Validation:
Workload

Dynamic workload (cyclic) number of
simultaneous users changes over time
 Dynamic
workload can highlight the impact of using
power-managed devices


Each user logins, then navigates randomly
through a set of various requests
PowerPerfCenter specification
workload cyclic
noofusers 25 30 35 40 45 40 35 30 25 25 20 15 20 25 end
interval 300 300 300 300 300 300 300 300 300 300 300 300 300 300 end
thinktime exp(6)
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Experiments - 1: Validation of
Perfomance Metrics

WebCalendar performance



Measured on testbed
Estimated using PowerPerfCenter model
Metrics compared


Throughput, response time, and resource utilizations
Governors compared



POwersave (PO): CPU Frequency fixed at lowest
Conservative (C): Speed increases/decreases when
high/low utilization thresholds are crossed
Performance (PE): CPU Frequency fixed at highest
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Modeled
Measured
Response Time
Web CPU Utilization
Throughput
Number of Users
Model vs Measured – Conservative Governor
Time
Time
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Governors comparison
Performance of
Conservative (C)
governor very close
to Performance (PE)
governor
CPU with
PowerSave (PO)
governor does
not have enough
capacity
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Experiments-2: Power-performance
Tradeoff

Validation

Only by trend checking – currently not validated by
measurement



power consumed by PE > power consumed by C > power
consumed by PO
Response time of PE <Response time of C < Response
time of of PO
Demonstrate insights possible by using
PowerPerfCenter

Compare CPU with same idlepower consumption at
different frequency settings (corei5), with CPU with
different idle power consumption at different
frequency settings (core i7)
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Power-Performance Results (corei5)
Power &
performance trends
are as expected
However, power saving
by Conservative
governor is insignificant
Workload-2 (w2):
75 users for 60
seconds, and 3 users
for 600 seconds
Explanation: CPU
utilization is not high
enough– “idlepower”
consumption dominates
– which is the same at
all speeds – so power
management is not
very useful
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Power-Performance Results (corei7)
corei7: Idle power Consumption
different at different frequencies
powermanagement corei7
speed_levels 2.8 3.4 4.0
end
power_consumed 120 120
195 end
idlepower 51 66 90 end
40% power saving by conservative
governor, as compared with
performance governor, with much
smaller performance penalty than
PO
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Summary & Future Work

PowerPerfCenter:a powerful tool for
performance prediction in presence of powermanaged devices
 Performance
metrics validated
 Power modeling shown to lead to interesting insights
in the power-performance trade-off

This is just a start (work-in-progress) – lots of
future work
 further validation of performance metrics
 validation of power metrics
 generalized power consumption model
 analytical models
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Thank you/
Questions
Acknowledgements:
Yogesh Bagul and Rakesh Mallick for contribution to
PowerPerfCenter development
Swetha P. T. Srinivasan for providing us with power
usage data
Senthil Nathan and Swetha P. T. Srinivasan for
valuable feedback
This
work was supported by Tata Consultancy Services, Mumbai,
IBM , Intel IT and MHRD
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