injecting realistic burstiness to a traditional client-server benchmark
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© 2006 Hewlett-Packard Development Company, L.P.The information contained herein is subject to change without notice
Injecting Realistic Burstiness to a Traditional Client-Server Benchmark
Ningfang Mi College of William and Mary
Giuliano Casale SAP Research
Ludmila Cherkasova Hewlett-Packard Labs
Evgenia Smirni College of William and Mary
Presenter: Lucy Cherkasova
2 International Conference on Autonomic Computing and Communications (ICAC) 2009
Origin of Burstiness
• Enterprise and Internet applications:
Clients DB Server
Front Server
Web + Application
Server
HTTP request
HTTP reply
SQL query
SQL reply
Burstiness
??
Highly Correlated Arrivals
?
3 International Conference on Autonomic Computing and Communications (ICAC) 2009
Client-Server Benchmark
• E.g., TPC-W (On-line bookstore Web site)
• Exponentially distributed user think timesExponentially distributed user think times
Clients DB Server
Front Server
Web + Application
Server
HTTP request
HTTP reply
SQL query
SQL reply
Burstiness
??
Highly Correlated Arrivals
?
4 International Conference on Autonomic Computing and Communications (ICAC) 2009
• Accounts for randomness and variability … • … but not for burstinessbut not for burstiness
Can we ignore burstiness in the arrival process?
Typical Client-Server Benchmark
BurstinessBurstinessVariabilityVariability
Serv
ice t
ime
Serv
ice t
ime
Request number Request number
5 International Conference on Autonomic Computing and Communications (ICAC) 2009
Why Need to Inject Burstiness?
• Burstiness impacts the performance of resource allocation mechanisms.
• Example: Session-based admission control (SBAC)−User session: sequence of transaction requests−Session is a unit of work−Typically, long sessions are “sales”.−Useful system throughput is the number of
completed sessions−Admission controller admits/rejects sessions
based on observed CPU utilization of the server (a combination of last measurement and some history).
L. Cherkasova, P. Phaal. Session Based Admission Control: a Mechanism for
Peak Load Management of Commercial Web Sites. IEEE J. TOC, June 2002.
6 International Conference on Autonomic Computing and Communications (ICAC) 2009
SBAC
Reject a new session when utilization is above the threshold
Abort an accepted session when the server queue is full
highly undesirable
Front ServerWeb +
ApplicationServer
DB Server
New Client Arrival
Requests from already accepted clients
limited server queue
7 International Conference on Autonomic Computing and Communications (ICAC) 2009
Impact of Burstiness
• We performed experiments for the same workload with different arrival patterns: non-bursty vs bursty
• Aborted ratio = aborted sessions/accepted sessions
highly undesirable
Queue Size Non-bursty Bursty
250 0.04% 11.37%
512 0.00% 6.28%
800 0.00% 2.50%
8 International Conference on Autonomic Computing and Communications (ICAC) 2009
Why Need to Inject Burstiness? (2)• Service level agreement (SLA)
−support given response time guarantees for accepted sessions
• SLA of 1.2s can be supported for 98% of requests with queue size =250 for non-bursty traffic
• Only 90% of requests meet SLA=1.2s bursty traffic.
0
0.2
0.4
0.6
0.8
1
1.2
250 512 800
90th
95th
98th
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
250 512 800
90th
95th
98th
Queue Size Queue Size
Resp
onse
Tim
e (
s)
Resp
onse
Tim
e (
s)
Non-Bursty Bursty
9 International Conference on Autonomic Computing and Communications (ICAC) 2009
Limitations of Standard TPC-W
• Think times are drawn randomly from the exponential distribution identical for all clients
• Exponential think times are incompatibleincompatible with the notion of burstiness.
Need to inject burstiness into user think times.
10 International Conference on Autonomic Computing and Communications (ICAC) 2009
Our Methodology
•Basic Idea: modify the distribution of client think time to create bursty arrivals−Regulate the arrivals by using a 2-phase
Markovian Arrival ProcessMarkovian Arrival Process (MAP).• MAPs are variations of popular On/OFF traffic
models that can be easily shaped to create correlated inter-arrival times
• All clients share a MAP(2) to draw think times
• A new module for client-server benchmarks
−Regulate the intensity of traffic surges by using the index of dispersionindex of dispersion. • A simple tunable knob of burstiness
11 International Conference on Autonomic Computing and Communications (ICAC) 2009
Index of Dispersion (I)• Popular burstiness index in networking• Definition
− SCV – the squared coefficient of variation (variance/mean2)− ρk – autocorrelation coefficients
• i.e., correlation of service times− Exponential: I = SCV = 1
)21(1
k
kSCVI variabilityburstines
s
BurstinessBurstinessVariabilityVariability
Serv
ice
tim
e
Serv
ice
tim
e
Request number Request number
12 International Conference on Autonomic Computing and Communications (ICAC) 2009
Markovian Arrival Process (MAP)
• MAPs have ability to provide variabilityvariability and temporal localitytemporal locality.
• We use a class of MAPs with two states only
Normal
Traffic
λlong
Traffic Surge
λshort
2 states: λshort > λlong
pl,s
ps,l
ps,spl,l
time
Num
. of
arr
ivals
pl,s, ps,l, ps,s, pl,s shape correlation
13 International Conference on Autonomic Computing and Communications (ICAC) 2009
MAP Fitting
• Input − Estimated mean service demands at servers: E[Di]
− Mean user think time E[Z]
− The pre-defined index of dispersion I
• Output− A MAP(2) to draw user think times
14 International Conference on Autonomic Computing and Communications (ICAC) 2009
MAP Fitting (2)
Key: determine (Key: determine (λλshortshort,, λλlonglong, , ppl,sl,s,, p ps,ls,l))• Condition for traffic surge
• Condition for normal traffic
• Mean think time
• We use non-linear optimizer to search for such f and ps,l and find a MAP(2) to best match the predefined I
fDEi ishort /)(1
])[),(max(1 ZEDENfi ilong
)][
][(
1
1
,,
short
longlssl ZE
ZEpp
Departure > Arrival
Arrival > Departure the arrival rate is f times higher than the throughput of the system
the arrival rate is f times slower for balanced system throughput
Balancing the height and the width of the burst
15 International Conference on Autonomic Computing and Communications (ICAC) 2009
Realistic values for Burstiness
−What is the range of realistic values for defining burstiness via index of dispersion I ? • Exponential: I = SCV = 1
• Bursty: values of thousands,
−e.g., FIFA World Cup 1998, one of the servers over 10 days, I = 6300
16 International Conference on Autonomic Computing and Communications (ICAC) 2009
TPC-W Testbed
• On-line bookstore Web site • Testbed: clients + front server + DB server
−Constant number of emulated browsers (EBs)
• User session−sequence of transaction requests
−think time (mean=7 sec) between two transaction requests
• 14 transactions types grouped in three mixes:−Browsing mix
−Shopping mix
−Ordering mix
17 International Conference on Autonomic Computing and Communications (ICAC) 2009
Validation – Arrival Process
• Arrival clients to the system (front server)
Think times drawn by a MAP(2) with I create the bursty conditions.
Shopping Mix
Non-bursty (I=1)
Time (s)
Num
ber
of
act
ive c
lients Bursty (I=4000)
Time (s)
Num
ber
of
act
ive c
lients
18 International Conference on Autonomic Computing and Communications (ICAC) 2009
Validation – Utilization DistributionShopping Mix
Non-bursty (I=1) Bursty (I=4000)
pd
fpd
f
pd
fpd
f
Utilization (%)Utilization (%)
Utilization (%) Utilization (%)
Front
DB
19 International Conference on Autonomic Computing and Communications (ICAC) 2009
Validation - Average Latency
0
500
1000
1500
2000
2500
3000
3500
200 400 600 800 1000 1200Number of EBs
non- bursty
I=4000
Browsing Mix
Resp
onse
tim
e (
ms)
0
200
400
600
800
1000
1200
1400
1600
200 400 600 800 1000 1200Number of EBs
non- bursty
I=4000
Shopping Mix
Resp
onse
tim
e (
ms)
20 International Conference on Autonomic Computing and Communications (ICAC) 2009
Validation – Latency Distributions
0%
20%
40%
60%
80%
100%
0 2000 4000 6000 8000 10000
non-bursty
I=4000
0%
20%
40%
60%
80%
100%
0 1000 2000 3000 4000 5000
non-bursty
I=4000
Browsing Mix
CD
F
Shopping Mix
Response time (ms) Response time (ms)
CD
F
0.83
2.98
0.04
1.25
21 International Conference on Autonomic Computing and Communications (ICAC) 2009
Conclusion• Burstiness critical for autonomic system design
− need representative benchmarks for system evaluation− need reproducible and controllable bursty workloads
• Traditional client-server benchmarks ignore burstiness in arrival flows− e.g., TPC-W with exponential think times
• Explicitly inject burstiness − a simple and tunable parameter: index of dispersion− can introduce different intensity of traffic surges
• http://www.cs.wm.edu/~ningfang/tpcw_codes/
• Supported by NSF grants CNS-0720699 and CCF-08114171 and HPLabs gift.
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