effects and implications of file size/service time correlation on web server scheduling policies
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Effects and Implications of File Size/Service Time Correlation on Web
Server Scheduling Policies
Dong Lu*+
Peter Dinda*Yi Qiao*
Huanyuan Sheng*
*Northwestern University+Ask Jeeves, Inc.
2
Outline
• Quick review of size-based scheduling
• Motivation and approach
• Correlation between file size and service time: a measurement study
• Performance of SRPT scheduling under real workload
• Domain-based scheduling
3
Quick Review of Size-based Scheduling
• SRPT– Shortest Remaining Processing Time– Assuming perfect knowledge of service times
• FSP – Fair Sojourn Protocol– Assuming perfect knowledge of service times
• Typical non-size-based scheduling– Processor Sharing (PS)– First Come First Serve (FCFS)
4
SRPT
• Always serve the job with minimum remaining processing time first, preemptive scheduling– Performance: Minimum mean response time
[Schrage, Operations Research, 1968]
– Fairness: performance gains of SRPT over PS do not usually come at the expense of large jobs, in other words, it is fair for heavy-tail job size distribution [Bansal and Harchol-Balter, Sigmetrics ‘01]
5
FSP
• Combined SRPT with PS, preemptive scheduling. [Friedman, et al, Sigmetrics ‘03]
– SRPT + the longer a job stay in the queue, the higher its priority
– Performance: Mean response time is close to that of SRPT
– Fairness: Fairer than PS
6
Outline
• Quick review of size-based scheduling
• Motivation and approach
• Correlation between file size and service time: a measurement study
• Performance of SRPT scheduling under real workload
• Domain-based scheduling
7
Motivation• Current implementation of SRPT and FSP
– Use file size as service time (sorting jobs using file size)
• Is file size a good estimator of service time?• What is the performance of SRPT and FSP using
file size as service time? And how to improve?
Service time: the time needed to send requested data in the absence of other requests in the system
8
Trace-driven Simulation
• Simulator:– C++– Supports G/G/n/m queuing model– Driven by enhanced web server traces – Validation
• Little’s law• Repeat the simulations in the FSP paper [Friedman, et al,
Sigmetrics ‘03]
• Compare with available theoretical results [Bansal and Harchol-Balter, Sigmetrics ‘01]
9
Scheduling Policies Studied
• SRPT: Ideal SRPT • SRPT-FS: File size as service time• SRPT-D: Domain-estimated service time
• FSP: Ideal FSP• FSP-FS: File size as service time• FSP-D: Domain-estimated service time
• PS: Processor sharing
10
Outline
• Quick review of size-based scheduling
• Our approach and questions answered
• Correlation between file size and service time: a measurement study
• Performance of SRPT-FS and FSP-FS scheduling under real workload
• Domain-based scheduling
11
Correlation is Weak on a Typical Web Server
• Measurement on departmental web server: Scatter plot of file size versus service time (log-log scale)
R ≈ 0.14
Service time
File
S
ize
Request from the whole Internet
12
Correlation is Weak on Web Cache Servers
• Measurement on 10 Squid web cache servers: – www.ircache.net
Correlation Coefficient R Between File size and Service time
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7
P[R
>x]
0
0.5
1.0
13
Main reason for the weak correlation
• End-to-end path diversity
Web Server
Client 1
Client 2
Client 3
Client 4
14
Outline
• Quick review of size-based scheduling
• Our approach and questions answered
• Correlation between file size and service time: a measurement study
• Performance of SRPT-FS and FSP-FS scheduling under real workload
• Domain-based scheduling
15
Mean Response Time Much Worse Than Expected
Simulation driven by web server trace. G/G/1/m. Pareto arrivals (rate controlled to tune the load).
Load on the queue
0 0.5 1.0 1.5 2.0
Mea
n R
espo
nse
Tim
e (m
illis
ec)
100
300
500
700
900
PS
SRPT-FS
FSP-FS
Ideal SRPT and FSP
16
Mean Queue Length Much Worse Than Expected
Simulation driven by web server trace. G/G/1/m. Pareto arrivals (rate controlled to tune the load).
Load on the queue
Mea
n Q
ueue
Le
ngth
0 0.5 1.0 1.5 2.0
1000
2000
3000
4000
5000
FSP-FS
SRPT-FSPS
Ideal SRPT and FSP
17
Outline
• Quick review of size-based scheduling
• Our approach and questions answered
• Correlation between file size and service time: a measurement study
• Performance of SRPT-FS and FSP-FS scheduling under real workload
• Domain-based scheduling
18
Requirements For A Better Service Time Estimator
• Low overhead– Passive measurement – Low computation complexity– Low / adjustable memory usage
• Effective– Approximate the correct ordering of the service
times. High correlation.
19
Domain-based estimator• Divide Internet into smaller “domains” by leveraging
CIDR (Classless Inter-domain Routing)• Hosts in the same domain are likely to share
same/similar routes to web server, and thus similar throughput
Web Server
20
Supporting Facts
• Statistical Internet stability and locality– Routing stability [Paxson, Sigcomm 1996]
– TCP throughput locality and stability [Balakrishnan, et
al, Sigmetrics 1997]; [Seshan, et al, USITS 1997]; [Myers, et al , Infocom 1999]
• Classless Inter-domain Routing– implies that routes from machines in the domain
to a server outside the domain will share many hops.
21
Algorithm
• Use high order k bits of client IP address to classify clients into 2k domains
• For each domain, calculate R = F/S– R: representative service rate– F: sum of file sizes delivered to domain– S: sum of corresponding service times
• For each request, first extract its domain, then service time can be estimated as B/R– B: requested file size– R: representative service rate obtained before
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Higher Correlation Can Be Achieved
0 8 16 24 32
Cor
rela
tion
Coe
ffic
ient
R0.1
0.3
0.5
0.7
Bits used to define a domain
23
Much Lower Service TimesCan Be Achieved
Bits used to define a domain
0 8 16 24 32
Mea
n R
espo
nse
time
(mili
sec)
100
300
500
700
900
PS
FSP-D
SRPT-FS
FSP-FS
SRPT-D
SRPT and FSP
24
Much Lower Queue LengthsCan Be Achieved
Bits used to define a domain
0 5 10 25 3515 20 30
Mea
n qu
eue
leng
th
1000
2000
3000 FSP-D
FSP-FS
SRPT-FS
PS
SRPT-D
SRPT and FSP
25
Conclusions
• File size may not be a good estimator of service time for many regimes
• File size-based SRPT and FSP can perform worse than PS in these regimes
• Domain-based scheduling brings the benefits of size-based scheduling to these regimes
26
For more information
• Prescience Lab at Northwestern University– www.presciencelab.org
27
Jeeves’ Invitation …
• Have you ever seen the whole Web at once? • Did you ever wonder how to rein the power of
thousands of machines?
• We are hiring talents for Internet Search– Software Engineer– Development Manager
Send us your Resume: talentacquisition@askjeeves.com
28
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Correlation is Weak on a Typical Web Server
• Measurement on departmental web server: Scatter plot of file size versus service time (log-log scale)
R ≈ 0.14
Service time
File
S
ize
Service time
File
S
ize
R ≈ 0.25
Request from the whole Internet Request from a “/16” IP network
30
Future Work
• The “back-filling” queuing model
Web Server
Bandwidth
Time
Bottleneck
Web Requests1
2
3
4
5
6
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