1

Mor Harchol-BalterCarnegie Mellon UniversitySchool of Computer Science

2

“size” = service requirement

load < 1

jobs SRPT

jobs

jobs PS

FCFS

Q: Which minimizes mean response time?

3

“size” = service requirement

jobs SRPT

jobs

load < 1

jobs PS

FCFS

Q: Which best represents scheduling in web servers ?

4

IDEA

How about using SRPT instead of PS in web servers?

Linux 0.S.

WEBSERVER(Apache)

client 1

client 2

client 3

“Get File 1”

“Get File 2”

“Get File 3”

Internet

5

Many servers receive mostly static web requests.

“GET FILE”

For static web requests, know file size

Approx. know service requirement of request.

Immediate Objections

1) Can’t assume known job size

2) But the big jobs will starve ...

6

Outline of Talk

[Sigmetrics 01] “Analysis of SRPT: Investigating Unfairness”[Performance 02] “Asymptotic Convergence of Scheduling Policies…”[Sigmetrics 03*] “Classifying Scheduling Policies wrt Unfairness …”

THEORY

IMPLEMENT

www.cs.cmu.edu/~harchol/

[TOCS 03] “Size-based Scheduling to Improve Web Performance”[ITC 03*, TOIT 06] “Web servers under overload: How scheduling helps”[ICDE 04,05,06] “Priority Mechanisms for OLTP and Web Apps”

(M/G/1)

Schroeder

Wierman

IBM/CMU Patent

7

THEORY SRPT has a long history ...

1966 Schrage & Miller derive M/G/1/SRPT response time:

1968 Schrage proves optimality

1979 Pechinkin & Solovyev & Yashkov generalize

1990 Schassberger derives distribution on queue length

BUT WHAT DOES IT ALL MEAN?

8

THEORYSRPT has a long history (cont.)1990 - 97 7-year long study at Univ. of Aachen under Schreiber SRPT WINS BIG ON MEAN!

1998, 1999 Slowdown for SRPT under adversary: Rajmohan, Gehrke, Muthukrishnan, Rajaraman, Shaheen, Bender, Chakrabarti, etc. SRPT STARVES BIG JOBS!

Various o.s. books: Silberschatz, Stallings, Tannenbaum: Warn about starvation of big jobs ...

Kleinrock’s Conservation Law: “Preferential treatment given to one class of customers is afforded at the expense of other customers.”

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Unfairness Question

SRPT

PS?

?

Let =0.9. Let G: Bounded Pareto(= 1.1, max=1010)

Question: Which queue does biggest job prefer?

M/G/1

M/G/1

THEORY

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Results on UnfairnessLet =0.9. Let G: Bounded Pareto(= 1.1, max=1010)

SRPT

PS

I SRPT

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Unfairness – General Distribution

All-can-win-theorem:

For all distributions, if ½,

E[T(x)]SRPT E[T(x)]PS for all x.

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All-can-win-theorem:

For all distributions, if ½,

E[T(x)]SRPT E[T(x)]PS for all x.

Proof idea:

x

t

dt

0 )1 1x

)

x

xFx

2

2

))1(2

(

0x

dttft 2 )(

Waiting time (SRPT) Residence (SRPT) Total (PS)

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Classification of Scheduling Policies

AlwaysUnfair

Sometimes Unfair

AlwaysFair

SRPT

Age-BasedPolicies

Preemptive Size-basedPolicies

Remaining Size-basedPolicies

Non-preemptive

PS PLCFS

FB

PSJF

LRPT

FCFS

LJF

SJF

FSP[Sigmetrics 01, 03]

[Sigmetrics 04]• Henderson FSP (Cornell) (both FAIR & efficient)• Levy’s RAQFM (Tel Aviv) (size + temporal fairness)• Biersack’s, Bonald’s flow fairness (France)• Nunez, Borst TCP/DPS fairness (EURANDOM)

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What does SRPT mean within a Web server?

• Many devices: Where to do the scheduling?

• No longer one job at a time.

IMPLEMENT From theory to practice:

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Server’s Performance BottleneckIMPLEMENT

5

Linux 0.S.

WEBSERVER(Apache)

client 1

client 2

client 3

“Get File 1”

“Get File 2”

“Get File 3”

Rest ofInternet ISP

Site buyslimited fractionof ISP’s bandwidth

We model bottleneck by limiting bandwidth on server’s uplink.

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Network/O.S. insides of traditional Web server

Sockets take turnsdraining --- FAIR = PS.

WebServer

Socket 1

Socket 3

Socket 2Network Card

Client1

Client3

Client2BOTTLENECK

IMPLEMENT

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Network/O.S. insides of our improved Web server

Socket corresponding to filewith smallest remaining datagets to feed first.

WebServer

Socket 1

Socket 3

Socket 2Network Card

Client1

Client3

Client2

priorityqueues.

1st

2nd

3rd

S

M

L

BOTTLENECK

IMPLEMENT

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Experimental Setup

Implementation SRPT-based scheduling: 1) Modifications to Linux O.S.: 6 priority Levels 2) Modifications to Apache Web server 3) Priority algorithm design.

Linux 0.S.

1

2

3

APACHEWEB

SERVER

Linux

12

3

200

Linux

123

200

Linux

12

3

200

switch

WA

N E

MU

WA

N E

MU

WA

N E

MU

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Experimental Setup

APACHEWEB

SERVER

Linux 0.S.

123

Linux

123

200

Linux

123

200

Linux

123

200

switch

WA

N E

MU

WA

N E

MU

WA

N E

MU

Trace-based workload: Number requests made: 1,000,000Size of file requested: 41B -- 2 MBDistribution of file sizes requested has HT property.

Flash

Apache

WAN EMU

Geographically-dispersed clients

10Mbps uplink

100Mbps uplink

Surge

Trace-based

Open system

Partly-open

Load < 1

Transient overload

+ Other effects: initial RTO; user abort/reload; persistent connections, etc.

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Preliminary Comments

• Job throughput, byte throughput, and bandwidth utilization were same under SRPT and FAIR scheduling.

• Same set of requests complete.

• No additional CPU overhead under SRPT scheduling. Network was bottleneck in all experiments.

APACHEWEB

SERVER

Linux 0.S.

123

Linux

123

200

Linux

123

200

Linux

123

200

switch

WA

N E

MU

WA

N E

MU

WA

N E

MU

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Load

FAIR

SRPTMea

n R

esp

onse

Tim

e (s

ec)

Results: Mean Response Time (LAN)

.

.

.

.

.

.

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Percentile of Request Size

Mea

n R

esp

onse

tim

e (

s)

FAIR

SRPT

Load =0.8

Mean Response Time vs. Size Percentile (LAN)

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Transient Overload

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Transient Overload - Baseline

Mean response time

SRPTFAIR

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Transient overloadResponse time as function of job

size

small jobswin big!

big jobsaren’t hurt!

FAIR

SRPT

WHY?

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Baseline Case

WAN propagation delays

WAN loss

Persistent Connections

Initial RTO value

SYN Cookies

User Abort/Reload

Packet Length

Realistic Scenario

WAN loss + delay

RTT: 0 – 150 ms

Loss: 0 – 15%

Loss: 0 – 15%RTT: 0 – 150 ms,

0 – 10 requests/conn.

RTO = 0.5 sec – 3 sec

ON/OFF

Abort after 3 – 15 sec, with 2,4,6,8 retries.

Packet length = 536 – 1500 Bytes

RTT = 100 ms; Loss = 5%; 5 requests/conn.,RTO = 3 sec; pkt len = 1500B; User abortsAfter 7 sec and retries up to 3 times.

FACTORS

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Transient Overload - Realistic

Mean response time

FAIR SRPT

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More questions …

STATIC web

requests

Everything so far in talk …

DYNAMICweb

requests

Current work…(ICDE 04,05,06)

SchroederMcWherterSchroeder

Wierman

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Online Shopping

Internet

client 1

client 2

client 3

“buy”

“buy”

“buy”

Web Server(eg: Apache/Linux)

Database(eg: DB2, Oracle, PostgreSQL)

• Dynamic responses take much longer – 10sec• Database is bottleneck.

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Online Shopping

Internet

client 1

client 2

client 3

“$$$buy$$$”

“buy”

“buy”

Web Server(eg: Apache/Linux)

Database(eg: DB2, Oracle, PostgreSQL)

Goal: Prioritize requests

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Isn’t “prioritizing requests” problem already solved?

Internet

“$$$buy$$$”

“buy”

“buy”

Web Server(eg: Apache/Linux)

Database(eg: DB2, Oracle, PostgreSQL)

No. Prior work is simulation or RTDBMS.

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Which resource to prioritize?“$$$buy$$$”

“buy”

“buy”

Web Server(eg: Apache/Linux)

Internet

Internet

Database

Disks LocksCPU(s)

High-Priority client Low-Priority client

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Q: Which resource to prioritize?“$$$buy$$$”

“buy”

“buy”

Web Server(eg: Apache/Linux)

Internet

Internet

Database

Disks LocksCPU(s)

High-Priority client Low-Priority client

A: 2PL Lock Queues

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What is bottleneck resource?

• IBM DB2 -- Lock waiting time (yellow) is bottleneck.• Therefore, need to schedule lock queues to have impact.

Fix at 10 warehouses #clients = 10 x #warehouses

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Existing Lock scheduling policiesLock resource 1

HHLL L

HHLL L

Lock resource 2

NP Non-preemptive. Can’t kick out lock holder.

NPinherit NP + Inheritance.

Pabort Preemptively abort.But suffer rollback cost + wasted work.

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Results:

Think time

Non-preemptive policies

High

Low

Think time

ResponseTime (sec) Low

High

ResponseTime(sec)

Preemptive-abort policy

New idea: POW (Preempt-on-Wait)Preempt selectively: only preempt those waiting.

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Results:

Think time(sec)

ResponseTime (sec)

Pabort

NPinherit

Pabort

NPinherit POW:

Best of both

IBM/CMUpatent

38

External DBMS scheduling

DBMS(eg: DB2, Oracle)QoSInternet

“$$$buy$$$”

“buy”

“buy”

Web Server

Scheduling

HL LL

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Scheduling is a very cheap solution… No need to buy new hardware No need to buy more memory Small software modifications

…with a potentially very big win.

Conclusion

Thank you!