1 components of a scalable distributed relational information service dong lu june 14, 2005

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Components of a Scalable Distributed Relational Information Service

Dong Lu

June 14, 2005

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Outline• Bird’s Eye View

– What is RGIS? – Architecture– What components are studied in the thesis?

• Size-Based Scheduling With Inaccurate Info– Fairness and efficiency as function of correlation – Other applications: beyond RGIS

• DualPats: Characterizing and Predicting TCP Throughput on the Wide Area Network– Why TCP throughput prediction?– Flow size / TCP throughput correlation– Issues with simple benchmarking– DualPats algorithm and dynamic rate adjustment

• Thesis Contributions

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RGIS• Grid computing

– Providing dependable, reliable, consistent, pervasive and unlimited computing resources

• RGIS: Relational Grid Information Service– Represents globally distributed resources,

including the network– Relational Model allows complex compositional

queries– Relational Model is well studied; large user

population– RGIS servers distributed among multiple

organizations and sites

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Query and Update Example

• A query example– Find a set of 16 Linux machines on the same

LAN, each has memory over 1GB, they have a total memory of at least 32 GB, and each has a link capacity >100Mb

• An update example– Host A has added 1GB memory, and will be

available from 1:00 PM to 6:00 PM central time

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RGIS Architecture

Oracle 9i Back EndWindows,Linux,Parallel Server,etc

Oracle 9i Front Endtransactional inserts and updates

using stored procedures, queries using select statements

(uses database’s access control)

UpdateManager

Web Interface

Content Delivery Network Interface

For loose consistency

Query Managerand Rewriter

Users

Schema, type hierarchy, indices,PL/SQL stored procedures

for each object

Applications

RDBMS

site-to-site

Updates encrypted using asymmetric cryptography on network. Only those with appropriate keys have access

Authenticated Direct Interface

SOAP Interface

Scoping Rewrite

Canned Queries

Canned Approximate

Queries

Nondeterminism Rewrite

Time Bounding (And Iteration Of Query)

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RGIS Web Interface

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RGIS Architecture

Oracle 9i Back EndWindows,Linux,Parallel Server,etc

Oracle 9i Front Endtransactional inserts and updates

using stored procedures, queries using select statements

(uses database’s access control)

UpdateManager

Web Interface

Content Delivery Network Interface

For loose consistency

Query Managerand Rewriter

Users

Schema, type hierarchy, indices,PL/SQL stored procedures

for each object

Applications

RDBMS

site-to-site

Updates encrypted using asymmetric cryptography on network. Only those with appropriate keys have access

Authenticated Direct Interface

SOAP Interface

Scoping Rewrite

Canned Queries

Canned Approximate

Queries

Nondeterminism Rewrite

Time Bounding (And Iteration Of Query)

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Query Components

• GridG: the first synthetic grid generator – Topology [Sigmetrics Performance Evaluation

Review, Vol 30, No. 4, 2003]– Annotation [SC’03-1]

• Query rewriting techniques to trade off query time and the result set size – Nondeterministic query [SC’03-2]– Scoped and approximate queries [GRID’03]

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Update and CDN Components

• Size-Based Scheduling with inaccurate info to minimize mean update time– Fairness and efficiency as function of correlation

[MASCOTS’04-1]– P2P scheduling [LCR’04], one in submission– Web server scheduling, in submission– Other applications [MASCOTS’04-2]

• Characterizing and predicting TCP throughput on the WAN to determine update transfer time– [ICDCS’05]

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Update and CDN Components

• Modeling and taming parallel TCP on the WAN to transfer updates faster– [IPDPS’05]

• Fat-tree based end-system multicast to disseminate update scalably– [WCW’04], one in submission

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Scheduling Section Outline

• Review of Size-Based Scheduling

• Motivation

• Simulation Setup

• Simulation Results

• New Applications

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The scheduling problem

Scheduler

Database

Updates come from CDN

Which update to run next?

Scheduling: a general problemGoal: minimize the mean response time; be fair

10K 8K 6K 3K

Response time: the time from job arrival to its completion

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Review of Non-size-based scheduling

• FCFS, PS, etc.

• FCFS: First Come First Serve– Intuitive– Easiest to implement

• PS: Processor Sharing– Fair: all jobs accept equal resources – Also easy to implement

Problem: Unaware of job size information, which results in high mean response time

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Review of size-based scheduling

• SRPT, FSP, etc.

• Use the job size (processing time, service time) information for scheduling– Optimal in mean response time– Fair?– Easy to implement?

We use Job Size to refer to the Processing Time (Service Time) of the job

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Shortest Remaining Processing Time (SRPT)

• Always serve the job with minimum remaining processing time first, Preemptive scheduling

• Yields minimum mean response time [Schrage, Operations Research, 1968]

• Surprisingly, it is fair for heavy-tail job size distribution [Bansal and Harchol-Balter, Sigmetrics ‘01]

• Easy to implement?– With accurate a priori job size information, YES– Otherwise, NO

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Fair Sojourn Protocol (FSP)

• Combined SRPT with PS, preemptive scheduling

• Mean response time is close to that of SRPT; and more fair than SRPT and PS [Friedman, et al, Sigmetrics ‘03]

• Easy to implement? – With accurate a priori job size information, YES– Otherwise, NO

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• Motivation




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Motivation

• Size-based scheduling requires accurate knowledge of job sizes

• In practice, a priori job size information is not always available

• All the previous work assumes perfect knowledge of job sizes a priori

• How does performance depend on quality of job size information?

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Correlation

We study the performance of Size-based schedulers as a function of the correlation coefficient (Pearson’s R) between actual job sizes and estimated job sizes.

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• Motivation




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Trace generator

Trace Generator

Correlation (Pearson’s R)

Distribution A Distribution B

X Y1 1005 300. .. .. .

Correlated random pairs of X and Y• X has distribution A• Y has distribution B• X and Y are correlated to R

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Trace generator algorithm

• Algorithm: “Normal-To-Anything”– First developed by Cario and Nelson, on

INFORMS Journal on Computing 10, 1 (1998).

– We simplified the algorithm and first introduced it into the simulation studies of computer systems

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Scatter plot of example traces

R=0.13 R=0.78

Y

X

Y

X

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Performance metrics

• Mean response time: Sojourn time, Turn-around time

• Slowdown: the ratio of response time to its size. Fairness metric

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Simulator

• Simulator– Supports M/G/1 and G/G/n/m queuing model

• Simulator 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]

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Scheduling Policies

• PS: Processor sharing

• Size-based scheduling policies– SRPT: Ideal SRPT scheduler– SRPT-E: SRPT scheduler using estimated job

size

– FSP: Ideal Fair Sojourn Protocol– FSP-E: FSP scheduler using estimated job size

Each simulation is repeated 20 times and we present the average

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• Motivation




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Mean response time as function of R

0.1

1

10

100

1000

0 0.2 0.4 0.6 0.8 1

Correlation Coefficient R

Mea

n R

espo

nse

Tim

e

PSSRPTSRPT-EFSPFSP-E

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Slowdown (R=0.0224)

1

10

100

1000

10000

0 10 20 30 40 50 60 70 80 90 100

Job Size Percentile (R=0.0224)

Slo

wd

ow

n


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Slowdown (R=0.239)

1

10

100

1000

10000

0 10 20 30 40 50 60 70 80 90 100


Slo

wdo

wn


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Slowdown (R=0.4022)

1

10

100

1000

0 10 20 30 40 50 60 70 80 90 100


Slo

wd

ow

n


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Slowdown (R=0.5366)

1

10

100

1000

0 10 20 30 40 50 60 70 80 90 100


Slo

wdo

wn


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Slowdown (R=0.7322)

1

10

100

1000

0 10 20 30 40 50 60 70 80 90 100


Slo

wdo

wn


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Slowdown (R=0.9779)

1

10

100

1000

0 10 20 30 40 50 60 70 80 90 100


Slo

wdo

wn


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Simulation Results: Conclusions

• Performance heavily depends on correlation– SRPT-E and FSP-E can outperform PS given an

effective job size estimator

• Crossover point of performance metrics is a function of correlation– Also of job size distributions (See TR NWU-CS-04-

33)

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• Motivation




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New Applications: Web server scheduling (TR NWU-CS-04-33)

• Is file size a good estimator of a job’s service time (processing time)? Not Really (R 0.14)

Service time (wall clock time)

File

Size

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New Applications: Web server scheduling

• Domain-based estimator: much more accurate prediction of the service time at low overhead

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0 2 4 6 8 10 12 14 1618 20 2224 26 2830 32Bits used to define a domain

R (

co

rrela

tio

n c

off

icie

nt

betw

een

actu

al serv

ice

tim

e a

nd

esti

mate

d s

erv

ice t

ime)

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New Applications: P2P server side scheduling (LCR ’04)

• “Server side” of current file sharing P2P applications superficially similar to web server – Both send back files upon requests.

• However, P2P application can’t even know the file size accurately a priori– Partial downloads

• Our ongoing work shows that SRPT-E performs well using our time-series based job size estimators.

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Scheduling Section Summary

• Performance of size-based scheduling policies depends on correlation between size estimates and actual sizes– Fairness, mean response time, etc.

• Estimator must preserve ordering of job sizes for high performance– Performance degrades as correlation degrades

• Effective new estimators for Web and P2P

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DualPats Overview

• Algorithm for predicting the TCP throughput as function of flow size

• Minimal active probing• Dynamic probe rate adjustment

• Explaining flow size / throughput correlation

• Explaining why simple active probing fails

Large scale empirical study

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DualPats Section Outline

• Why TCP Throughput Prediction?

• Particulars of Study

• Flow Size / TCP Throughput Correlation

• Issues with Simple Benchmarking

• DualPats Algorithm

• Stability and Dynamic Rate Adjustment

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GoalA library call

BW = PredictTransfer(src,dst,numbytes);

Expected Time = numbytes/BW;

Ideally, we want a confidence interval:

(BWLow,BWHigh) = PredictTransfer(src,dst,numbytes,p);

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Available Bandwidth

• Maximum rate a path can offer a flow without slowing other flows– pathchar, cprobe, nettimer, delphi, IGI, pathchirp,

pathload … – mainly for traffic engineering

• Available bandwidth can differ significantly from TCP throughput

• Not real time, takes at least tens of seconds to run

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Simple TCP Benchmarking

• Benchmark paths with a single small probe– BW = ProbeSize/Time– Widely used Network Weather Service (NWS)

and others (Remos benchmarking collector)

• Not accurate for large transfers on the current high speed Internet– Numerous papers show this and attempt to fix it

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Fixing Simple TCP Benchmarking

• Logs [Sundharshan]: correlate real transfer measurements with benchmarking measurements

• Recent transfers needed• Similar size transfers needed• Measurements at application chosen times

• CDF-matching [Swany]: correlate CDF of real transfer measurements with CDF of benchmarking measurements

• Recent transfers still needed• Measurements at application chosen times

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Analysis of TCP

• Extensive research on TCP throughput modeling in networking community

• Really intended to build better TCPs

• Difficult to use models online because of hard to measure parameters

• Future loss rate and RTT

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51

Our Study

• PlanetLab and additional machines– Located all over the world

• Measurements of throughput– Wide open socket buffers (1-3 MB)– Simple client/server– scp– GridFTP

• Four separate sets of measurements

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Four sets of measurements

• Distribution set: for analysis of TCP throughput stability and distributions

• Correlation set: for studying correlation between throughput and flow size, initial testing of algorithm

• Verification Set: test our benchmarking mechanism

• Online Evaluation Set: test our online algorithm

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Distribution Set

• For analysis of TCP throughput stability and distributions

• 60 randomly chosen paths among PlanetLab machines

• 1.6 million transfers (client/server)– 100 KB, 200 KB, 400 KB, … 10 MB flows– 3000 consecutive transfers per path+flow size

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Correlation Set

• For studying correlation between throughput and flow size, initial testing of algorithm

• 60 randomly chosen paths among PlanetLab machines

• 2.4 million transfers, 270 thousand runs, client/server– 100 KB, 200 KB, 400 KB, … 10 MB flows

– Run = sweep flow size for path

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Verification Set

• Test algorithm

• 30 randomly chosen paths among PlanetLab machines and others

• 4800 transfers, 300 runs, scp and GridFTP– 5 KB to 1 GB flows– Run = sweep flow size for path

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Online Evaluation Set

• Test online algorithm

• 50 randomly chosen paths among PlanetLab machines and others

• 14000 transfers, scp and GridFTP– 40 MB or 160 MB file, randomly chosen– 10 days

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Strong Correlation Between Throughput and Flow Size

Correlation andVerification Sets

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An example of Strong Correlation

0

2000

4000

6000

8000

10000

12000

0 20000 40000 60000 80000 100000

File size (KB)

BW

(K

B/S

ec)

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Why Does The Correlation Exist?

• Slow start and user effects [Zhang]

• Non-negligible startup overheads– Control messages in scp and GridFTP

• Residual slow start effect– SACK results in slow convergence to

equilibrium

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62

y = 9E-05x + 0.7246

R2 = 0.9992

0

0.5

1

1.5

2

2.5

3

3.5

0 5000 10000 15000 20000 25000 30000 35000

File size (KB)

Tim

e (

se

c)

Why Simple Benchmarking FailsProbes are too small

Need more than one probe to capture correlation

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y = 9E-05x + 0.7246

R2 = 0.9992

0

0.5

1

1.5

2

2.5

3

3.5

0 5000 10000 15000 20000 25000 30000 35000

File size (KB)

Tim

e (

se

c)

Our ApproachTwo consecutive probes, both larger than the noise region

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Our Approach

• Two consecutive probes are integrated into a single probe– 400KB, 800 KB in single 800 KB probe

0 T1 T2

Probe one

Probe two

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Our Approach

BxAT

BxA

x

T

xTP

Flow sizeTransfer Time

Solve For A and B

Predict Throughput For Some Other Transfer

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Model Fit is Excellent

Correlation SetLow and Normally Distributed Relative ErrorsAt All Flow Sizes

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Stability

• How long does the TCP throughput function remain stable? – How frequently should we probe the path?

• What’s the distribution of throughput around the function (i.e., the error)?

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Throughput is Stable For Long Periods

Correlation Set

Increasing Max/Min Throughput in Interval

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Throughput For a Given Flow Size Is Normally Distributed In An Interval

Distribution Set

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Online DualPats Algorithm

• Fetch probe sequence for destination– Start probing process if no data exists

• Project probe sequence ahead– 20 point moving average over values with

current sampling interval

• Apply model using projected data

• Return result– confidence interval computed using normality

assumptions

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Dynamic Sampling Rate

• Adjust sampling interval to correspond to the path’s stable intervals

• Limit rate (20 to 1200 seconds)

• Additive increase / Additive decrease of interval based on difference between last two probes – < 5% => increase interval– > 15% => decrease interval

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Evaluation

0

1

0.4-0.4

Mean relative error

Mean abs(relative error)

Relative error

P[m

ean

erro

r <

X

]

• Slight conservative bias• About 90 % of predictions have < 20% error

Online Evaluation Set

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Section Summary

• Algorithm for predicting the TCP throughput as function of flow size

• Minimal active probing• Dynamic probe rate adjustment

• Explaining flow size / throughput correlation

• Explaining why simple active probing fails

Large scale empirical study

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Thesis Contributions• It is feasible to build a scalable distributed

Relational Grid Information Service

• RGIS architecture

• Query rewriting– Trade off query time with the size of result set

• GridG– First synthetic grid generator– Relationship between power-laws

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Thesis Contributions

• Size-based scheduling with imperfect info

• DualPats: monitoring and predicting TCP throughput

• TameParallelTCP: modeling and taming parallel TCP

• FatNemo: fat-tree based end-system multicast

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Future work

• Integration of research components with RGIS system

• Highly dynamic grid information: how to incorporate data from services such as RPS, NWS, DualPats, Remos

• Passive monitoring of TCP throughput• Understanding size-based scheduling in the

presence of backfilling

80

Acknowledgements

• Collaborators– P2P scheduling: Yi Qiao, Fabian Bustamante – FatNemo: Stefan Birrer, Fabian Bustamante

• RGIS implementation– Andrew Weinrich, Jack Lange, Andrew Simpson

84

Current State

• Prototyped RGIS system– Schema, stored SQL– Query manager/rewriter– Soap interface, web interface– Publish/subscribe based CDN

• To be done– Integration of update scheduling, TCP

throughput monitoring, end-system multicast based CDN

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Finding Sufficiently Large Probe Size

• Default values: 400 KB / 800 KB

• Upper bound

• Additive increase until prediction error are less than threshold, all with same sign.

1 components of a scalable distributed relational information service dong lu june 14, 2005

Documents

query time

rgis web interface slide

iteration of query slide

query example

rgis dualpats

query components gridg

rgis architecture oracle

rgis grid computing