Multicast Data Dissemination

Multicast Data DisseminationMulticast Data Dissemination

Wang LamSpecial University Oral Examination

7 July 2004

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ContentsContents

Current multicast networksContributions– Data scheduling– Network issues

Related and future workConclusion

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Current multicast networksCurrent multicast networks

Traditional data service: one-to-one

Multicast networks: one-to-many

IP: multicast group addresses (IPv4: 224.0.0.0 - 239.255.255.255; IPv6: FF00::/8)

Network bottlenecks

Client joins Unreliable delivery Datagrams (UDP)

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Multicast data disseminationMulticast data dissemination

Client joins

Unreliable delivery

Datagrams

Supports varying bandwidth clients

All requested data must arrive

Data arranged to optimize performance

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Principal contributionsPrincipal contributions

Data scheduling–Minimize delay for clients requesting many

items– Scheduling for subscribers and Scheduling for subscribers and

downloadersdownloaders 11Networking issues– Reliable deliveryReliable delivery

2– Splitting bandwidth into channels

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Data scheduling

– Scheduling for subscribers and Scheduling for subscribers and downloadersdownloaders

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Subscribers and downloadersSubscribers and downloaders

Data scheduling– Scheduling for subscribers and

downloaders• Distributing data for a Web repository• Metrics and techniques• Sample results

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The multicast sourceThe multicast source

Stanford WebBase

100+ million Web pages

Additional benefits of multicast

crawler

repository

multicastserver

indexingand

analysis

clients

WWW

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Multicast server

A multicast facilityA multicast facility

Clients issue requests to server

Clients listen to shared multicast

Server schedules data onto multicast

Downloaders and subscribers

clients

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Clients request multiple itemsClients request multiple items

Broadcast disks: one-item “response time”

Multicast: client delay is different

Subscribers: freshness and age

w x y z y w x z A • • A • • B • • B • • C • • C • • D • D • E • E •

F • F •

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Example scheduler: CircExample scheduler: Circ

Arbitrarily order data items

Send requested data

w x y z G • • • H • • I • J •

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Example scheduler: PopExample scheduler: Pop

Send most requested data

w x y z G • • • H • • I • J •

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Example scheduler: R/QExample scheduler: R/Q

Number of requesting clients

Smallest request size

w x y z G • • • H • • I • J •

clients 1 3 2 1 minreq 3 1 2 1

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Example scheduler: R/QExample scheduler: R/Q

Number of requesting clients

Smallest request size

w x y z G • • H • I J •

clients 1 0 2 1 minreq 2 0 1 1

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Some results for subscribersSome results for subscribers

Choice of scheduler depends on performance metric

Update frequency has little effect

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Downloaders and subscribersDownloaders and subscribers

Average download client delay

0

100

200

300

400

500

600

700

800

25 50 75 100 125 150 175 200

Number of downloaders

hou

rs

Circ

R/Q

RxC

Pop

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Average freshness over clients

0.830.840.850.860.870.880.89

0.90.910.920.930.94

25 50 75 100 125 150 175 200


Circ

R/Q

RxC

Pop

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0

100

200

300

400

500

600

700

800

25 50 75 100 125 150 175 200


hour

s

Circ

R/Q

RxC

Pop

Average freshness over clients

0.830.840.850.860.870.880.89

0.90.910.920.930.94

25 50 75 100 125 150 175 200


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SummarySummary

Differences from broadcast disksDownloaders and subscribersStudied design tradeoffs for various

metrics and techniques

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Data scheduling–Minimize delay for clients requesting many

items– Scheduling for subscribers and Scheduling for subscribers and

downloadersdownloadersNetworking issues– Reliable deliveryReliable delivery

2– Splitting bandwidth into channels

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Networking issues– Reliable deliveryReliable delivery

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Networking issues– Reliable deliveryReliable delivery• Multicast server model• Reliability techniques• Sample results• Other challenges

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The multicast sourceThe multicast source

Stanford WebBase

100+ million Web pages

Network loss <5% to >20%

crawler

repository

multicastserver

indexingand

analysis

clients

WWW

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multicast server

A multicast facilityA multicast facility

Clients issue requests to server

Clients listen to shared multicast

Server schedules data onto multicast

Data channel unreliable

clients

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Forward Error CorrectionForward Error Correction

Compute fixed fraction of redundant data

Reconstruct from subset of bits

Vary padding by item

data FEC data FEC

requests requests

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27





28





29





FEC(0.2R)

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RetransmissionRetransmission

Wait for NAK Queue

retransmission of enough bits

Queue only on selected NAKs requests requests

data data

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Wait for NAK Queue


Queue only on selected NAKs

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Wait for NAK Queue



NAK

NAK

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Wait for NAK Queue



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Wait for NAK Queue



35


Wait for NAK Queue



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Wait for NAK Queue



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Wait for NAK Queue



NAK

R(1)

NAK

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ReschedulingRescheduling

Do nothing Rerequest data

item

Combine with prior reliability schemes

requests requests

data data

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ReschedulingRescheduling

Do nothing Rerequest data

item

Combine with prior reliability schemes

NAK

NAK

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Clients of Uniform Loss RatesClients of Uniform Loss Rates


400

500

600

700

800

900

1000

0 2 4 6 8 10client loss (% packets)

dela

y (s

econ

ds)

FEC(0)+R(0)

FEC(0)+R(1)

FEC(0)+R(2)

FEC(0)+R(10)

FEC(0)+R(inf)

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Clients of Tiered Loss RatesClients of Tiered Loss Rates


0

1000

2000

3000

4000

5000

6000

7000

8000

9000

0 0.2 0.4 0.6 0.8 1fraction of clients having high loss

dela

y (s

econ

ds)

FEC(0.03R)+R(inf)

FEC(0.03R)+R(0)

FEC(0.1)+R(inf)

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Clients of Tiered Loss RatesClients of Tiered Loss Rates


0

1000

2000

3000

4000

5000

6000

7000

8000

9000

0 0.2 0.4 0.6 0.8 1fraction of clients having high loss

dela

y (s

econ

ds)

FEC(0.01R)+R(inf)FEC(0.03R)+R(inf)FEC(0.05R)+R(inf)FEC(0.01R)+R(0)FEC(0.03R)+R(0)FEC(0.05R)+R(0)FEC(0.1)+R(inf)

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Additional resultsAdditional results

Error-correcting packets help retransmissions

Variable FEC can outperform matched-rate FEC

Data-in-progress announcement can slightly help new clients

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SummarySummary

Multicast server scenario allows a variety of reliability techniques

Techniques form many combinationsStudied design tradeoffs

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Data scheduling–Minimize delay for clients requesting

many items– Scheduling for subscribers and Scheduling for subscribers and

downloadersdownloadersNetworking issues– Reliable deliveryReliable delivery– Splitting bandwidth into channels

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PublicationsPublications

W. Lam and H. Garcia-Molina, “Multicasting a Data Repository,” WebDB 2001

W. Lam and H. Garcia-Molina, “Multicasting a Changing Repository,” ICDE 2003

W. Lam and H. Garcia-Molina, “Reliably Networking a Multicast Repository,” SRDS 2003

W. Lam and H. Garcia-Molina, “Slicing Broadcast Disks,” submitted for publication

W. Lam and H. Garcia-Molina, “Implementing Multicast Data Dissemination,” technical report

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Publications (Stanford WebBase)Publications (Stanford WebBase)

J. Cho, T. Haveliwala, W. Lam, S. Raghavan, A. Paepcke, and H. Garcia-Molina, “Stanford WebBase Components and Applications”

http://www-diglib.stanford.edu/~testbed/doc2/WebBase/

Web crawler and client code:ftp://db.stanford.edu/pub/digital_library/

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Related workRelated work

Broadcast disksWeb cachingPublish/subscribe systemsVideo on demandReliable multicast protocolsLayered multicast protocols

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Next stepsNext steps

Other kinds of clients– On-the-fly processing– Partially ordered clients– Opportunistic clients

Distributed serversRequest mining

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http://www.cs.stanford.edu/~wlam/compsci/http://www.cs.stanford.edu/~wlam/compsci/

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More challengesMore challenges

Different network loss ratesDifferent download speeds

Multicast server

clients

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More challengesMore challenges

Different download speeds– How many, how fast, how distributed?

Multicast server

clients

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Data schedulingAcharya, Franklin, Aksoy, Zdonik,et al.

Web cachingBestavros, Rodriguez, et al.

Multicast networkingFloyd, Van Jacobson, McCanne, Miller, Almeroth, et al.

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Multicast dissemination– Acharya, Franklin, Zdonik, Vaidya,

Hameed (broadcast disks)– Almeroth, Ammar, Fei (Web service)

Reliable multicast networking– Floyd, Van Jacobson, McCanne (SRM)–Miller, Robertson, et al. (MFTP)– Yajnik, Kurose, Towsley, et al. (Mbone)

Documents

Multicast Data Dissemination