Supporting Disconnected Operations in Publish/Subscribe Systems

Vinod Muthusamy

Joint work withMilenko Petrovic, Ioana Burcea, H.-Arno Jacobsen, Eyal de Lara

University of Toronto

2004 IEEE International Conference on Mobile Data Management

Agenda• Part I – Publish/Subscribe

– Centralized/distributed models– Benefits– Applications

• Part II – Mobile Publish/Subscribe– Disconnected operation in distributed publish/subscribe

systems• Motivation and Problem• Solutions

– Evaluation• Factors• Results

• Conclusions

The Publish/Subscribe Model

Broker

Publisher Publisher

Subscriber Subscriber

Subscriptions

Publications

NotificationNotification

IBM=84

MSFT=27 INTC=19 JNJ=58ORCL=12

HON=24

AMGN=58

Stock marketsNYSE

NASDAQTSX

Subscriptions:IBM > 85ORCL < 10JNJ > 60

Distributed Publish/Subscribe

• Hierarchy of brokers [Siena, JEDI]

• Subscriptions to root

• Publications to root and multicast down to interested subscribers

Broker

Broker

stocks/ibm/*

stocks/*

Publisher

Broker

Broker

stocks/oracle

Broker Broker

Publish/Subscribe Benefits• Simple interface

• Decoupling of producers and consumers of data– Address

• Content-based routing• Anonymity

– Platform– Space– Time– Representation (semantic)

• Efficient data dissemination (scalability)– Push model– Multicast

Publish/Subscribe Applications

• News dissemination• Location based services• Workflow management• E-commerce• Auctions• Distributed gaming• Software updates delivery• Sensor networks

• Existing systems– Research: ToPSS, Siena, JEDI, Gryphon, Hermes– Industry: IBM, Precache, TIBCO, Talarian

Part II

• How does disconnected operation work in distributed publish/subscribe systems?

• Why is disconnected operation a problem?

• What are some solutions?

• How do these solutions perform?

Disconnected Operation: Motivation

• User’s laptop connected at work• Disconnect at the end of the day• Reconnect at home

• Events published while a subscriber is disconnected should be stored somewhere and replayed upon reconnection

Broker Broker

Broker

Subscriber

Disconnected Operation [JEDI]

• Subscriber receives publications• Subscriber disconnects • Broker stores publications• Subscriber connects• Transfer state to new broker &

replay old publications• New publications go directly to

subscriber Broker Broker

Broker

Publisher

Subscriber

t1

At Old Broker

t2

Disconnected At New Broker

t4t3

Receivenew events

Connect(movein)

Disconnect(moveout)

Disconnected Operation [JEDI]

• Subscriber receives publications• Subscriber disconnects • Broker stores publications• Subscriber connects• Transfer state to new broker &

replay old publications• New publications go directly to

subscriber Broker Broker

Broker

Publisher

Subscriber

t1

At Old Broker

t2

Disconnected At New Broker

t4t3

Receivenew events

Connect(movein)

Disconnect(moveout)

Disconnected Operation [JEDI]

• Subscriber receives publications• Subscriber disconnects • Broker stores publications• Subscriber connects• Transfer state to new broker &

replay old publications• New publications go directly to

subscriber Broker Broker

Broker

Publisher

Subscriber

moveout

t1

At Old Broker

t2

Disconnected At New Broker

t4t3

Receivenew events

Connect(movein)

Disconnect(moveout)

Disconnected Operation [JEDI]

• Subscriber receives publications• Subscriber disconnects • Broker stores publications• Subscriber connects• Transfer state to new broker &

replay old publications• New publications go directly to

subscriber Broker Broker

Broker

Publisher

Subscriber

t1

At Old Broker

t2

Disconnected At New Broker

t4t3

Receivenew events

Connect(movein)

Disconnect(moveout)

Disconnected Operation [JEDI]

• Subscriber receives publications• Subscriber disconnects • Broker stores publications• Subscriber connects• Transfer state to new broker &

replay old publications• New publications go directly to

subscriber Broker Broker

Broker

Publisher

movein

t1

At Old Broker

t2

Disconnected At New Broker

t4t3

Receivenew events

Connect(movein)

Disconnect(moveout)

Disconnected Operation [JEDI]

• Subscriber receives publications• Subscriber disconnects • Broker stores publications• Subscriber connects• Transfer state to new broker &

replay old publications• New publications go directly to

subscriber Broker Broker

Broker

Publisher

t1

At Old Broker

t2

Disconnected At New Broker

t4t3

Receivenew events

Connect(movein)

Disconnect(moveout)

Disconnected Operation [JEDI]

• Subscriber receives publications• Subscriber disconnects • Broker stores publications• Subscriber connects• Transfer state to new broker &

replay old publications• New publications go directly to

subscriber Broker Broker

Broker

Publisher

t1

At Old Broker

t2

Disconnected At New Broker

t4t3

Receivenew events

Connect(movein)

Disconnect(moveout)

Problem: Unicast State Transfer

Broker

Publish/subscribe is efficient [multicast]

State transfer is inefficient [unicast]

What’s the bandwidth overhead of unicast state transfer?

Broker Broker

BrokerBroker

Broker

State Transfer Optimizations:Prefetching

• Eagerly migrate state while the user is disconnected

• Exploits knowledge of future mobility patterns

• Shortens perceived length of disconnection to the system

• Less state to transfer• State transfer period hidden from user Broker Broker

Broker

moveout

t1

At Old Broker

t2

Disconnected At New Broker

t4t3

Receivenew events

Connect(movein)

tp

Disconnect(moveout)

State Transfer Optimizations:Logging

• Brokers cache recent publications

• Exploits locality

• Only partial state transfer needed if locality exists

• Overhead could be worse if no locality exists

• Requires cache space at brokers

Broker Broker

Broker

State Transfer Optimizations:Home-Broker

• Mobile client logically reconnects to “home” broker

• Eliminates state transfer overhead

• Increases perceived disconnection period– Unicast transfer even when

connected movein

Broker Broker

Broker

Evaluation:Factors Affecting Mobility

Network

Bandwidth and latency of links

Broker placement

Broker topology Number of

brokers

Mobility

Connection and disconnection periods

Predictive or repetitive mobility

Group mobility

Application

No. of publishers and subscribers

Publication rate Subscription

specificity Subscription

(interest) locality Message size

Evaluation: Setup• Simulation Environment

– NS2 network simulator– Implemented state transfer optimizations

• Parameters– Topology

• Metropolitan Area Network• 4 levels of degree 4 64 leaf brokers

– Subscribers: 200, 400, 600, 800– Publishers: 100– Locality: random, 30%, 60%, 90%

• Metric– Total message traffic– State transfer overhead (unicast/multicast)

• • •

64• • •

1

Commute Scenario• Simulate evening commute home

– Subscribers work downtown (20 inner brokers)– Start commute between 4:00 pm and 6:00 pm– 40% live in city center (30 inner brokers)

• Take 15 min to 45 min to commute– 60% live in outskirts (34 outer brokers)

• Take 45 min to 90 min to commute

• Few total disconnections• Long disconnection periods

Downtown

City center OutskirtsOutskirts

1 64

Commute Scenario:Average Overhead

• Standard: almost 100% overhead

• Logging: Slight improvement

• Prefetching: negligible overhead

• Home broker: poor due to sustained unicast

• Peak overhead results

– Up to 3x for Standard

– Up to 27x for Home-broker

Commute Scenario:Total Message Cost

• 800 subscribers

• Same relative performance

• Message cost tracks # of state transfers

• Home-broker overhead even after reconnection

• Max 10% concurrent state transfers

Random Scenario

• More ad-hoc mobility– Subscriber starts at random broker– Disconnects for 10 min to 30 min– Walk (5 km/h), city driving (50 km/h), highway

driving (100 km/h)– Reconnects to a random broker within range– Remains connected for 10 min to 30 min– (repeat)

• More disconnections• Shorter disconnection periods

Random Scenario:Average Overhead

• Standard: 100% overhead

• Prefetching overhead now noticeable

– Due to shorter disconnections

• Logging diverges more from Standard

– Due to hidden increase in locality with population

– Due to more disconnections

Random Scenario:Effect of Locality

• 800 subscribers

• Adjust subscription locality by varying % of subscribers with identical subscriptions

• Logging can approach “ideal” Prefetching

• Others’ increasing overhead is due to decreasing multicast, and constant unicast

Random Scenario:Effect of Log Size

• For Logging approach

• Log size 0, 400, 800, 1200 events

• Diminishing returns of increasing log size

Pervasive Scenario

• Persistent connectivity– Similar mobility patterns as Random scenario– Disconnections are few seconds long

• E.g. Cellular handoffs between cells

• Many disconnections• Short disconnection periods

Pervasive Scenario:Average Overhead

• Event migration is small portion of state transfer overhead

• Similar overhead (except home-broker)

– Due to very short disconnec-tions

Conclusions• The publish/subscribe model lends itself well to mobile

applications

• Mobility can break a distributed pub/sub system– Network overhead can double with only 10% concurrent movein’s– Stored events typically dominate overhead, not subscriptions– With sufficient locality, Logging approaches Prefetching

• Future Work– Other scenarios: realistic traces, mobile publishers– Investigate effects of state transfer traffic on latency– Develop more state transfer optimizations

• Multicast state transfer• Logging at intermediate brokers• Smarter cache replacement policies

Thank You

Pub/Sub Optimizations

Broker

Reduce subscription traffic

Broker

Covering

stocks/ibm/*stocks/*

stocks/*

Broker

Reduce publication traffic to the root

Broker

Advertisements

stocks/*

Publisher

Subscriptions

Advertisements

Publications

Broker Broker

Covering and Advertisements• Our scenarios did not benefit significantly

from covering and ads

• Forwarding of events dominate overhead – 98% of overhead in Commute and Random

scenarios– Covering only helps to quench subscriptions

• There is little subscriber locality (in our scenarios)– Most events must propagate to the root broker– Ads only help to quench upstream publications

Toronto Publish/Subscribe System (ToPSS) Research Areas

S-ToPSS(semantic)

X-ToPSS(semi-structured data; XML)

A-ToPSS(approximate)

M-ToPSS(mobile)

Ad hoc-ToPSS(ad hoc networking)

Federated-ToPSS(federation of ToPSS brokers)

persistent-ToPSS(Subject Spaces)

Rb-ToPSS(rule-based)

ToPSS(matching algorithms)

L-ToPSS(location-based correlation)

p2p-ToPSS(peer-to-peer)ToPSS

Information consumers subscribe to information of interest.Information producers publish information. ToPSS-broker(s) match and route relevant information to interested subscribers.