dynamic load balancing in distributed content-based publish/subscribe alex k. y. cheung &...

Dynamic Load Balancing in Distributed Content-based

Publish/Subscribe

Alex K. Y. Cheung & Hans-Arno JacobsenUniversity of Toronto

November 30th, 2006Middleware 2006

MIDDLEWARE SYSTEMSRESEARCH GROUP

Copyright © 2006 by Alex K. Y. Cheung Middleware 2006

2


Overview

Introduction Background Motivation

Contributions Load Balancing Framework Load Estimation

Experiments Setup

Conclusions Future Work

Goals Related Work

3 Offload Algorithms

Results


3


Background on Distributed Pub/Sub

subscriber

stock = ‘YHOO’ price > $35.00

subscriber

stock = ‘YHOO’ price > $30.00

publisher

stock = ‘YHOO’ price = $40.00

publisher

stock = ‘GM’ price = $20.00

broker

multicast


4


Motivation

A distributed content-based publish/subscribe system offers:

1. Loosely coupled data sources and sinks2. Flexible routing based on message content3. Comprised of a distributed broker overlay network

Main focus: online games, decentralized workflow execution, and real-time monitoring systems

Real-world usage patterns are never evenly distributed:

Performance degradation Instability and unavailability Under-utilized resources:

Reduced scalability Poor investment in resources


5


Research Goal

Develop a load balancing algorithm for distributed content-based pub/sub having these strong properties:

1. Dynamic – load balancing can be invoked anytime when overload or uneven load distribution is detected

2. Adaptive – a distinct offload algorithm designed to load balance on each type of resource

3. Heterogeneous – account for brokers with different resource capacities and subscriptions subscribing to different publication sets

4. Distributed – scalability of load balancing algorithm and the pub/sub infrastructure

5. Transparent – publishers and subscribers have no idea about load balancing


6


Related Work

Compared to Tersptra et al. [5], Meghdoot [2], and Opportunistic Overlays [1]: Applied to dedicated broker pub/sub architecture Accounts for heterogeneity Unlike load sharing, the proposed solution is a dynamic

load balancing algorithm that can distribute load in the absence of new node joins

Load estimation prevents overloading load-accepting brokers in load balancing

Balances 3 performance metrics simultaneously on a best-effort approach using adaptive offload algorithms

input utilization ratio, matching delay and output utilization ratio

Load balancing is done by migrating subscribers


7


Overview

Introduction

Contributions Load Balancing Framework Load Estimation 3 Offload Algorithms

Experiments

Conclusions


8


Load Balancing Framework

Pub

Pub

Sub

Sub

Sub

Sub

Sub

Sub

Sub

SubPEER

(Padres Efficient Event Routing)

clusters

Promotes 2 levels of loadbalancing:1. Local-level2. Global-level

cluster-head brokers

edge-brokers


9


Sub

Sub Sub Sub

Sub


Pub


10


Sub

Sub Sub Sub

Sub


Local Load BalancingGlobal Load Balancing

Pub

offloading broker

load-accepting broker


11


Load Estimation

Padres Real-time Event-to-Subscription Spectrum (PRESS) Bit vector approach to record, compare, and

estimate subscription load reduced/gained at each broker

How much additional traffic does the subscription [class,=,*] introduce onto a broker already subscribed to [class,=,’SPORTS’]?


12


[class,=,’STOCK’], [value,>,40]

[class,=,’STOCK’], [value,>,45]

[class,=,’SPORTS’]

[class,=,*]

Load Estimation with PRESS

Subscriptions PRESS bit vector

1. [class,’STOCK’], [value,60]

2. [class,’SPORTS’]


4. [class,’SPORTS’], [year,04]


Publication arrival order:

1

1

0

1

1

0

0

1

0

0

1

1

1

1

0

1

0

0

1

1

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

To answer the question: “How much additional traffic does the subscription [class,=,*] introduce onto another broker already subscribing to [class,=,’SPORTS’]?

Answer: 3/5 of the measured total incoming traffic

Assuming an incoming publication rate of 5 msg/s, then the additional publication rate is:

(5 msg/s) x (3/5) = 3 msg/s


13


Offload Algorithms

Input Offload Algorithm

Load balances on the input utilization ratio:input utilization ratio =

incoming pub rate / max match rate Offload subscriptions that

reduce the incoming publication rate of the broker

Increase matching rate by reducing the matching delay through the match offload algorithm only if the broker is overloaded

input queue

Incoming pub/sub messages

Matching Engine & Routing

Tables

…

Load Balancer

output queues

Outgoing pub/sub messages

PADRES broker


14


Offload Algorithms

Match Offload Algorithm

Load balances on the matching delay

Offload subscriptions with the lowest traffic

input queue



Tables

…

Load Balancer

output queues


PADRES broker


15


Offload Algorithms

Output Offload Algorithm

Load balances on the output utilization ratiooutput utilization ratio =

output bandwidth used /

total output bandwidth

Offload subscriptions with the highest traffic possible. Favor subscriptions that do not increase the incoming publication rate onto the load-accepting broker

input queue



Tables

…

Load Balancer

output queues


PADRES broker


16


Load Balancing Components

DETECTOR

PIEMEDIATOR

PIE messages

OFFLOAD ALGORITHMS

Input Match Output

LOAD ESTIMATION

PRESS

Mediation and migration protocols

Subscribers to offload

Target broker

Load Balancer

•Establish and teardown load balancing sessions between two brokers/clusters•Coordinate transparent subscriber migrations

Calculates the set of subscribers to offload for balancing the performance metric in question based on load information about the subscriptions and load-accepting broker

Padres Real-time Event-to-Subscription Spectrum (PRESS) uses a bit vector approach to estimate load of subscriptions

•Triggers load balancing if overload or uneven load distribution is detected by examining the 3 performance metrics•Padres Information Exchange (PIE) for exchanging load information with other brokers


17


Overview

Introduction

Contributions

Experiments Setup Results

Conclusions


18


Experiments

Integrated the load balancing algorithm into PADRES [3] with 20,000 LOC

Results obtained through emulation Bandwidth, queuing, and matching delays

Dataset consists of real stock quote publications obtained from Yahoo! Finance [6]

[class,'STOCK'],[symbol,'YHOO'],[open,30.25],[high,31.75],[low,30.00],[close,31.62],[volume,203400],[date,'1-May-96']

Subscription distribution:0.20 = [class,eq,'STOCK'],[symbol,eq,'REPLACE_SYMBOL'],[high,>,REPLACE_HIGH]0.20 = [class,eq,'STOCK'],[symbol,eq,'REPLACE_SYMBOL'],[low,<,REPLACE_LOW]0.20 = [class,eq,'STOCK'],[symbol,eq,'REPLACE_SYMBOL'],[volume,>,REPLACE_VOLUME]0.34 = [class,eq,'STOCK'],[symbol,eq,'REPLACE_SYMBOL']0.05 = [class,eq,'STOCK'],[volume,>,REPLACE_VOLUME]0.01 = [class,eq,'STOCK']


19


Macro Experiment Setup – Local LB

B3 B2

B1B4

B0

Pub

Pub

40 unique publishers

Sub

Sub

2000 subscribers, 20% sink zero traffic

Broker ID

CPU (MHz)

Memory (MB)

Bandwidth (Mbps)

B0 2000 32 10

B1 100 16 0.5B2 200 16 1

B3 400 32 2B4 1000 32 5


20


Local LB – Input Utilization Ratio

new subscribers join

converged

50% of the publishers increased their publication rate by 100%


21


Local LB – Matching Delay

converged

50% of the publishers increased their publication rate by 100%

new subscribers join


22


Local LB – Output Utilization Ratio


23


Local LB – Subscriber Distribution


24

MIDDLEWARE SYSTEMSRESEARCH GROUPLocal LB – Client Perceived Delivery

Delay


25

MIDDLEWARE SYSTEMSRESEARCH GROUPLocal LB – Input Utilization Ratio

Estimation Accuracy


26

MIDDLEWARE SYSTEMSRESEARCH GROUPLocal LB – Press Samples on Input

Utilization Ratio Estimation Accuracy


27


Scalability on Number of Edge Brokers


28


Scalability on Number of Clusters


29


Summary of Other Experimental Results

Load balancing effectiveness is independent of: Number of subscribers Distribution of zero-traffic subscribers Number of brokers/clusters

Load balancing on the input and output utilization ratios also balances their queuing delay counterparts

Decreasing the detection threshold can reduce load difference between brokers at the expense of higher overhead

Increasing the PIE publication frequency can decrease the convergence time in local load balancing at the expense of higher overhead


30


Conclusions

Load balancing solution for distributed content-based pub/sub that is dynamic, adaptive, distributed, transparent, and accommodates for heterogeneity

Consisting of 3 key contributions: Load balancing framework Load estimation methodologies 3 offload algorithms

Promote scalability by evenly distributing load onto the available resources


31


Future Work

Evaluation on a real-world test bed, such as PlanetLab [4]

Support load balancing for cluster-head brokers Integrate load balancing with other on-going

research projects in PADRES, such as composite subscriptions, historical data access, and failure recovery

Self-tuning capability


32


References

[1] Y. Chen and K. Schwan, “Opportunistic overlays: Efficient content delivery in mobile ad hoc networks,” in ACM Middleware ’05 Grenoble, 2005.

[2] A. Gupta, O. D. Sahin, D. Agrawal, and A. E. Abbadi, “Meghdoot: content-based publish/subscribe over p2p networks,” in Proceedings of the 5th ACM/IFIP/USENIX international conference on Middleware. New York, NY, USA: Springer-Verlag New York, Inc., 2004, pp. 254-273.

[3] G. Li and H.-A. Jacobsen, “Composite subscriptions in content-based publish/subscribe systems,” in ACM Middleware ’05 Grenoble, 2005.

[4] PlanetLab. [Online]. Available: http://www.planet-lab.org/[5] W. W. Terpstra, S. Behnel, L. Fiege, A. Zeidler, and A. P. Buchmann, “A

peer- to-peer approach to content-based publish/subscribe,” in DEBS ’03: Proceedings of the 2nd international workshop on distributed event-based systems. New York, NY, USA: ACM Press, 2003, pp. 1-8.

[6] Yahoo! Finance. [Online]. Available: http://finance.yahoo.com


33


The End

Thank you


34


Offload Strategies

Poset [Carzaniga et al.01]

[class,eq,*]

[class,eq,’stock’] [class,eq,’jobs’]

[class,eq,’stock’], [name,eq,’cyber’]

[class,eq,’stock’], [value,>,30]

[class,eq,’jobs’], [type,eq,’engineering’]


35


Handoff

1. Tell selected subscribers to connect to new broker

2. Subscribers issue subscription

3. Broker 2 counts down from 10 x (n-1) seconds after receiving first subscription before assuming migration is complete

2

1

3

= subscriber

[class,’SUB_CONTROL’], [command,’MIGRATE’], [IP,123.45.67.89], [port,1234] payload[offloadID,’e8sU27’], [population,2]


36


Handoff

2

1

37. Broker 2 updates its timer.

8. Broker 2 sends OK message to broker 1 when t = 0 or all subscribers are connected

9. Broker 1 tells all remaining migrating subscribers to unsubscribe from broker 1 after its processing delay

10. Subscribers unsubscribe from broker 1

Done! = subscriber

OK

OKunsubscribe


37


Hand-off

Goal: Achieve 100% delivery ratio in migration process

Solution: Subscribers issue subscription to remote broker Remote broker sends to original broker that it received all

subscribers (guarantees subscriptions are processed by the cluster-head already)

Original broker tells the subscribers to disconnect from it after a delay:

delay = (input queuing delay)+ (output queuing delay)+ (matching delay)

To ensure publications still in waiting in the queues get delivered to the migrated subscriber


38

MIDDLEWARE SYSTEMSRESEARCH GROUPLocal LB – Output Utilization Ratio

Estimation Accuracy


39


Local LB – Overhead


40

MIDDLEWARE SYSTEMSRESEARCH GROUPLocal LB – Press Samples on Output

Utilization Ratio Estimation Accuracy


41


Macro Experiment Setup – Global LB

B12

B11

B10

B22

B21

B20

B32

B31

B30

B42

B41

B40

Pub

Pub

40

Sub Sub2000


42


Global LB – Input Utilization Ratio


43


Global LB – Matching Delay


44


Global LB – Output Utilization Ratio


45


Global LB – Subscriber Distribution


46


Global LB – Overhead


47


Related Work

Tersptra et al. [1] Load sharing scheme for distributed hash table (DHT)

based peer-to-peer (P2P) pub/sub involving a unique dissemination tree from each node

Meghdoot [2] Load sharing technique for DHT based P2P pub/sub Strategically have new incoming nodes to share the

matching or bandwidth load of existing node Opportunistic Overlays [3]

Load sharing as a secondary requirement to optimizing delivery path in a mobile P2P publish/subscribe environment


48



= Local PIE Message


49



= Global PIE Message

dynamic load balancing in distributed content-based publish/subscribe alex k. y. cheung &...

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