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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
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Overview
Introduction Background Motivation
Contributions Load Balancing Framework Load Estimation
Experiments Setup
Conclusions Future Work
Goals Related Work
3 Offload Algorithms
Results
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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
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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
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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
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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
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Overview
Introduction
Contributions Load Balancing Framework Load Estimation 3 Offload Algorithms
Experiments
Conclusions
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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
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Sub
Sub Sub Sub
Sub
Load Balancing Framework
Pub
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Sub
Sub Sub Sub
Sub
Load Balancing Framework
Local Load BalancingGlobal Load Balancing
Pub
offloading broker
load-accepting broker
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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’]?
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[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’]
3. [class,’STOCK’], [value,99]
4. [class,’SPORTS’], [year,04]
5. [class,’STOCK’], [value,42]
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
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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
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Offload Algorithms
Match Offload Algorithm
Load balances on the matching delay
Offload subscriptions with the lowest traffic
input queue
Incoming pub/sub messages
Matching Engine & Routing
Tables
…
Load Balancer
output queues
Outgoing pub/sub messages
PADRES broker
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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
Incoming pub/sub messages
Matching Engine & Routing
Tables
…
Load Balancer
output queues
Outgoing pub/sub messages
PADRES broker
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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
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Overview
Introduction
Contributions
Experiments Setup Results
Conclusions
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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']
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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
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Local LB – Input Utilization Ratio
new subscribers join
converged
50% of the publishers increased their publication rate by 100%
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Local LB – Matching Delay
converged
50% of the publishers increased their publication rate by 100%
new subscribers join
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Local LB – Output Utilization Ratio
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Local LB – Subscriber Distribution
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MIDDLEWARE SYSTEMSRESEARCH GROUPLocal LB – Client Perceived Delivery
Delay
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MIDDLEWARE SYSTEMSRESEARCH GROUPLocal LB – Input Utilization Ratio
Estimation Accuracy
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MIDDLEWARE SYSTEMSRESEARCH GROUPLocal LB – Press Samples on Input
Utilization Ratio Estimation Accuracy
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Scalability on Number of Edge Brokers
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Scalability on Number of Clusters
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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
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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
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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
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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
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The End
Thank you
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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’]
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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]
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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
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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
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MIDDLEWARE SYSTEMSRESEARCH GROUPLocal LB – Output Utilization Ratio
Estimation Accuracy
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Local LB – Overhead
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MIDDLEWARE SYSTEMSRESEARCH GROUPLocal LB – Press Samples on Output
Utilization Ratio Estimation Accuracy
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Macro Experiment Setup – Global LB
B12
B11
B10
B22
B21
B20
B32
B31
B30
B42
B41
B40
Pub
Pub
40
Sub Sub2000
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Global LB – Input Utilization Ratio
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Global LB – Matching Delay
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Global LB – Output Utilization Ratio
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Global LB – Subscriber Distribution
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MIDDLEWARE SYSTEMSRESEARCH GROUP
Global LB – Overhead
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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
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Load Balancing Framework
= Local PIE Message
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Load Balancing Framework
= Global PIE Message