efficient event-based resource discovery wei yan, songlin hu, vinod muthusamy +, hans-arno...

Efficient Event-based Resource Discovery

Wei Yan*, Songlin Hu*, Vinod Muthusamy+, Hans-Arno Jacobsen+, Li Zha*

* Chinese Academy of Sciences, Beijing+ University of Toronto

July 9, 2009

3rd Int’l Conference on Distributed Event-

Based Systems(DEBS 2009) MIDDLEWARE SYSTEMS

RESEARCH GROUPhttp://padres.msrg.toronto.edu

MIDDLEWARE SYSTEMSRESEARCH GROUP

Composite applications

MashupsService-oriented

architecturesCloud

computing

A fundamental need is to discover resources and services.

Manyresources

DistributedDynamicattributes

Real-timediscovery


Related work

DEBS ’09 Event-based Resource Discovery 3

Scheme CommentsCentralized index

(Condor’s Matchmaker)

Suffers from large scale and high dynamism.

Hierarchical index (Globus’s MDS)

Root node easily becomes a bottleneck.

Federated UDDI Expensive to replicate frequently updated information among repositories.

Discovery flooding (Gnutella)

Creates a large volume of traffic.

DHT(CAN, Chord, Pastry,

etc.)

Optimized for key-based discoveries.

Range queries over DHTs (P-Ring, Mercury, etc.)

Multi-attribute lookups can be expensive.

Many approaches have limited expressiveness, or support for dynamic attributes or real-time discovery.



Contributions

Event-based resource discovery Distributed architecture Leverage publish/subscribe system Support dynamic resource updates Allow for continuous discovery and real-time results

Discovery similarity optimization Share results among discoveries

Evaluations Tradeoffs of decentralized architecture Benefits of sharing discovery results



Event-basedresource discovery

framework



Supported models

One-timediscovery

Continuousdiscovery

Staticresource

Dynamicresource

Static(e.g., find weather

service)

Dynamic(e.g., find micro-

generation power)

Static continuous(e.g., monitorreal estate)

Dynamic continuous(e.g., monitor grid

resources)Res

ourc

esDiscoveries

Event-based


Architecture


Resource providers act as publishers

Discovery clients act as subscribers

Advertise all attributes:system = linuxmemory <= 2000disk <= 320

Publish updates of dynamic attributes:memory = 1500disk = 80

Subscribe for resources:system = linuxdisk >= 200

Discovery Client

Resource Provider

B1 B4

B5B2

B3

Distributed Content-Based Publish/Subscribe


Static model


Discovery is performed locally by any single broker.

Discovery Client

Advertisement: system = linux, memory = 2, disk = 320

Resource Provider

Subscription: memory > 1 Publication

B1 B4

B5B2

B3


Dynamic model


Resource update publication cached at the resource’s host broker.

Discovery subscription routed to potentially matching resource host brokers.

Discovery Client

Advertisement: system= linux, memory <= 2, disk < 320

Resource Provider

Subscription: memory > 1 Publication: memory = 1, disk = 200

B1

B3

B4

B5B2


Static continuous model


Discovery is performed locally by any single broker (like static model).

Discovery subscription stored at discovery host broker.

Discovery Client

Advertisement: system = linux, memory = 2, disk = 320

Resource Provider

Subscription: memory > 1 Publication

B1 B4

B5B2

B3


Dynamic continuous model


Traditional pub/sub routing of messages.

Discovery subscription is routed to and stored at matching resource host brokers.

Discovery Client

Resource Provider

B5

B4

B3

B2

B1

Advertisement: system= linux, memory <= 2, disk < 320

Subscription: memory > 1 Publication: memory = 1, disk = 200


Summary of models

One-timediscovery

Continuousdiscovery

Staticresource

Dynamicresource

Static

Dynamic

Staticcontinuous

Dynamiccontinuous

Discovery handled locally at discovery host broker

Updates delivered only to interested clients

No persistent subscription state

Subscription state used to route back

updates



Discovery similarity


Reuse results of similar discoveries


Find machines withat least 1GB memory

S1

Subscription:memory >= 1000

R1

R2R3

More general

Find machines withat least 2GB memory

S1

Subscription:memory >= 2000

R1

R2

Covers Superset


Similarity forwarding


To retrieve old results: Send covered sub to the covering sub’s discovery host broker.

To intercept new results: Store covered sub at the first broker with a covering sub.

Discovery Client

Resource Provider

Adv: system= linux, memory <= 2, disk < 320

Sub2: memory > 2Pub: memory = 1, disk = 200

Discovery Client

Sub1: memory > 1

B1

B5B2

B3

B4



Evaluations


Experimental setup Algorithms implemented in Java

Based on PADRES content-based pub/sub system Run on a cluster of nodes with 1.86 GHz CPU and 4 GB memory

Default workload Topology

Decentralized: 24 brokers Centralized: 1 broker

1000 resources Balanced and unbalanced spatial distributions

1000 discoveries Balanced and unbalanced spatial distributions Various degrees of similarity

Metrics Discovery time Message overhead



Discovery time Similarity forwarding optimization is

faster

Increased discovery similarity Normal algorithm suffers

More matching resources are found Optimized algorithm benefits

Reuse results

Spatial clustering of resources Normal algorithm benefits

Smaller subscription propagation tree (more “multicast”)

Optimized algorithm benefits slightly Results are often retrieved from discovery

host broker

Spatial clustering of discoveries Normal algorithm suffers

Congestion of messages near discovery host brokers

Optimized algorithm suffers slightly Matching of cached results is relatively

cheap

DEBS ’09

Clustered spatial distribution of discoveries

Balanced spatial distribution of discoveries

0.0

0.5

1.0

1.5

2.0

2.5

3.0

10 20 30 40 50 60 70 80 90 100

Discovery similarity (%)

Avg

dis

cove

ry t

ime

(s)

Normal(B)Similarity(B)Normal(U)Similarity(U)

0.0

2.0

2.5

3.0

0.5

1.0

1.5

10 20 30 40 50 60 70 80 90 100


Avg

dis

cove

ry t

ime

(s)



0

5

10

15

20

25

30

10 20 30 40 50 60 70 80 90 100


Su

bsc

rip

tio

n m

sgs

(x10

00) Normal(B)

Similarity(B)Normal(U)Similarity(U)

Similarity forwarding optimization propagates fewer subscriptions

Increased discovery similarity Normal algorithm suffers slightly

More matching resources are found Optimized algorithm benefits

Covered subs only propagate to a single discovery host broker

Spatial clustering of resources Normal algorithm benefits

Smaller subscription propagation tree (more “multicast”)

Optimized algorithm benefits (but less than normal algorithm) Covered subs are not affected

Spatial clustering of discoveries Normal algorithm has little effect

Subscriptions still propagate to resource host brokers

Optimized algorithm has little effect Cost is dominated by the covering subs, which still

need to propagate to resource host brokers

DEBS ’09

0

5

10

15

20

25

30

10 20 30 40 50 60 70 80 90 100


Su

bsc

rip

tio

n m

sgs

(x10

00)


Clustered spatial distribution of discoveries

Balanced spatial distribution of discoveries

Subscription messages


Decentralized architecture(one-time requests)


Successive discovery groups match increasing number of resources Measure time to find (updated) resources

Decentralized architecture distributes the load Discovery handled locally by discovery host broker Updates are propagated only to interested discovery host brokers


Decentralized architecture(continuous requests)


Decentralized architecture better distributes the load Results from similar discoveries are reused Updates are propagated only to interested brokers


Conclusions Discovering resources and services is increasingly important in

composite distributed applications

A distributed event-based resource discovery framework was designed Parallel discovery of static resources Efficient dissemination of dynamic resource attributes Real-time discovery of new resources

Optimizations to exploit similarity among discoveries were developed Find similar discoveries Reuse results Exploit publish/subscribe covering techniques

Evaluations show that the distributed architecture achieves faster discovery at the expense of increased network traffic

The similarity optimization benefits from more skewed spatial and interest distributions




Efficient Event-based Resource Discovery

http://padres.msrg.toronto.eduOpen source soon!

Q&A

efficient event-based resource discovery wei yan, songlin hu, vinod muthusamy +, hans-arno...

Documents

discovery subscription

discovery host broker

publishers discovery

discovery flooding gnutella

resource update publication

resource providers act

keybased discoveries

matching resource host

efficient event-based resource discovery wei yan*, songlin hu*, vinod muthusamy +, hans-arno...

efficient event-based resource discovery wei yan, songlin hu, vinod muthusamy +, hans-arno...